ICLR 2025 Papers

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Watermark Anything With Localized Messages

SePer: Measure Retrieval Utility Through The Lens Of Semantic Perplexity Reduction

Searching for Optimal Solutions with LLMs via Bayesian Optimization

Do Stochastic, Feel Noiseless: Stable Stochastic Optimization via a Double Momentum Mechanism

Learning to Plan Before Answering: Self-Teaching LLMs to Learn Abstract Plans for Problem Solving

HyperPLR: Hypergraph Generation through Projection, Learning, and Reconstruction

PN-GAIL: Leveraging Non-optimal Information from Imperfect Demonstrations

DRoC: Elevating Large Language Models for Complex Vehicle Routing via Decomposed Retrieval of Constraints

Detecting Backdoor Samples in Contrastive Language Image Pretraining

Hierarchical Autoregressive Transformers: Combining Byte- and Word-Level Processing for Robust, Adaptable Language Models

Ensembling Diffusion Models via Adaptive Feature Aggregation

Diffusion Transformers for Tabular Data Time Series Generation

Solving Token Gradient Conflict in Mixture-of-Experts for Large Vision-Language Model

SVBench: A Benchmark with Temporal Multi-Turn Dialogues for Streaming Video Understanding

Efficient Multi-agent Offline Coordination via Diffusion-based Trajectory Stitching

Bayesian Image Regression with Soft-thresholded Conditional Autoregressive Prior

VVC-Gym: A Fixed-Wing UAV Reinforcement Learning Environment for Multi-Goal Long-Horizon Problems

RecDreamer: Consistent Text-to-3D Generation via Uniform Score Distillation

Towards Continuous Reuse of Graph Models via Holistic Memory Diversification

Artificial Kuramoto Oscillatory Neurons

Uncertainty and Influence aware Reward Model Refinement for Reinforcement Learning from Human Feedback

ComLoRA: A Competitive Learning Approach for Enhancing LoRA

Token-Supervised Value Models for Enhancing Mathematical Problem-Solving Capabilities of Large Language Models

AdvPaint: Protecting Images from Inpainting Manipulation via Adversarial Attention Disruption

Dynamical Diffusion: Learning Temporal Dynamics with Diffusion Models

Hallo2: Long-Duration and High-Resolution Audio-Driven Portrait Image Animation

Don't Take Things Out of Context: Attention Intervention for Enhancing Chain-of-Thought Reasoning in Large Language Models

Unsupervised Multiple Kernel Learning for Graphs via Ordinality Preservation

TaskGalaxy: Scaling Multi-modal Instruction Fine-tuning with Tens of Thousands Vision Task Types

Gramian Multimodal Representation Learning and Alignment

RouteLLM: Learning to Route LLMs from Preference Data

Beware of Calibration Data for Pruning Large Language Models

Skill Expansion and Composition in Parameter Space

DiscoveryBench: Towards Data-Driven Discovery with Large Language Models

HiRA: Parameter-Efficient Hadamard High-Rank Adaptation for Large Language Models

PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding

Advancing Mathematical Reasoning in Language Models: The Impact of Problem-Solving Data, Data Synthesis Methods, and Training Stages

GeoILP: A Synthetic Dataset to Guide Large-Scale Rule Induction

On Speeding Up Language Model Evaluation

Learning to Contextualize Web Pages for Enhanced Decision Making by LLM Agents

Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis

Enhancing Cognition and Explainability of Multimodal Foundation Models with Self-Synthesized Data

Teaching LLMs How to Learn with Contextual Fine-Tuning

KAA: Kolmogorov-Arnold Attention for Enhancing Attentive Graph Neural Networks

ECHOPulse: ECG Controlled Echocardio-gram Video Generation

Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want

TAU-106K: A New Dataset for Comprehensive Understanding of Traffic Accident

Revisiting Random Walks for Learning on Graphs

ZAPBench: A Benchmark for Whole-Brain Activity Prediction in Zebrafish

Safety Representations for Safer Policy Learning

HiLo: A Learning Framework for Generalized Category Discovery Robust to Domain Shifts

Provable unlearning in topic modeling and downstream tasks

PaLD: Detection of Text Partially Written by Large Language Models

A Watermark for Order-Agnostic Language Models

Gaussian Mixture Counterfactual Generator

Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

Large Language Models Meet Symbolic Provers for Logical Reasoning Evaluation

STAFF: Speculative Coreset Selection for Task-Specific Fine-tuning

Approaching Rate-Distortion Limits in Neural Compression with Lattice Transform Coding

LeFusion: Controllable Pathology Synthesis via Lesion-Focused Diffusion Models

Manifolds, Random Matrices and Spectral Gaps: The geometric phases of generative diffusion

Pursuing Feature Separation based on Neural Collapse for Out-of-Distribution Detection

Doubly robust identification of treatment effects from multiple environments

Fat-to-Thin Policy Optimization: Offline Reinforcement Learning with Sparse Policies

Model-based Offline Reinforcement Learning with Lower Expectile Q-Learning

MDSGen: Fast and Efficient Masked Diffusion Temporal-Aware Transformers for Open-Domain Sound Generation

LongPO: Long Context Self-Evolution of Large Language Models through Short-to-Long Preference Optimization

Understanding and Enhancing Safety Mechanisms of LLMs via Safety-Specific Neuron

CofCA: A STEP-WISE Counterfactual Multi-hop QA benchmark

Non-Adversarial Inverse Reinforcement Learning via Successor Feature Matching

Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models

DiffPC: Diffusion-based High Perceptual Fidelity Image Compression with Semantic Refinement

MMQA: Evaluating LLMs with Multi-Table Multi-Hop Complex Questions

Predicate Hierarchies Improve Few-Shot State Classification

Rethinking Neural Multi-Objective Combinatorial Optimization via Neat Weight Embedding

Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm

Methods for Convex $(L_0,L_1)$-Smooth Optimization: Clipping, Acceleration, and Adaptivity

Reducing Hallucinations in Large Vision-Language Models via Latent Space Steering

BirdSet: A Large-Scale Dataset for Audio Classification in Avian Bioacoustics

DeepLTL: Learning to Efficiently Satisfy Complex LTL Specifications for Multi-Task RL

Revealing and Mitigating Over-Attention in Knowledge Editing

TFG-Flow: Training-free Guidance in Multimodal Generative Flow

GlycanML: A Multi-Task and Multi-Structure Benchmark for Glycan Machine Learning

Adversarial Generative Flow Network for Solving Vehicle Routing Problems

Ctrl-U: Robust Conditional Image Generation via Uncertainty-aware Reward Modeling

MAGE: Model-Level Graph Neural Networks Explanations via Motif-based Graph Generation

No Preference Left Behind: Group Distributional Preference Optimization

DataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback

An Intelligent Agentic System for Complex Image Restoration Problems

DartControl: A Diffusion-Based Autoregressive Motion Model for Real-Time Text-Driven Motion Control

Neural Multi-Objective Combinatorial Optimization via Graph-Image Multimodal Fusion

Homomorphism Counts as Structural Encodings for Graph Learning

GridMix: Exploring Spatial Modulation for Neural Fields in PDE Modeling

Lightweight Neural App Control

Discrete Codebook World Models for Continuous Control

GraphBridge: Towards Arbitrary Transfer Learning in GNNs

ReGen: Generative Robot Simulation via Inverse Design

Progressive Mixed-Precision Decoding for Efficient LLM Inference

Do Large Language Models Truly Understand Geometric Structures?

Training-Free Diffusion Model Alignment with Sampling Demons

ROUTE: Robust Multitask Tuning and Collaboration for Text-to-SQL

Going Beyond Static: Understanding Shifts with Time-Series Attribution

Quest: Query-centric Data Synthesis Approach for Long-context Scaling of Large Language Model

Rethinking Light Decoder-based Solvers for Vehicle Routing Problems

Are Transformers Able to Reason by Connecting Separated Knowledge in Training Data?

Distilling Structural Representations into Protein Sequence Models

Diverse Preference Learning for Capabilities and Alignment

Probabilistic Geometric Principal Component Analysis with application to neural data

Weak-to-Strong Preference Optimization: Stealing Reward from Weak Aligned Model

Diverse Policies Recovering via Pointwise Mutual Information Weighted Imitation Learning

Extending Mercer's expansion to indefinite and asymmetric kernels

CLoSD: Closing the Loop between Simulation and Diffusion for multi-task character control

Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning

L3Ms — Lagrange Large Language Models

Can Watermarks be Used to Detect LLM IP Infringement For Free?

Taming Transformer Without Using Learning Rate Warmup

Have the VLMs Lost Confidence? A Study of Sycophancy in VLMs

Aligning Visual Contrastive learning models via Preference Optimization

DuoAttention: Efficient Long-Context LLM Inference with Retrieval and Streaming Heads

MoDGS: Dynamic Gaussian Splatting from Casually-captured Monocular Videos with Depth Priors

Exploring a Principled Framework for Deep Subspace Clustering

Noise Separation guided Candidate Label Reconstruction for Noisy Partial Label Learning

Human Simulacra: Benchmarking the Personification of Large Language Models

OccProphet: Pushing the Efficiency Frontier of Camera-Only 4D Occupancy Forecasting with an Observer-Forecaster-Refiner Framework

AvatarGO: Zero-shot 4D Human-Object Interaction Generation and Animation

Selective Label Enhancement Learning for Test-Time Adaptation

Tight Lower Bounds under Asymmetric High-Order Hölder Smoothness and Uniform Convexity

Learning Gain Map for Inverse Tone Mapping

Accelerating Training with Neuron Interaction and Nowcasting Networks

Unleashing the Power of Task-Specific Directions in Parameter Efficient Fine-tuning

Elliptic Loss Regularization

Multi-agent cooperation through learning-aware policy gradients

A Multi-Power Law for Loss Curve Prediction Across Learning Rate Schedules

Calibrating Expressions of Certainty

Random-Set Neural Networks

CBQ: Cross-Block Quantization for Large Language Models

PaCA: Partial Connection Adaptation for Efficient Fine-Tuning

CraftRTL: High-quality Synthetic Data Generation for Verilog Code Models with Correct-by-Construction Non-Textual Representations and Targeted Code Repair

Expressivity of Neural Networks with Random Weights and Learned Biases

Learning vector fields of differential equations on manifolds with geometrically constrained operator-valued kernels

Ctrl-Adapter: An Efficient and Versatile Framework for Adapting Diverse Controls to Any Diffusion Model

eQMARL: Entangled Quantum Multi-Agent Reinforcement Learning for Distributed Cooperation over Quantum Channels

VEDIT: Latent Prediction Architecture For Procedural Video Representation Learning

Sensitivity Verification for Additive Decision Tree Ensembles

CycleResearcher: Improving Automated Research via Automated Review

Personality Alignment of Large Language Models

Oscillatory State-Space Models

Self-play with Execution Feedback: Improving Instruction-following Capabilities of Large Language Models

State Space Model Meets Transformer: A New Paradigm for 3D Object Detection

Towards Unbiased Learning in Semi-Supervised Semantic Segmentation

Disentangling Representations through Multi-task Learning

Is Your Video Language Model a Reliable Judge?

LaMPlace: Learning to Optimize Cross-Stage Metrics in Macro Placement

Differentiable Integer Linear Programming

Polynomial Composition Activations: Unleashing the Dynamics of Large Language Models

Hyper-Connections

Ultra-Sparse Memory Network

TabWak: A Watermark for Tabular Diffusion Models

LongVILA: Scaling Long-Context Visual Language Models for Long Videos

MRAG-Bench: Vision-Centric Evaluation for Retrieval-Augmented Multimodal Models

Deep Distributed Optimization for Large-Scale Quadratic Programming

DGQ: Distribution-Aware Group Quantization for Text-to-Image Diffusion Models

AdaManip: Adaptive Articulated Object Manipulation Environments and Policy Learning

TC-MoE: Augmenting Mixture of Experts with Ternary Expert Choice

Mitigating Spurious Correlations in Zero-Shot Multimodal Models

Learning Shape-Independent Transformation via Spherical Representations for Category-Level Object Pose Estimation

DeLLMa: Decision Making Under Uncertainty with Large Language Models

Making Text Embedders Few-Shot Learners

Beyond Random Augmentations: Pretraining with Hard Views

Differential Transformer

Enhance Multi-View Classification Through Multi-Scale Alignment and Expanded Boundary

Learning Distributions of Complex Fluid Simulations with Diffusion Graph Networks

Brain Mapping with Dense Features: Grounding Cortical Semantic Selectivity in Natural Images With Vision Transformers

LoRA-X: Bridging Foundation Models with Training-Free Cross-Model Adaptation

Rethinking Classifier Re-Training in Long-Tailed Recognition: Label Over-Smooth Can Balance

Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model

Gnothi Seauton: Empowering Faithful Self-Interpretability in Black-Box Transformers

Improving the Sparse Structure Learning of Spiking Neural Networks from the View of Compression Efficiency

Multi-level Certified Defense Against Poisoning Attacks in Offline Reinforcement Learning

Efficient Action-Constrained Reinforcement Learning via Acceptance-Rejection Method and Augmented MDPs

Pangea: A Fully Open Multilingual Multimodal LLM for 39 Languages

Linear Recursions for Everyone

DataGen: Unified Synthetic Dataset Generation via Large Language Models

Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge

Attribute-based Visual Reprogramming for Vision-Language Models

Generalization and Distributed Learning of GFlowNets

Halton Scheduler for Masked Generative Image Transformer

MixEval-X: Any-to-any Evaluations from Real-world Data Mixture

When do GFlowNets learn the right distribution?

In Search of the Engram in LLMs: A Neuroscience Perspective on the Memory Functions in AI Models

Manifold Learning by Mixture Models of VAEs for Inverse Problems

Harnessing Webpage UIs for Text-Rich Visual Understanding

Towards Neural Scaling Laws for Time Series Foundation Models

SparsyFed: Sparse Adaptive Federated Learning

ACES: Automatic Cohort Extraction System for Event-Stream Datasets

Generating Less Certain Adversarial Examples Improves Robust Generalization

PivotMesh: Generic 3D Mesh Generation via Pivot Vertices Guidance

GUI-World: A Video Benchmark and Dataset for Multimodal GUI-oriented Understanding

Do Egocentric Video-Language Models Truly Understand Hand-Object Interactions?

Quantifying Generalization Complexity for Large Language Models

MatExpert: Decomposing Materials Discovery By Mimicking Human Experts

STORM: Spatio-TempOral Reconstruction Model For Large-Scale Outdoor Scenes

OmniRe: Omni Urban Scene Reconstruction

PseDet: Revisiting the Power of Pseudo Label in Incremental Object Detection

Automated Proof Generation for Rust Code via Self-Evolution

MindSearch: Mimicking Human Minds Elicits Deep AI Searcher

Language Models Need Inductive Biases to Count Inductively

Neural Fluid Simulation on Geometric Surfaces

Explore Theory of Mind: program-guided adversarial data generation for theory of mind reasoning

Transformer Block Coupling and its Correlation with Generalization in LLMs

MGCFNN: A Neural MultiGrid Solver with Novel Fourier Neural Network for High Wave Number Helmholtz Equations

Language-Assisted Feature Transformation for Anomaly Detection

Adversarial Policy Optimization for Offline Preference-based Reinforcement Learning

Causally Motivated Sycophancy Mitigation for Large Language Models

Spectral-Refiner: Accurate Fine-Tuning of Spatiotemporal Fourier Neural Operator for Turbulent Flows

Differentiable Causal Discovery for Latent Hierarchical Causal Models

Robust Watermarking Using Generative Priors Against Image Editing: From Benchmarking to Advances

Chain-of-Thought Provably Enables Learning the (Otherwise) Unlearnable

Dysca: A Dynamic and Scalable Benchmark for Evaluating Perception Ability of LVLMs

Subgraph Federated Learning for Local Generalization

$q$-exponential family for policy optimization

Generalizable Human Gaussians from Single-View Image

Towards Out-of-Modal Generalization without Instance-level Modal Correspondence

Port-Hamiltonian Architectural Bias for Long-Range Propagation in Deep Graph Networks

Probe Pruning: Accelerating LLMs through Dynamic Pruning via Model-Probing

LevAttention: Time, Space and Streaming Efficient Algorithm for Heavy Attentions

Extreme Risk Mitigation in Reinforcement Learning using Extreme Value Theory

Perplexity Trap: PLM-Based Retrievers Overrate Low Perplexity Documents

On the Relation between Trainability and Dequantization of Variational Quantum Learning Models

Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling

Emergence of a High-Dimensional Abstraction Phase in Language Transformers

Graph-Guided Scene Reconstruction from Images with 3D Gaussian Splatting

Self-Supervised Diffusion MRI Denoising via Iterative and Stable Refinement

A Generalist Hanabi Agent

Enhancing Zeroth-order Fine-tuning for Language Models with Low-rank Structures

Interleaved Scene Graphs for Interleaved Text-and-Image Generation Assessment

Boost Self-Supervised Dataset Distillation via Parameterization, Predefined Augmentation, and Approximation

Benchmarking LLMs' Judgments with No Gold Standard

Sort-free Gaussian Splatting via Weighted Sum Rendering

cryoSPHERE: Single-Particle HEterogeneous REconstruction from cryo EM

Actions Speak Louder Than Words: Rate-Reward Trade-off in Markov Decision Processes

Presto! Distilling Steps and Layers for Accelerating Music Generation

Surgical, Cheap, and Flexible: Mitigating False Refusal in Language Models via Single Vector Ablation

MMR: A Large-scale Benchmark Dataset for Multi-target and Multi-granularity Reasoning Segmentation

Breach By A Thousand Leaks: Unsafe Information Leakage in 'Safe' AI Responses

QPM: Discrete Optimization for Globally Interpretable Image Classification

Navigating the Digital World as Humans Do: Universal Visual Grounding for GUI Agents

How much of my dataset did you use? Quantitative Data Usage Inference in Machine Learning

Phidias: A Generative Model for Creating 3D Content from Text, Image, and 3D Conditions with Reference-Augmented Diffusion

A Simple Approach to Unifying Diffusion-based Conditional Generation

Training Neural Networks as Recognizers of Formal Languages

Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning

Zero-shot Model-based Reinforcement Learning using Large Language Models

Fast Summation of Radial Kernels via QMC Slicing

Grounding Continuous Representations in Geometry: Equivariant Neural Fields

SAVA: Scalable Learning-Agnostic Data Valuation

EMOS: Embodiment-aware Heterogeneous Multi-robot Operating System with LLM Agents

miniCTX: Neural Theorem Proving with (Long-)Contexts

Self-supervised contrastive learning performs non-linear system identification

Limits to scalable evaluation at the frontier: LLM as judge won’t beat twice the data

Model Editing as a Robust and Denoised variant of DPO: A Case Study on Toxicity

Bootstrapping Language Models with DPO Implicit Rewards

Training on the Test Task Confounds Evaluation and Emergence

Causal Graphical Models for Vision-Language Compositional Understanding

Efficient Learning with Sine-Activated Low-Rank Matrices

AFlow: Automating Agentic Workflow Generation

RandLoRA: Full rank parameter-efficient fine-tuning of large models

Swiss Army Knife: Synergizing Biases in Knowledge from Vision Foundation Models for Multi-Task Learning

AgentStudio: A Toolkit for Building General Virtual Agents

FreDF: Learning to Forecast in the Frequency Domain

Geometry of Lightning Self-Attention: Identifiability and Dimension

Optimal Transport for Time Series Imputation

No Pose, No Problem: Surprisingly Simple 3D Gaussian Splats from Sparse Unposed Images

Programming Refusal with Conditional Activation Steering

Robotouille: An Asynchronous Planning Benchmark for LLM Agents

Limits of Deep Learning: Sequence Modeling through the Lens of Complexity Theory

Exact Community Recovery under Side Information: Optimality of Spectral Algorithms

Implicit Bias of Mirror Flow for Shallow Neural Networks in Univariate Regression

PolyPythias: Stability and Outliers across Fifty Language Model Pre-Training Runs

FairMT-Bench: Benchmarking Fairness for Multi-turn Dialogue in Conversational LLMs

Easing Training Process of Rectified Flow Models Via Lengthening Inter-Path Distance

Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models

BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games

Prompt as Knowledge Bank: Boost Vision-language model via Structural Representation for zero-shot medical detection

Attention layers provably solve single-location regression

Release the Powers of Prompt Tuning: Cross-Modality Prompt Transfer

MagicDec: Breaking the Latency-Throughput Tradeoff for Long Context Generation with Speculative Decoding

PICASO: Permutation-Invariant Context Composition with State Space Models

Streamlining Redundant Layers to Compress Large Language Models

Efficient Automated Circuit Discovery in Transformers using Contextual Decomposition

TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysis

Exposing and Addressing Cross-Task Inconsistency in Unified Vision-Language Models

Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Vision Language Models are In-Context Value Learners

Adaptive Energy Alignment for Accelerating Test-Time Adaptation

See It from My Perspective: How Language Affects Cultural Bias in Image Understanding

A Probabilistic Perspective on Unlearning and Alignment for Large Language Models

Animate Your Thoughts: Reconstruction of Dynamic Natural Vision from Human Brain Activity

CONTRA: Conformal Prediction Region via Normalizing Flow Transformation

$\text{I}^2\text{AM}$: Interpreting Image-to-Image Latent Diffusion Models via Bi-Attribution Maps

Does Spatial Cognition Emerge in Frontier Models?

Understanding the Stability-based Generalization of Personalized Federated Learning

VSTAR: Generative Temporal Nursing for Longer Dynamic Video Synthesis

THE ROBUSTNESS OF DIFFERENTIABLE CAUSAL DISCOVERY IN MISSPECIFIED SCENARIOS

Provably Reliable Conformal Prediction Sets in the Presence of Data Poisoning

The Breakdown of Gaussian Universality in Classification of High-dimensional Linear Factor Mixtures

You Only Sample Once: Taming One-Step Text-to-Image Synthesis by Self-Cooperative Diffusion GANs

Web Agents with World Models: Learning and Leveraging Environment Dynamics in Web Navigation

Stem-OB: Generalizable Visual Imitation Learning with Stem-Like Convergent Observation through Diffusion Inversion

DenseMatcher: Learning 3D Semantic Correspondence for Category-Level Manipulation from a Single Demo

DistillHGNN: A Knowledge Distillation Approach for High-Speed Hypergraph Neural Networks

Discrete Distribution Networks

Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations

VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documents

RAG-DDR: Optimizing Retrieval-Augmented Generation Using Differentiable Data Rewards

SplineGS: Learning Smooth Trajectories in Gaussian Splatting for Dynamic Scene Reconstruction

System 1.x: Learning to Balance Fast and Slow Planning with Language Models

DarkBench: Benchmarking Dark Patterns in Large Language Models

Decoupling Layout from Glyph in Online Chinese Handwriting Generation

A Robust Method to Discover Causal or Anticausal Relation

Loss Landscape of Shallow ReLU-like Neural Networks: Stationary Points, Saddle Escape, and Network Embedding

Quantum-PEFT: Ultra parameter-efficient fine-tuning

Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent

Refine Knowledge of Large Language Models via Adaptive Contrastive Learning

A Conditional Independence Test in the Presence of Discretization

High-Quality Joint Image and Video Tokenization with Causal VAE

HyPoGen: Optimization-Biased Hypernetworks for Generalizable Policy Generation

SafeDiffuser: Safe Planning with Diffusion Probabilistic Models

Peeking Behind Closed Doors: Risks of LLM Evaluation by Private Data Curators

Reassessing EMNLP 2024’s Best Paper: Does Divergence-Based Calibration for MIAs Hold Up?

Chain-of-Focus Prompting: Leveraging Sequential Visual Cues to Prompt Large Autoregressive Vision Models

Flow: Modularized Agentic Workflow Automation

Online Reward-Weighted Fine-Tuning of Flow Matching with Wasserstein Regularization

Optimized Multi-Token Joint Decoding With Auxiliary Model for LLM Inference

ImageFolder: Autoregressive Image Generation with Folded Tokens

Intelligence at the Edge of Chaos

Manifold Constraint Reduces Exposure Bias in Accelerated Diffusion Sampling

DreamDistribution: Learning Prompt Distribution for Diverse In-distribution Generation

Open-Vocabulary Customization from CLIP via Data-Free Knowledge Distillation

Unintentional Unalignment: Likelihood Displacement in Direct Preference Optimization

HELMET: How to Evaluate Long-context Models Effectively and Thoroughly

Grid Cell-Inspired Fragmentation and Recall for Efficient Map Building

Everything is Editable: Extend Knowledge Editing to Unstructured Data in Large Language Models

Fine-Grained Verifiers: Preference Modeling as Next-token Prediction in Vision-Language Alignment

Fantastic Copyrighted Beasts and How (Not) to Generate Them

Duoduo CLIP: Efficient 3D Understanding with Multi-View Images

MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models

Trusted Multi-View Classification via Evolutionary Multi-View Fusion

Learned Reference-based Diffusion Sampler for multi-modal distributions

CLIBD: Bridging Vision and Genomics for Biodiversity Monitoring at Scale

BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval

Connectome Mapping: Shape-Memory Network via Interpretation of Contextual Semantic Information

SV4D: Dynamic 3D Content Generation with Multi-Frame and Multi-View Consistency

MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Anyprefer: An Agentic Framework for Preference Data Synthesis

Generative Flows on Synthetic Pathway for Drug Design

VD3D: Taming Large Video Diffusion Transformers for 3D Camera Control

Filtered not Mixed: Filtering-Based Online Gating for Mixture of Large Language Models

3DTrajMaster: Mastering 3D Trajectory for Multi-Entity Motion in Video Generation

EG4D: Explicit Generation of 4D Object without Score Distillation

The Geometry of Categorical and Hierarchical Concepts in Large Language Models

Salvage: Shapley-distribution Approximation Learning Via Attribution Guided Exploration for Explainable Image Classification

Jailbreak Antidote: Runtime Safety-Utility Balance via Sparse Representation Adjustment in Large Language Models

Selective Task Group Updates for Multi-Task Optimization

XLand-100B: A Large-Scale Multi-Task Dataset for In-Context Reinforcement Learning

Hierarchically Encapsulated Representation for Protocol Design in Self-Driving Labs

MeshMask: Physics-Based Simulations with Masked Graph Neural Networks

UIFace: Unleashing Inherent Model Capabilities to Enhance Intra-Class Diversity in Synthetic Face Recognition

RFMamba: Frequency-Aware State Space Model for RF-Based Human-Centric Perception

Adversarial Perturbations Cannot Reliably Protect Artists From Generative AI

Measuring Non-Adversarial Reproduction of Training Data in Large Language Models

Risk-Controlling Model Selection via Guided Bayesian Optimization

Edge-aware Image Smoothing with Relative Wavelet Domain Representation

Joint Fine-tuning and Conversion of Pretrained Speech and Language Models towards Linear Complexity

MrT5: Dynamic Token Merging for Efficient Byte-level Language Models

Reflexive Guidance: Improving OoDD in Vision-Language Models via Self-Guided Image-Adaptive Concept Generation

Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection

Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers

Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution

AugKD: Ingenious Augmentations Empower Knowledge Distillation for Image Super-Resolution

Building Interactable Replicas of Complex Articulated Objects via Gaussian Splatting

Diffusion$^2$: Dynamic 3D Content Generation via Score Composition of Video and Multi-view Diffusion Models

LOKI: A Comprehensive Synthetic Data Detection Benchmark using Large Multimodal Models

SVDQuant: Absorbing Outliers by Low-Rank Component for 4-Bit Diffusion Models

TIGeR: Unifying Text-to-Image Generation and Retrieval with Large Multimodal Models

IV-mixed Sampler: Leveraging Image Diffusion Models for Enhanced Video Synthesis

HASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agents

Multi-modal Agent Tuning: Building a VLM-Driven Agent for Efficient Tool Usage

SWEb: A Large Web Dataset for the Scandinavian Languages

SoftMatcha: A Soft and Fast Pattern Matcher for Billion-Scale Corpus Searches

BadJudge: Backdoor Vulnerabilities of LLM-As-A-Judge

Intrinsic Dimension Correlation: uncovering nonlinear connections in multimodal representations

Mechanistic Permutability: Match Features Across Layers

Neural Wave Equation for Irregularly Sampled Sequence Data

BANGS: Game-theoretic Node Selection for Graph Self-Training

Efficiently Parameterized Neural Metriplectic Systems

Neural Functions for Learning Periodic Signal

PIORF: Physics-Informed Ollivier-Ricci Flow for Long–Range Interactions in Mesh Graph Neural Networks

Global Convergence in Neural ODEs: Impact of Activation Functions

Mitigating Memorization in Language Models

MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding

Multi-domain Distribution Learning for De Novo Drug Design

As large as it gets – Studying Infinitely Large Convolutions via Neural Implicit Frequency Filters

Not All Prompts Are Made Equal: Prompt-based Pruning of Text-to-Image Diffusion Models

BrainUICL: An Unsupervised Individual Continual Learning Framework for EEG Applications

Analytic DAG Constraints for Differentiable DAG Learning

GameGen-X: Interactive Open-world Game Video Generation

Large-scale and Fine-grained Vision-language Pre-training for Enhanced CT Image Understanding

OASIS Uncovers: High-Quality T2I Models, Same Old Stereotypes

Coreset Selection via Reducible Loss in Continual Learning

Failures to Find Transferable Image Jailbreaks Between Vision-Language Models

Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis

A primer on analytical learning dynamics of nonlinear neural networks

KAN: Kolmogorov–Arnold Networks

NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG Signals

Super(ficial)-alignment: Strong Models May Deceive Weak Models in Weak-to-Strong Generalization

On the expressiveness and spectral bias of KANs

Fragment and Geometry Aware Tokenization of Molecules for Structure-Based Drug Design Using Language Models

Relax and Merge: A Simple Yet Effective Framework for Solving Fair $k$-Means and $k$-sparse Wasserstein Barycenter Problems

AutoBencher: Towards Declarative Benchmark Construction

Start Smart: Leveraging Gradients For Enhancing Mask-based XAI Methods

Evaluating Large Language Models through Role-Guide and Self-Reflection: A Comparative Study

Complementary Label Learning with Positive Label Guessing and Negative Label Enhancement

Offline RL in Regular Decision Processes: Sample Efficiency via Language Metrics

Jailbreaking as a Reward Misspecification Problem

PooDLe🐩: Pooled and dense self-supervised learning from naturalistic videos

Probabilistic Language-Image Pre-Training

A Single Goal is All You Need: Skills and Exploration Emerge from Contrastive RL without Rewards, Demonstrations, or Subgoals

Chain-of-Action: Faithful and Multimodal Question Answering through Large Language Models

LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient Fine-Tuning

BBCaL: Black-box Backdoor Detection under the Causality Lens

BlendRL: A Framework for Merging Symbolic and Neural Policy Learning

Mind Control through Causal Inference: Predicting Clean Images from Poisoned Data

Distribution Backtracking Builds A Faster Convergence Trajectory for Diffusion Distillation

What Makes a Good Diffusion Planner for Decision Making?

Tree-Wasserstein Distance for High Dimensional Data with a Latent Feature Hierarchy

NNsight and NDIF: Democratizing Access to Open-Weight Foundation Model Internals

Token Statistics Transformer: Linear-Time Attention via Variational Rate Reduction

Generalization Bounds and Model Complexity for Kolmogorov–Arnold Networks

Systems with Switching Causal Relations: A Meta-Causal Perspective

Metric-Driven Attributions for Vision Transformers

Strategist: Self-improvement of LLM Decision Making via Bi-Level Tree Search

InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciences

Towards Auto-Regressive Next-Token Prediction: In-context Learning Emerges from Generalization

When LLMs Play the Telephone Game: Cultural Attractors as Conceptual Tools to Evaluate LLMs in Multi-turn Settings

FlashRNN: I/O-Aware Optimization of Traditional RNNs on modern hardware

Cocoon: Robust Multi-Modal Perception with Uncertainty-Aware Sensor Fusion

Feedback Schrödinger Bridge Matching

Discrete Diffusion Schrödinger Bridge Matching for Graph Transformation

Transition Path Sampling with Improved Off-Policy Training of Diffusion Path Samplers

6DGS: Enhanced Direction-Aware Gaussian Splatting for Volumetric Rendering

3D Vision-Language Gaussian Splatting

A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discovery

TAID: Temporally Adaptive Interpolated Distillation for Efficient Knowledge Transfer in Language Models

Improving Large Language Model Planning with Action Sequence Similarity

Order-aware Interactive Segmentation

LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory

Beyond Circuit Connections: A Non-Message Passing Graph Transformer Approach for Quantum Error Mitigation

PT-T2I/V: An Efficient Proxy-Tokenized Diffusion Transformer for Text-to-Image/Video-Task

Efficient Reinforcement Learning with Large Language Model Priors

Efficient and Accurate Explanation Estimation with Distribution Compression

Causal Representation Learning from Multimodal Biomedical Observations

BigDocs: An Open Dataset for Training Multimodal Models on Document and Code Tasks

InsightBench: Evaluating Business Analytics Agents Through Multi-Step Insight Generation

UniGS: Unified Language-Image-3D Pretraining with Gaussian Splatting

Build-A-Scene: Interactive 3D Layout Control for Diffusion-Based Image Generation

Progressive Token Length Scaling in Transformer Encoders for Efficient Universal Segmentation

Do You Keep an Eye on What I Ask? Mitigating Multimodal Hallucination via Attention-Guided Ensemble Decoding

FIRING-Net: A filtered feature recycling network for speech enhancement

Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation

Dynamic Loss-Based Sample Reweighting for Improved Large Language Model Pretraining

Optimistic Games for Combinatorial Bayesian Optimization with Application to Protein Design

Adversarial Training for Defense Against Label Poisoning Attacks

InstaRevive: One-Step Image Enhancement via Dynamic Score Matching

KiVA: Kid-inspired Visual Analogies for Testing Large Multimodal Models

Self-Correcting Decoding with Generative Feedback for Mitigating Hallucinations in Large Vision-Language Models

Enhancing End-to-End Autonomous Driving with Latent World Model

E-Valuating Classifier Two-Sample Tests

Geometry of Neural Reinforcement Learning in Continuous State and Action Spaces

Automated Design of Agentic Systems

Intelligent Go-Explore: Standing on the Shoulders of Giant Foundation Models

Visual Description Grounding Reduces Hallucinations and Boosts Reasoning in LVLMs

Online Preference Alignment for Language Models via Count-based Exploration

Controllable Safety Alignment: Inference-Time Adaptation to Diverse Safety Requirements

Shedding Light on Time Series Classification using Interpretability Gated Networks

DenseGrounding: Improving Dense Language-Vision Semantics for Ego-centric 3D Visual Grounding

Synthio: Augmenting Small-Scale Audio Classification Datasets with Synthetic Data

MMAU: A Massive Multi-Task Audio Understanding and Reasoning Benchmark

S4M: S4 for multivariate time series forecasting with Missing values

Context-Parametric Inversion: Why Instruction Finetuning May Not Actually Improve Context Reliance

Investigating Pattern Neurons in Urban Time Series Forecasting

Open-World Reinforcement Learning over Long Short-Term Imagination

CoMRes: Semi-Supervised Time Series Forecasting Utilizing Consensus Promotion of Multi-Resolution

SymDiff: Equivariant Diffusion via Stochastic Symmetrisation

Learning Generalizable Skills from Offline Multi-Task Data for Multi-Agent Cooperation

Multi-Dimensional Conformal Prediction

GIFT: Unlocking Full Potential of Labels in Distilled Dataset at Near-zero Cost

Vision-LSTM: xLSTM as Generic Vision Backbone

Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders

Plug, Play, and Generalize: Length Extrapolation with Pointer-Augmented Neural Memory

Understanding Optimization in Deep Learning with Central Flows

Counterfactual Concept Bottleneck Models

Black-Box Detection of Language Model Watermarks

Stochastic Semi-Gradient Descent for Learning Mean Field Games with Population-Aware Function Approximation

Learning from Imperfect Human Feedback: A Tale from Corruption-Robust Dueling

CATCH: Channel-Aware Multivariate Time Series Anomaly Detection via Frequency Patching

Manifold Induced Biases for Zero-shot and Few-shot Detection of Generated Images

Single-agent Poisoning Attacks Suffice to Ruin Multi-Agent Learning

UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation

DEPfold: RNA Secondary Structure Prediction as Dependency Parsing.

Fréchet Wavelet Distance: A Domain-Agnostic Metric for Image Generation

KLay: Accelerating Arithmetic Circuits for Neurosymbolic AI

MoS: Unleashing Parameter Efficiency of Low-Rank Adaptation with Mixture of Shards

Inference Optimal VLMs Need Fewer Visual Tokens and More Parameters

NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models

How to Probe: Simple Yet Effective Techniques for Improving Post-hoc Explanations

$\gamma-$MoD: Exploring Mixture-of-Depth Adaptation for Multimodal Large Language Models

Air Quality Prediction with Physics-Guided Dual Neural ODEs in Open Systems

Learning the Optimal Stopping for Early Classification within Finite Horizons via Sequential Probability Ratio Test

Robust Root Cause Diagnosis using In-Distribution Interventions

Modeling Complex System Dynamics with Flow Matching Across Time and Conditions

From Search to Sampling: Generative Models for Robust Algorithmic Recourse

Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws

IterComp: Iterative Composition-Aware Feedback Learning from Model Gallery for Text-to-Image Generation

Consistency Models Made Easy

IDA-VLM: Towards Movie Understanding via ID-Aware Large Vision-Language Model

Prioritized Generative Replay

Generalization v.s. Memorization: Tracing Language Models’ Capabilities Back to Pretraining Data

DECO: Unleashing the Potential of ConvNets for Query-based Detection and Segmentation

AdaRankGrad: Adaptive Gradient Rank and Moments for Memory-Efficient LLMs Training and Fine-Tuning

ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation

Cross-Domain Offline Policy Adaptation with Optimal Transport and Dataset Constraint

Semantic Aware Representation Learning for Lifelong Learning

Compute-Optimal LLMs Provably Generalize Better with Scale

Episodic Novelty Through Temporal Distance

Calibrating LLMs with Information-Theoretic Evidential Deep Learning

AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents

Learning View-invariant World Models for Visual Robotic Manipulation

Swift Hydra: Self-Reinforcing Generative Framework for Anomaly Detection with Multiple Mamba Models

Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues

Solving hidden monotone variational inequalities with surrogate losses

DOPL: Direct Online Preference Learning for Restless Bandits with Preference Feedback

ML4TSPBench: Drawing Methodological Principles for TSP and Beyond from Streamlined Design Space of Learning and Search

Surprising Effectiveness of pretraining Ternary Language Model at Scale

Looped Transformers for Length Generalization

JudgeBench: A Benchmark for Evaluating LLM-Based Judges

Capturing the Temporal Dependence of Training Data Influence

Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold

Selective Aggregation for Low-Rank Adaptation in Federated Learning

Towards Robust Multimodal Open-set Test-time Adaptation via Adaptive Entropy-aware Optimization

Data Shapley in One Training Run

Reinforcement learning with combinatorial actions for coupled restless bandits

Scalable Bayesian Learning with posteriors

Let SSMs be ConvNets: State-space Modeling with Optimal Tensor Contractions

An Undetectable Watermark for Generative Image Models

HADAMRNN: BINARY AND SPARSE TERNARY ORTHOGONAL RNNS

Incorporating Visual Correspondence into Diffusion Model for Virtual Try-On

ReCogLab: a framework testing relational reasoning & cognitive hypotheses on LLMs

MAP: Multi-Human-Value Alignment Palette

Enhancing Prediction Performance through Influence Measure

Regularized Proportional Fairness Mechanism for Resource Allocation Without Money

MovieDreamer: Hierarchical Generation for Coherent Long Visual Sequences

Occlusion-aware Non-Rigid Point Cloud Registration via Unsupervised Neural Deformation Correntropy

Recite, Reconstruct, Recollect: Memorization in LMs as a Multifaceted Phenomenon

LLMs' Potential Influences on Our Democracy: Challenges and Opportunities

X-NeMo: Expressive Neural Motion Reenactment via Disentangled Latent Attention

PaRa: Personalizing Text-to-Image Diffusion via Parameter Rank Reduction

Multimodal Situational Safety

A Statistical Approach for Controlled Training Data Detection

Rethinking Visual Counterfactual Explanations Through Region Constraint

The Semantic Hub Hypothesis: Language Models Share Semantic Representations Across Languages and Modalities

Tell me about yourself: LLMs are aware of their learned behaviors

Controllable Context Sensitivity and the Knob Behind It

MixMax: Distributional Robustness in Function Space via Optimal Data Mixtures

PRDP: Progressively Refined Differentiable Physics

Retrieval Augmented Diffusion Model for Structure-informed Antibody Design and Optimization

Training-free Camera Control for Video Generation

Flaws of ImageNet, Computer Vision's Favourite Dataset

DiffGAD: A Diffusion-based Unsupervised Graph Anomaly Detector

Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement

Linear Mode Connectivity in Differentiable Tree Ensembles

SONICS: Synthetic Or Not - Identifying Counterfeit Songs

Towards Scalable Topological Regularizers

Unified Parameter-Efficient Unlearning for LLMs

ImpScore: A Learnable Metric For Quantifying The Implicitness Level of Sentences

AIR-BENCH 2024: A Safety Benchmark based on Regulation and Policies Specified Risk Categories

Conformal Language Model Reasoning with Coherent Factuality

PABBO: Preferential Amortized Black-Box Optimization

EIA: ENVIRONMENTAL INJECTION ATTACK ON GENERALIST WEB AGENTS FOR PRIVACY LEAKAGE

Composing Unbalanced Flows for Flexible Docking and Relaxation

Scaling Large Language Model-based Multi-Agent Collaboration

Adding Conditional Control to Diffusion Models with Reinforcement Learning

The Belief State Transformer

GANDALF: Generative AttentioN based Data Augmentation and predictive modeLing Framework for personalized cancer treatment

Tamper-Resistant Safeguards for Open-Weight LLMs

Utility-Directed Conformal Prediction: A Decision-Aware Framework for Actionable Uncertainty Quantification

Provably Accurate Shapley Value Estimation via Leverage Score Sampling

MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models

Integral Performance Approximation for Continuous-Time Reinforcement Learning Control

The Crucial Role of Samplers in Online Direct Preference Optimization

SAGEPhos: Sage Bio-Coupled and Augmented Fusion for Phosphorylation Site Detection

CAT-3DGS: A Context-Adaptive Triplane Approach to Rate-Distortion-Optimized 3DGS Compression

Adversarial Machine Unlearning

Divide and Translate: Compositional First-Order Logic Translation and Verification for Complex Logical Reasoning

ET-SEED: EFFICIENT TRAJECTORY-LEVEL SE(3) EQUIVARIANT DIFFUSION POLICY

$\sigma$-zero: Gradient-based Optimization of $\ell_0$-norm Adversarial Examples

Centrality-guided Pre-training for Graph

From Commands to Prompts: LLM-based Semantic File System for AIOS

Student-Informed Teacher Training

Analysis of Linear Mode Connectivity via Permutation-Based Weight Matching: With Insights into Other Permutation Search Methods

AniSDF: Fused-Granularity Neural Surfaces with Anisotropic Encoding for High-Fidelity 3D Reconstruction

A Distributional Approach to Uncertainty-Aware Preference Alignment Using Offline Demonstrations

Toward Exploratory Inverse Constraint Inference with Generative Diffusion Verifiers

TASAR: Transfer-based Attack on Skeletal Action Recognition

Generative Adapter: Contextualizing Language Models in Parameters with A Single Forward Pass

Hotspot-Driven Peptide Design via Multi-Fragment Autoregressive Extension

Framer: Interactive Frame Interpolation

Breaking Neural Network Scaling Laws with Modularity

Semantic Temporal Abstraction via Vision-Language Model Guidance for Efficient Reinforcement Learning

CL-MFAP: A Contrastive Learning-Based Multimodal Foundation Model for Molecular Property Prediction and Antibiotic Screening

Size-Generalizable RNA Structure Evaluation by Exploring Hierarchical Geometries

Add-it: Training-Free Object Insertion in Images With Pretrained Diffusion Models

DenoiseVAE: Learning Molecule-Adaptive Noise Distributions for Denoising-based 3D Molecular Pre-training

Learnable Expansion of Graph Operators for Multi-Modal Feature Fusion

Learning to Discover Regulatory Elements for Gene Expression Prediction

Language Agents Meet Causality -- Bridging LLMs and Causal World Models

Gaussian Head & Shoulders: High Fidelity Neural Upper Body Avatars with Anchor Gaussian Guided Texture Warping

RGB-Event ISP: The Dataset and Benchmark

Self-Normalized Resets for Plasticity in Continual Learning

UNSURE: self-supervised learning with Unknown Noise level and Stein's Unbiased Risk Estimate

To Trust or Not to Trust? Enhancing Large Language Models' Situated Faithfulness to External Contexts

A Differentiable Rank-Based Objective for Better Feature Learning

PhyloLM: Inferring the Phylogeny of Large Language Models and Predicting their Performances in Benchmarks

Concept Bottleneck Large Language Models

Interpreting and Editing Vision-Language Representations to Mitigate Hallucinations

Energy-based Backdoor Defense Against Federated Graph Learning

Multi-Reward as Condition for Instruction-based Image Editing

Fugatto 1: Foundational Generative Audio Transformer Opus 1

Conformal Prediction Sets Can Cause Disparate Impact

MLLMs Know Where to Look: Training-free Perception of Small Visual Details with Multimodal LLMs

UniMatch: Universal Matching from Atom to Task for Few-Shot Drug Discovery

SimpleTM: A Simple Baseline for Multivariate Time Series Forecasting

Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression

Complexity Lower Bounds of Adaptive Gradient Algorithms for Non-convex Stochastic Optimization under Relaxed Smoothness

Langevin Soft Actor-Critic: Efficient Exploration through Uncertainty-Driven Critic Learning

Knowing Your Target: Target-Aware Transformer Makes Better Spatio-Temporal Video Grounding

Conflict-Averse Gradient Aggregation for Constrained Multi-Objective Reinforcement Learning

Nesterov acceleration in benignly non-convex landscapes

Growth Inhibitors for Suppressing Inappropriate Image Concepts in Diffusion Models

Reasoning of Large Language Models over Knowledge Graphs with Super-Relations

Metalic: Meta-Learning In-Context with Protein Language Models

AVHBench: A Cross-Modal Hallucination Benchmark for Audio-Visual Large Language Models

Graph Neural Networks Are More Than Filters: Revisiting and Benchmarking from A Spectral Perspective

ClassDiffusion: More Aligned Personalization Tuning with Explicit Class Guidance

Graph Neural Preconditioners for Iterative Solutions of Sparse Linear Systems

A Large-Scale 3D Face Mesh Video Dataset via Neural Re-parameterized Optimization

Graph Neural Networks for Edge Signals: Orientation Equivariance and Invariance

Pareto Prompt Optimization

GS-LiDAR: Generating Realistic LiDAR Point Clouds with Panoramic Gaussian Splatting

Bad-PFL: Exploiting Backdoor Attacks against Personalized Federated Learning

TULIP: Token-length Upgraded CLIP

Tuning Timestep-Distilled Diffusion Model Using Pairwise Sample Optimization

More Experts Than Galaxies: Conditionally-Overlapping Experts with Biologically-Inspired Fixed Routing

Continuity-Preserving Convolutional Autoencoders for Learning Continuous Latent Dynamical Models from Images

Epistemic Monte Carlo Tree Search

GMValuator: Similarity-based Data Valuation for Generative Models

Scaling up Masked Diffusion Models on Text

The Foundations of Tokenization: Statistical and Computational Concerns

Posterior-Mean Rectified Flow: Towards Minimum MSE Photo-Realistic Image Restoration

PointOBB-v2: Towards Simpler, Faster, and Stronger Single Point Supervised Oriented Object Detection

A Generic Framework for Conformal Fairness

MA$^2$E: Addressing Partial Observability in Multi-Agent Reinforcement Learning with Masked Auto-Encoder

Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data

Efficient Evolutionary Search Over Chemical Space with Large Language Models

TexTailor: Customized Text-aligned Texturing via Effective Resampling

Context Clues: Evaluating Long Context Models for Clinical Prediction Tasks on EHR Data

Steering Protein Family Design through Profile Bayesian Flow

Achieving Dimension-Free Communication in Federated Learning via Zeroth-Order Optimization

A Large-scale Dataset and Benchmark for Commuting Origin-Destination Flow Generation

A Periodic Bayesian Flow for Material Generation

On the Crucial Role of Initialization for Matrix Factorization

Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

FreqPrior: Improving Video Diffusion Models with Frequency Filtering Gaussian Noise

Taming Overconfidence in LLMs: Reward Calibration in RLHF

Doubly Optimal Policy Evaluation for Reinforcement Learning

Monet: Mixture of Monosemantic Experts for Transformers

SAFREE: Training-Free and Adaptive Guard for Safe Text-to-Image And Video Generation

Scalable Mechanistic Neural Networks

CodeMMLU: A Multi-Task Benchmark for Assessing Code Understanding & Reasoning Capabilities of CodeLLMs

Distance-Based Tree-Sliced Wasserstein Distance

Structure Language Models for Protein Conformation Generation

Causal Reasoning and Large Language Models: Opening a New Frontier for Causality

RazorAttention: Efficient KV Cache Compression Through Retrieval Heads

HarmAug: Effective Data Augmentation for Knowledge Distillation of Safety Guard Models

ChroKnowledge: Unveiling Chronological Knowledge of Language Models in Multiple Domains

Brain-inspired $L_p$-Convolution benefits large kernels and aligns better with visual cortex

Learning Transformer-based World Models with Contrastive Predictive Coding

Benchmarking Agentic Workflow Generation

Adapt-$\infty$: Scalable Continual Multimodal Instruction Tuning via Dynamic Data Selection

CREMA: Generalizable and Efficient Video-Language Reasoning via Multimodal Modular Fusion

Causal Order: The Key to Leveraging Imperfect Experts in Causal Inference

BAMDP Shaping: a Unified Theoretical Framework for Intrinsic Motivation and Reward Shaping

CheapNet: Cross-attention on Hierarchical representations for Efficient protein-ligand binding Affinity Prediction

On the Byzantine-Resilience of Distillation-Based Federated Learning

Painting with Words: Elevating Detailed Image Captioning with Benchmark and Alignment Learning

AutoEval: Autonomous Evaluation of LLMs for Truth Maintenance and Reasoning Tasks

STAR: Stability-Inducing Weight Perturbation for Continual Learning

Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF

Online Reinforcement Learning in Non-Stationary Context-Driven Environments

Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models

SINGAPO: Single Image Controlled Generation of Articulated Parts in Objects

Intricacies of Feature Geometry in Large Language Models

Efficient Sparse PCA via Block-Diagonalization

Exploring Prosocial Irrationality for LLM Agents: A Social Cognition View

Adversarial Attacks on Data Attribution

Zeroth-Order Policy Gradient for Reinforcement Learning from Human Feedback without Reward Inference

Captured by Captions: On Memorization and its Mitigation in CLIP Models

Comparing Targeting Strategies for Maximizing Social Welfare with Limited Resources

Unposed Sparse Views Room Layout Reconstruction in the Age of Pretrain Model

Sufficient Context: A New Lens on Retrieval Augmented Generation Systems

Revisiting text-to-image evaluation with Gecko: on metrics, prompts, and human rating

Topograph: An Efficient Graph-Based Framework for Strictly Topology Preserving Image Segmentation

Dynamic Neural Fortresses: An Adaptive Shield for Model Extraction Defense

Spherical Tree-Sliced Wasserstein Distance

Task Descriptors Help Transformers Learn Linear Models In-Context

SiMHand: Mining Similar Hands for Large-Scale 3D Hand Pose Pre-training

SimXRD-4M: Big Simulated X-ray Diffraction Data and Crystal Symmetry Classification Benchmark

Differentially Private Federated Learning with Time-Adaptive Privacy Spending

Unbounded: A Generative Infinite Game of Character Life Simulation

Circuit Transformer: A Transformer That Preserves Logical Equivalence

PFGuard: A Generative Framework with Privacy and Fairness Safeguards

ParaSolver: A Hierarchical Parallel Integral Solver for Diffusion Models

SPDIM: Source-Free Unsupervised Conditional and Label Shift Adaptation in EEG

Advancing Prompt-Based Methods for Replay-Independent General Continual Learning

Bridging the Gap between Database Search and \emph{De Novo} Peptide Sequencing with SearchNovo

LoRA3D: Low-Rank Self-Calibration of 3D Geometric Foundation models

Steering Large Language Models between Code Execution and Textual Reasoning

u-$\mu$P: The Unit-Scaled Maximal Update Parametrization

Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models

ThinK: Thinner Key Cache by Query-Driven Pruning

Precise Parameter Localization for Textual Generation in Diffusion Models

Chunk-Distilled Language Modeling

BTBS-LNS: Binarized-Tightening, Branch and Search on Learning LNS Policies for MIP

Gaussian Ensemble Belief Propagation for Efficient Inference in High-Dimensional, Black-box Systems

On the Computation of the Fisher Information in Continual Learning

Newton Meets Marchenko-Pastur: Massively Parallel Second-Order Optimization with Hessian Sketching and Debiasing

Discovering Influential Neuron Path in Vision Transformers

CrossMPT: Cross-attention Message-passing Transformer for Error Correcting Codes

NeSyC: A Neuro-symbolic Continual Learner For Complex Embodied Tasks in Open Domains

What Has Been Overlooked in Contrastive Source-Free Domain Adaptation: Leveraging Source-Informed Latent Augmentation within Neighborhood Context

Differentiable Rule Induction from Raw Sequence Inputs

Track-On: Transformer-based Online Point Tracking with Memory

Minimal Impact ControlNet: Advancing Multi-ControlNet Integration

Needle Threading: Can LLMs Follow Threads Through Near-Million-Scale Haystacks?

Enhancing Graph Of Thought: Enhancing Prompts with LLM Rationales and Dynamic Temperature Control

Permute-and-Flip: An optimally stable and watermarkable decoder for LLMs

Black Sheep in the Herd: Playing with Spuriously Correlated Attributes for Vision-Language Recognition

Nova: Generative Language Models for Assembly Code with Hierarchical Attention and Contrastive Learning

Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting

MCNC: Manifold-Constrained Reparameterization for Neural Compression

Unlocking Point Processes through Point Set Diffusion

Interpreting Language Reward Models via Contrastive Explanations

Bridging Information Asymmetry in Text-video Retrieval: A Data-centric Approach

OGBench: Benchmarking Offline Goal-Conditioned RL

The Superposition of Diffusion Models Using the Itô Density Estimator

Improving Probabilistic Diffusion Models With Optimal Diagonal Covariance Matching

Diffusing States and Matching Scores: A New Framework for Imitation Learning

Boltzmann Semantic Score: A Semantic Metric for Evaluating Large Vision Models Using Large Language Models

Exact Certification of (Graph) Neural Networks Against Label Poisoning

LLM-based Typed Hyperresolution for Commonsense Reasoning with Knowledge Bases

Beyond Mere Token Analysis: A Hypergraph Metric Space Framework for Defending Against Socially Engineered LLM Attacks

Variational Diffusion Posterior Sampling with Midpoint Guidance

PCNN: Probable-Class Nearest-Neighbor Explanations Improve Fine-Grained Image Classification Accuracy for AIs and Humans

Once-for-All: Controllable Generative Image Compression with Dynamic Granularity Adaptation

Revealing the 3D Cosmic Web through Gravitationally Constrained Neural Fields

PeriodWave: Multi-Period Flow Matching for High-Fidelity Waveform Generation

LLMOPT: Learning to Define and Solve General Optimization Problems from Scratch

Unlocking the Power of Function Vectors for Characterizing and Mitigating Catastrophic Forgetting in Continual Instruction Tuning

Planning in Natural Language Improves LLM Search for Code Generation

NeRAF: 3D Scene Infused Neural Radiance and Acoustic Fields

Language Imbalance Driven Rewarding for Multilingual Self-improving

Imputation for prediction: beware of diminishing returns.

An Exploration with Entropy Constrained 3D Gaussians for 2D Video Compression

Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes

MarS: a Financial Market Simulation Engine Powered by Generative Foundation Model

Provably Safeguarding a Classifier from OOD and Adversarial Samples

ContextGNN: Beyond Two-Tower Recommendation Systems

Physics-aligned field reconstruction with diffusion bridge

Semi-Supervised CLIP Adaptation by Enforcing Semantic and Trapezoidal Consistency

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis

Nonlinear Sequence Embedding by Monotone Variational Inequality

Efficient Biological Data Acquisition through Inference Set Design

Learning Efficient Positional Encodings with Graph Neural Networks

EFFICIENT JAILBREAK ATTACK SEQUENCES ON LARGE LANGUAGE MODELS VIA MULTI-ARMED BANDIT-BASED CONTEXT SWITCHING

Dynamic Assortment Selection and Pricing with Censored Preference Feedback

Step-by-Step Reasoning for Math Problems via Twisted Sequential Monte Carlo

Consistent Flow Distillation for Text-to-3D Generation

Diffusion State-Guided Projected Gradient for Inverse Problems

Text2PDE: Latent Diffusion Models for Accessible Physics Simulation

Breaking the Reclustering Barrier in Centroid-based Deep Clustering

Fast and Slow Streams for Online Time Series Forecasting Without Information Leakage

AttriBoT: A Bag of Tricks for Efficiently Approximating Leave-One-Out Context Attribution

COPER: Correlation-based Permutations for Multi-View Clustering

MVTokenFlow: High-quality 4D Content Generation using Multiview Token Flow

Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling

Identifiable Exchangeable Mechanisms for Causal Structure and Representation Learning

MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Masked Image Modeling Representations

MLLM can see? Dynamic Correction Decoding for Hallucination Mitigation

DPaI: Differentiable Pruning at Initialization with Node-Path Balance Principle

Variational Best-of-N Alignment

Weighted-Reward Preference Optimization for Implicit Model Fusion

DEPT: Decoupled Embeddings for Pre-training Language Models

Self-Improvement for Neural Combinatorial Optimization: Sample Without Replacement, but Improvement

Can We Talk Models Into Seeing the World Differently?

LiveXiv - A Multi-Modal live benchmark based on Arxiv papers content

On the Optimization and Generalization of Two-layer Transformers with Sign Gradient Descent

Mining your own secrets: Diffusion Classifier Scores for Continual Personalization of Text-to-Image Diffusion Models

ChemAgent: Self-updating Memories in Large Language Models Improves Chemical Reasoning

Capability Localization: Capabilities Can be Localized rather than Individual Knowledge

Interpreting Emergent Planning in Model-Free Reinforcement Learning

Robust LLM safeguarding via refusal feature adversarial training

DRESSing Up LLM: Efficient Stylized Question-Answering via Style Subspace Editing

Neuralized Markov Random Field for Interaction-Aware Stochastic Human Trajectory Prediction

Stable Hadamard Memory: Revitalizing Memory-Augmented Agents for Reinforcement Learning

Training-Free Message Passing for Learning on Hypergraphs

MIA-DPO: Multi-Image Augmented Direct Preference Optimization For Large Vision-Language Models

Graph Neural Networks Can (Often) Count Substructures

Learning Long Range Dependencies on Graphs via Random Walks

PostEdit: Posterior Sampling for Efficient Zero-Shot Image Editing

Cross the Gap: Exposing the Intra-modal Misalignment in CLIP via Modality Inversion

Monte Carlo Planning with Large Language Model for Text-Based Game Agents

Accelerating Goal-Conditioned Reinforcement Learning Algorithms and Research

Horizon Generalization in Reinforcement Learning

Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons

TODO: Enhancing LLM Alignment with Ternary Preferences

Towards Calibrated Deep Clustering Network

Local convergence of simultaneous min-max algorithms to differential equilibrium on Riemannian manifold

Diffusion-based Neural Network Weights Generation

Towards Synergistic Path-based Explanations for Knowledge Graph Completion: Exploration and Evaluation

Learning Task Belief Similarity with Latent Dynamics for Meta-Reinforcement Learning

Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target Generation

Generative Adversarial Ranking Nets

Digi-Q: Learning VLM Q-Value Functions for Training Device-Control Agents

Beyond-Expert Performance with Limited Demonstrations: Efficient Imitation Learning with Double Exploration

Second-Order Fine-Tuning without Pain for LLMs: A Hessian Informed Zeroth-Order Optimizer

Sharpness-Aware Black-Box Optimization

Generalized Video Moment Retrieval

ProAdvPrompter: A Two-Stage Journey to Effective Adversarial Prompting for LLMs

Robust Representation Consistency Model via Contrastive Denoising

DOCS: Quantifying Weight Similarity for Deeper Insights into Large Language Models

Equivariant Neural Functional Networks for Transformers

DLEFT-MKC: Dynamic Late Fusion Multiple Kernel Clustering with Robust Tensor Learning via Min-Max Optimization

Encryption-Friendly LLM Architecture

OATS: Outlier-Aware Pruning Through Sparse and Low Rank Decomposition

6D Object Pose Tracking in Internet Videos for Robotic Manipulation

Contextual Self-paced Learning for Weakly Supervised Spatio-Temporal Video Grounding

Directional Gradient Projection for Robust Fine-Tuning of Foundation Models

Resolution Attack: Exploiting Image Compression to Deceive Deep Neural Networks

Think while You Generate: Discrete Diffusion with Planned Denoising

Sylber: Syllabic Embedding Representation of Speech from Raw Audio

CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph

A Simple yet Effective $\Delta\Delta G$ Predictor is An Unsupervised Antibody Optimizer and Explainer

Generalized Principal-Agent Problem with a Learning Agent

Variational Search Distributions

Flow Distillation Sampling: Regularizing 3D Gaussians with Pre-trained Matching Priors

Why Does the Effective Context Length of LLMs Fall Short?

Unsupervised Meta-Learning via In-Context Learning

MMSearch: Unveiling the Potential of Large Models as Multi-modal Search Engines

Temporal Reasoning Transfer from Text to Video

Scaling Diffusion Language Models via Adaptation from Autoregressive Models

Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport

HShare: Fast LLM Decoding by Hierarchical Key-Value Sharing

Studying the Interplay Between the Actor and Critic Representations in Reinforcement Learning

Understanding the Generalization of In-Context Learning in Transformers: An Empirical Study

Interpreting Global Perturbation Robustness of Image Models using Axiomatic Spectral Importance Decomposition

Instruct-SkillMix: A Powerful Pipeline for LLM Instruction Tuning

AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors

ToolGen: Unified Tool Retrieval and Calling via Generation

Prototype antithesis for biological few-shot class-incremental learning

WavTokenizer: an Efficient Acoustic Discrete Codec Tokenizer for Audio Language Modeling

Score-based Self-supervised MRI Denoising

Unhackable Temporal Reward for Scalable Video MLLMs

Overcoming Lower-Level Constraints in Bilevel Optimization: A Novel Approach with Regularized Gap Functions

Advantage-Guided Distillation for Preference Alignment in Small Language Models

Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model

ChartMoE: Mixture of Diversely Aligned Expert Connector for Chart Understanding

ProtComposer: Compositional Protein Structure Generation with 3D Ellipsoids

DaWin: Training-free Dynamic Weight Interpolation for Robust Adaptation

On the self-verification limitations of large language models on reasoning and planning tasks

Grounding by Trying: LLMs with Reinforcement Learning-Enhanced Retrieval

A Tight Convergence Analysis of Inexact Stochastic Proximal Point Algorithm for Stochastic Composite Optimization Problems

Conformalized Survival Analysis for General Right-Censored Data

Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning

ILLUSION: Unveiling Truth with a Comprehensive Multi-Modal, Multi-Lingual Deepfake Dataset

Topological Schrödinger Bridge Matching

FlowDec: A flow-based full-band general audio codec with high perceptual quality

Can a Large Language Model be a Gaslighter?

MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworks

VILA-U: a Unified Foundation Model Integrating Visual Understanding and Generation

InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales

Jamba: Hybrid Transformer-Mamba Language Models

Exploring The Forgetting in Adversarial Training: A Novel Method for Enhancing Robustness

PhyloVAE: Unsupervised Learning of Phylogenetic Trees via Variational Autoencoders

StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurization

Learn Your Reference Model for Real Good Alignment

Shh, don't say that! Domain Certification in LLMs

Min-K%++: Improved Baseline for Pre-Training Data Detection from Large Language Models

Selective Attention Improves Transformer

IgGM: A Generative Model for Functional Antibody and Nanobody Design

Associative memory and dead neurons

Diffusion Models Are Real-Time Game Engines

Block Verification Accelerates Speculative Decoding

DeepRTL: Bridging Verilog Understanding and Generation with a Unified Representation Model

Wicked Oddities: Selectively Poisoning for Effective Clean-Label Backdoor Attacks

ImDy: Human Inverse Dynamics from Imitated Observations

Bonsai: Gradient-free Graph Condensation for Node Classification

Positional Embeddings in Transformer Models: Evolution from Text to Vision Domains

CameraCtrl: Enabling Camera Control for Video Diffusion Models

SciLitLLM: How to Adapt LLMs for Scientific Literature Understanding

Progressive Parameter Efficient Transfer Learning for Semantic Segmentation

SPA: 3D Spatial-Awareness Enables Effective Embodied Representation

OmniKV: Dynamic Context Selection for Efficient Long-Context LLMs

API Pack: A Massive Multi-Programming Language Dataset for API Call Generation

OBI-Bench: Can LMMs Aid in Study of Ancient Script on Oracle Bones?

PathGen-1.6M: 1.6 Million Pathology Image-text Pairs Generation through Multi-agent Collaboration

Shot2Story: A New Benchmark for Comprehensive Understanding of Multi-shot Videos

Deep Networks Learn Features From Local Discontinuities in the Label Function

LongGenBench: Benchmarking Long-Form Generation in Long Context LLMs

Pursuing Better Decision Boundaries for Long-Tailed Object Detection via Category Information Amount

InvestESG: A multi-agent reinforcement learning benchmark for studying climate investment as a social dilemma

REGENT: A Retrieval-Augmented Generalist Agent That Can Act In-Context in New Environments

Towards Semantic Equivalence of Tokenization in Multimodal LLM

Efficient Causal Decision Making with One-sided Feedback

Reliable and Diverse Evaluation of LLM Medical Knowledge Mastery

Federated Domain Generalization with Data-free On-server Matching Gradient

DINOv2: Learning Robust Visual Features without Supervision

Beyond the convexity assumption: Realistic tabular data generation under quantifier-free real linear constraints

Optimizing 4D Gaussians for Dynamic Scene Video from Single Landscape Images

Can Knowledge Editing Really Correct Hallucinations?

BadRobot: Jailbreaking Embodied LLMs in the Physical World

PAD: Personalized Alignment at Decoding-time

Asymmetric Factorized Bilinear Operation for Vision Transformer

ConcreTizer: Model Inversion Attack via Occupancy Classification and Dispersion Control for 3D Point Cloud Restoration

To Tackle Adversarial Transferability: A Novel Ensemble Training Method with Fourier Transformation

TDDBench: A Benchmark for Training data detection

Evidential Learning-based Certainty Estimation for Robust Dense Feature Matching

Learning Structured Representations by Embedding Class Hierarchy with Fast Optimal Transport

From Sparse Dependence to Sparse Attention: Unveiling How Chain-of-Thought Enhances Transformer Sample Efficiency

Navigation-Guided Sparse Scene Representation for End-to-End Autonomous Driving

Can Textual Gradient Work in Federated Learning?

DARE the Extreme: Revisiting Delta-Parameter Pruning For Fine-Tuned Models

Scaling Instruction-tuned LLMs to Million-token Contexts via Hierarchical Synthetic Data Generation

Morphing Tokens Draw Strong Masked Image Models

An Effective Manifold-based Optimization Method for Distributionally Robust Classification

Fiddler: CPU-GPU Orchestration for Fast Inference of Mixture-of-Experts Models

Accelerating Neural ODEs: A Variational Formulation-based Approach

TRACE: Temporal Grounding Video LLM via Causal Event Modeling

Learning Harmonized Representations for Speculative Sampling

Re-Thinking Inverse Graphics With Large Language Models

Any-step Dynamics Model Improves Future Predictions for Online and Offline Reinforcement Learning

Multiple Heads are Better than One: Mixture of Modality Knowledge Experts for Entity Representation Learning

Multi-session, multi-task neural decoding from distinct cell-types and brain regions

In vivo cell-type and brain region classification via multimodal contrastive learning

Both Ears Wide Open: Towards Language-Driven Spatial Audio Generation

Efficient Discovery of Pareto Front for Multi-Objective Reinforcement Learning

AstroCompress: A benchmark dataset for multi-purpose compression of astronomical data

ToolDial: Multi-turn Dialogue Generation Method for Tool-Augmented Language Models

Exploring the Design Space of Visual Context Representation in Video MLLMs

FLIP: Flow-Centric Generative Planning as General-Purpose Manipulation World Model

Revisiting Source-Free Domain Adaptation: a New Perspective via Uncertainty Control

Correlating instruction-tuning (in multimodal models) with vision-language processing (in the brain)

ReNovo: Retrieval-Based \emph{De Novo} Mass Spectrometry Peptide Sequencing

Second Order Bounds for Contextual Bandits with Function Approximation

A Theoretical Framework for Partially-Observed Reward States in RLHF

MotionAura: Generating High-Quality and Motion Consistent Videos using Discrete Diffusion

ORSO: Accelerating Reward Design via Online Reward Selection and Policy Optimization

Fast Training of Sinusoidal Neural Fields via Scaling Initialization

ZIP: An Efficient Zeroth-order Prompt Tuning for Black-box Vision-Language Models

Locally Connected Echo State Networks for Time Series Forecasting

Gyrogroup Batch Normalization

Autocorrelation Matters: Understanding the Role of Initialization Schemes for State Space Models

Long Context Compression with Activation Beacon

Do Vision & Language Decoders use Images and Text equally? How Self-consistent are their Explanations?

Neural Eulerian Scene Flow Fields

Differentiation and Specialization of Attention Heads via the Refined Local Learning Coefficient

CREIMBO: Cross-Regional Ensemble Interactions in Multi-view Brain Observations

PFDiff: Training-Free Acceleration of Diffusion Models Combining Past and Future Scores

MMD-Regularized Unbalanced Optimal Transport

Improving Convergence Guarantees of Random Subspace Second-order Algorithm for Nonconvex Optimization

Microcanonical Langevin Ensembles: Advancing the Sampling of Bayesian Neural Networks

Rethinking Graph Neural Networks From A Geometric Perspective Of Node Features

Learning the Complexity of Weakly Noisy Quantum States

Budgeted Online Continual Learning by Adaptive Layer Freezing and Frequency-based Sampling

Spurious Forgetting in Continual Learning of Language Models

Learning Continually by Spectral Regularization

LVSM: A Large View Synthesis Model with Minimal 3D Inductive Bias

A-Bench: Are LMMs Masters at Evaluating AI-generated Images?

Training Large Language Models for Retrieval-Augmented Question Answering through Backtracking Correction

Learning to Help in Multi-Class Settings

Open-Set Graph Anomaly Detection via Normal Structure Regularisation

Scrutinize What We Ignore: Reining In Task Representation Shift Of Context-Based Offline Meta Reinforcement Learning

Towards Automated Knowledge Integration From Human-Interpretable Representations

Active Task Disambiguation with LLMs

Clique Number Estimation via Differentiable Functions of Adjacency Matrix Permutations

The impact of allocation strategies in subset learning on the expressive power of neural networks

RelitLRM: Generative Relightable Radiance for Large Reconstruction Models

Model-Agnostic Knowledge Guided Correction for Improved Neural Surrogate Rollout

IFORMER: INTEGRATING CONVNET AND TRANSFORMER FOR MOBILE APPLICATION

On the Linear Speedup of Personalized Federated Reinforcement Learning with Shared Representations

Lambda-Skip Connections: the architectural component that prevents Rank Collapse

Sail into the Headwind: Alignment via Robust Rewards and Dynamic Labels against Reward Hacking

Is uniform expressivity too restrictive? Towards efficient expressivity of GNNs

Universal Image Restoration Pre-training via Degradation Classification

SG-I2V: Self-Guided Trajectory Control in Image-to-Video Generation

T2V2: A Unified Non-Autoregressive Model for Speech Recognition and Synthesis via Multitask Learning

GPS: A Probabilistic Distributional Similarity with Gumbel Priors for Set-to-Set Matching

Joint Gradient Balancing for Data Ordering in Finite-Sum Multi-Objective Optimization

Curriculum-aware Training for Discriminating Molecular Property Prediction Models

CityGaussianV2: Efficient and Geometrically Accurate Reconstruction for Large-Scale Scenes

A Spark of Vision-Language Intelligence: 2-Dimensional Autoregressive Transformer for Efficient Finegrained Image Generation

The Computational Complexity of Positive Non-Clashing Teaching in Graphs

NExT-Mol: 3D Diffusion Meets 1D Language Modeling for 3D Molecule Generation

X-Gen: Ego-centric Video Prediction by Watching Exo-centric Videos

Articulate-Anything: Automatic Modeling of Articulated Objects via a Vision-Language Foundation Model

Designing Concise ConvNets with Columnar Stages

Flow With What You Know

SysBench: Can LLMs Follow System Message?

Text4Seg: Reimagining Image Segmentation as Text Generation

Understanding Constraint Inference in Safety-Critical Inverse Reinforcement Learning

MTSAM: Multi-Task Fine-Tuning for Segment Anything Model

BoneMet: An Open Large-Scale Multi-Modal Murine Dataset for Breast Cancer Bone Metastasis Diagnosis and Prognosis

Multilevel Generative Samplers for Investigating Critical Phenomena

HeadMap: Locating and Enhancing Knowledge Circuits in LLMs

Specialized Foundation Models Struggle to Beat Supervised Baselines

Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding

Tool-Planner: Task Planning with Clusters across Multiple Tools

Do Vision-Language Models Represent Space and How? Evaluating Spatial Frame of Reference under Ambiguities

Dynamic Mixture of Experts: An Auto-Tuning Approach for Efficient Transformer Models

Efficient Low-Bit Quantization with Adaptive Scales for Multi-Task Co-Training

LR0.FM: LOW-RESOLUTION ZERO-SHOT CLASSIFICATION BENCHMARK FOR FOUNDATION MODELS

Long-tailed Adversarial Training with Self-Distillation

Certifying Language Model Robustness with Fuzzed Randomized Smoothing: An Efficient Defense Against Backdoor Attacks

CAKE: Cascading and Adaptive KV Cache Eviction with Layer Preferences

Indirect Gradient Matching for Adversarial Robust Distillation

Decision Information Meets Large Language Models: The Future of Explainable Operations Research

PharmacoMatch: Efficient 3D Pharmacophore Screening via Neural Subgraph Matching

RevisEval: Improving LLM-as-a-Judge via Response-Adapted References

Quantized Spike-driven Transformer

3D-MolT5: Leveraging Discrete Structural Information for Molecule-Text Modeling

Erasing Concept Combination from Text-to-Image Diffusion Model

Deep Signature: Characterization of Large-Scale Molecular Dynamics

Local-Prompt: Extensible Local Prompts for Few-Shot Out-of-Distribution Detection

Matérn Kernels for Tunable Implicit Surface Reconstruction

PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative Models

Improved Sampling Algorithms for Lévy-Itô Diffusion Models

Instance-dependent Early Stopping

From GNNs to Trees: Multi-Granular Interpretability for Graph Neural Networks

GrabS: Generative Embodied Agent for 3D Object Segmentation without Scene Supervision

Biologically Plausible Brain Graph Transformer

Multi-Resolution Decomposable Diffusion Model for Non-Stationary Time Series Anomaly Detection

YOLO-RD: Introducing Relevant and Compact Explicit Knowledge to YOLO by Retriever-Dictionary

Medium-Difficulty Samples Constitute Smoothed Decision Boundary for Knowledge Distillation on Pruned Datasets

UniCon: Unidirectional Information Flow for Effective Control of Large-Scale Diffusion Models

Edge Prompt Tuning for Graph Neural Networks

One for all and all for one: Efficient computation of partial Wasserstein distances on the line

DriveTransformer: Unified Transformer for Scalable End-to-End Autonomous Driving

CL-DiffPhyCon: Closed-loop Diffusion Control of Complex Physical Systems

Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows

PolyhedronNet: Representation Learning for Polyhedra with Surface-attributed Graph

A Visual Dive into Conditional Flow Matching

PnP-Flow: Plug-and-Play Image Restoration with Flow Matching

Sparse components distinguish visual pathways & their alignment to neural networks

Expand and Compress: Exploring Tuning Principles for Continual Spatio-Temporal Graph Forecasting

FasterCache: Training-Free Video Diffusion Model Acceleration with High Quality

Hidden in the Noise: Two-Stage Robust Watermarking for Images

Truncated Consistency Models

PETRA: Parallel End-to-end Training with Reversible Architectures

Generative Representational Instruction Tuning

RegMix: Data Mixture as Regression for Language Model Pre-training

$InterLCM$: Low-Quality Images as Intermediate States of Latent Consistency Models for Effective Blind Face Restoration

One-Prompt-One-Story: Free-Lunch Consistent Text-to-Image Generation Using a Single Prompt

Proteina: Scaling Flow-based Protein Structure Generative Models

Scaling Laws for Precision

Correlated Proxies: A New Definition and Improved Mitigation for Reward Hacking

Mitigating Object Hallucination in MLLMs via Data-augmented Phrase-level Alignment

SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains?

Mitigating Information Loss in Tree-Based Reinforcement Learning via Direct Optimization

OpenHands: An Open Platform for AI Software Developers as Generalist Agents

GETS: Ensemble Temperature Scaling for Calibration in Graph Neural Networks

Graph Sparsification via Mixture of Graphs

LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression

Language models scale reliably with over-training and on downstream tasks

3D-Properties: Identifying Challenges in DPO and Charting a Path Forward

Matrix Product Sketching via Coordinated Sampling

On the Convergence of No-Regret Dynamics in Information Retrieval Games with Proportional Ranking Functions

Learning Geometric Reasoning Networks For Robot Task And Motion Planning

Outlier Synthesis via Hamiltonian Monte Carlo for Out-of-Distribution Detection

DiffPuter: An EM-Driven Diffusion Model for Missing Data Imputation

FedTMOS: Efficient One-Shot Federated Learning with Tsetlin Machine

Accelerating neural network training: An analysis of the AlgoPerf competition

HQGS: High-Quality Novel View Synthesis with Gaussian Splatting in Degraded Scenes

Ready-to-React: Online Reaction Policy for Two-Character Interaction Generation

TabDiff: a Mixed-type Diffusion Model for Tabular Data Generation

Benchmarking Predictive Coding Networks -- Made Simple

$\phi$-Update: A Class of Policy Update Methods with Policy Convergence Guarantee

Masked Temporal Interpolation Diffusion for Procedure Planning in Instructional Videos

Dynamic Multimodal Evaluation with Flexible Complexity by Vision-Language Bootstrapping

Diff3DS: Generating View-Consistent 3D Sketch via Differentiable Curve Rendering

InstantSplamp: Fast and Generalizable Stenography Framework for Generative Gaussian Splatting

MUSE: Machine Unlearning Six-Way Evaluation for Language Models

A Benchmark for Semantic Sensitive Information in LLMs Outputs

An Engorgio Prompt Makes Large Language Model Babble on

Revisit Micro-batch Clipping: Adaptive Data Pruning via Gradient Manipulation

The Labyrinth of Links: Navigating the Associative Maze of Multi-modal LLMs

Bias Mitigation in Graph Diffusion Models

Efficient Inference for Large Language Model-based Generative Recommendation

Isometric Regularization for Manifolds of Functional Data

High-dimension Prototype is a Better Incremental Object Detection Learner

Towards Understanding Text Hallucination of Diffusion Models via Local Generation Bias

JPEG Inspired Deep Learning

Efficiently Democratizing Medical LLMs for 50 Languages via a Mixture of Language Family Experts

Prevalence of Negative Transfer in Continual Reinforcement Learning: Analyses and a Simple Baseline

Attributing Culture-Conditioned Generations to Pretraining Corpora

Automatic Curriculum Expert Iteration for Reliable LLM Reasoning

Going Beyond Feature Similarity: Effective Dataset distillation based on Class-aware Conditional Mutual Information

Logical Consistency of Large Language Models in Fact-Checking

Conditional Diffusion Models are Minimax-Optimal and Manifold-Adaptive for Conditional Distribution Estimation

The Lottery LLM Hypothesis, Rethinking What Abilities Should LLM Compression Preserve?

Multimodality Helps Few-shot 3D Point Cloud Semantic Segmentation

Distribution-Specific Agnostic Conditional Classification With Halfspaces

Diffusion Feedback Helps CLIP See Better

Unsupervised Model Tree Heritage Recovery

Severing Spurious Correlations with Data Pruning

KBLaM: Knowledge Base augmented Language Model

Understanding Virtual Nodes: Oversquashing and Node Heterogeneity

Explaining Modern Gated-Linear RNNs via a Unified Implicit Attention Formulation

EditRoom: LLM-parameterized Graph Diffusion for Composable 3D Room Layout Editing

BlueSuffix: Reinforced Blue Teaming for Vision-Language Models Against Jailbreak Attacks

Agent Skill Acquisition for Large Language Models via CycleQD

MEGA-Bench: Scaling Multimodal Evaluation to over 500 Real-World Tasks

MMDisCo: Multi-Modal Discriminator-Guided Cooperative Diffusion for Joint Audio and Video Generation

Bundle Neural Network for message diffusion on graphs

Understanding Model Calibration - A gentle introduction and visual exploration of calibration and the expected calibration error (ECE)

Concept Pinpoint Eraser for Text-to-image Diffusion Models via Residual Attention Gate

K-HALU: Multiple Answer Korean Hallucination Benchmark for Large Language Models

PerturboLLaVA: Reducing Multimodal Hallucinations with Perturbative Visual Training

Stochastic Bandits Robust to Adversarial Attacks

Understanding and Enhancing the Transferability of Jailbreaking Attacks

ARB-LLM: Alternating Refined Binarizations for Large Language Models

GOLD: Graph Out-of-Distribution Detection via Implicit Adversarial Latent Generation

Training-free LLM-generated Text Detection by Mining Token Probability Sequences

When Attention Sink Emerges in Language Models: An Empirical View

Efficient Model Editing with Task-Localized Sparse Fine-tuning

Demystifying Online Clustering of Bandits: Enhanced Exploration Under Stochastic and Smoothed Adversarial Contexts

DiffSplat: Repurposing Image Diffusion Models for Scalable Gaussian Splat Generation

OmniPhysGS: 3D Constitutive Gaussians for General Physics-Based Dynamics Generation

GenDataAgent: On-the-fly Dataset Augmentation with Synthetic Data

InstantSwap: Fast Customized Concept Swapping across Sharp Shape Differences

To Code or Not To Code? Exploring Impact of Code in Pre-training

Looking Backward: Retrospective Backward Synthesis for Goal-Conditioned GFlowNets

Neuroplastic Expansion in Deep Reinforcement Learning

Adversarial Search Engine Optimization for Large Language Models

Towards Multiple Character Image Animation Through Enhancing Implicit Decoupling

PPT: Patch Order Do Matters In Time Series Pretext Task

RocketEval: Efficient automated LLM evaluation via grading checklist

DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search

GravMAD: Grounded Spatial Value Maps Guided Action Diffusion for Generalized 3D Manipulation

From Exploration to Mastery: Enabling LLMs to Master Tools via Self-Driven Interactions

Improving Language Model Distillation through Hidden State Matching

MGMapNet: Multi-Granularity Representation Learning for End-to-End Vectorized HD Map Construction

DUET: Decentralized Bilevel Optimization without Lower-Level Strong Convexity

Bandit Learning in Matching Markets with Indifference

Structuring Benchmark into Knowledge Graphs to Assist Large Language Models in Retrieving and Designing Models

Predicting the Energy Landscape of Stochastic Dynamical System via Physics-informed Self-supervised Learning

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods

Asymptotic Analysis of Two-Layer Neural Networks after One Gradient Step under Gaussian Mixtures Data with Structure

OSTQuant: Refining Large Language Model Quantization with Orthogonal and Scaling Transformations for Better Distribution Fitting

SIMPL: Scalable and hassle-free optimisation of neural representations from behaviour

MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers

Mitigating Parameter Interference in Model Merging via Sharpness-Aware Fine-Tuning

GLoRa: A Benchmark to Evaluate the Ability to Learn Long-Range Dependencies in Graphs

MQuAKE-Remastered: Multi-Hop Knowledge Editing Can Only Be Advanced with Reliable Evaluations

Bayesian WeakS-to-Strong from Text Classification to Generation

Sparse Autoencoders Do Not Find Canonical Units of Analysis

StochSync: Stochastic Diffusion Synchronization for Image Generation in Arbitrary Spaces

Neural Phylogeny: Fine-Tuning Relationship Detection among Neural Networks

HG-Adapter: Improving Pre-Trained Heterogeneous Graph Neural Networks with Dual Adapters

DiffusionGuard: A Robust Defense Against Malicious Diffusion-based Image Editing

ImagineNav: Prompting Vision-Language Models as Embodied Navigator through Scene Imagination

IDInit: A Universal and Stable Initialization Method for Neural Network Training

Optimizing Backward Policies in GFlowNets via Trajectory Likelihood Maximization

Connecting Federated ADMM to Bayes

Efficient Masked AutoEncoder for Video Object Counting and A Large-Scale Benchmark

Vevo: Controllable Zero-Shot Voice Imitation with Self-Supervised Disentanglement

Learning multi-modal generative models with permutation-invariant encoders and tighter variational objectives

Flash Inference: Near Linear Time Inference for Long Convolution Sequence Models and Beyond

Generating Physical Dynamics under Priors

Deconstructing Denoising Diffusion Models for Self-Supervised Learning

Energy-Based Diffusion Language Models for Text Generation

SuperCorrect: Advancing Small LLM Reasoning with Thought Template Distillation and Self-Correction

CircuitFusion: Multimodal Circuit Representation Learning for Agile Chip Design

Prompting Fairness: Integrating Causality to Debias Large Language Models

Learning Evolving Tools for Large Language Models

Learning Graph Invariance by Harnessing Spuriosity

On Scaling Up 3D Gaussian Splatting Training

Probing the Latent Hierarchical Structure of Data via Diffusion Models

WorkflowLLM: Enhancing Workflow Orchestration Capability of Large Language Models

GeSubNet: Gene Interaction Inference for Disease Subtype Network Generation

Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistance

FormalAlign: Automated Alignment Evaluation for Autoformalization

How Does Critical Batch Size Scale in Pre-training?

Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence

Deconstructing What Makes a Good Optimizer for Autoregressive Language Models

How Low Can You Go? Searching for the Intrinsic Dimensionality of Complex Networks using Metric Node Embeddings

HD-Painter: High-Resolution and Prompt-Faithful Text-Guided Image Inpainting with Diffusion Models

Glauber Generative Model: Discrete Diffusion Models via Binary Classification

LiFT: Learning to Fine-Tune via Bayesian Parameter Efficient Meta Fine-Tuning

Causal Graph Transformer for Treatment Effect Estimation Under Unknown Interference

MIA-Bench: Towards Better Instruction Following Evaluation of Multimodal LLMs

On Generalization Across Environments In Multi-Objective Reinforcement Learning

AuroraCap: Efficient, Performant Video Detailed Captioning and a New Benchmark

On the Identification of Temporal Causal Representation with Instantaneous Dependence

Enhancing Document Understanding with Group Position Embedding: A Novel Approach to Incorporate Layout Information

Bridging and Modeling Correlations in Pairwise Data for Direct Preference Optimization

VideoShield: Regulating Diffusion-based Video Generation Models via Watermarking

Language Models Are Implicitly Continuous

When Selection Meets Intervention: Additional Complexities in Causal Discovery

Synergy Between Sufficient Changes and Sparse Mixing Procedure for Disentangled Representation Learning

GenXD: Generating Any 3D and 4D Scenes

MAI: A Multi-turn Aggregation-Iteration Model for Composed Image Retrieval

Operator Deep Smoothing for Implied Volatility

Exploiting Distribution Constraints for Scalable and Efficient Image Retrieval

Does Training with Synthetic Data Truly Protect Privacy?

GSE: Group-wise Sparse and Explainable Adversarial Attacks

CSA: Data-efficient Mapping of Unimodal Features to Multimodal Features

Revisit the Open Nature of Open Vocabulary Semantic Segmentation

ConFIG: Towards Conflict-free Training of Physics Informed Neural Networks

Federated $Q$-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost

Modeling dynamic social vision highlights gaps between deep learning and humans

Exploring Local Memorization in Diffusion Models via Bright Ending Attention

Gap-Dependent Bounds for Q-Learning using Reference-Advantage Decomposition

Fast and Accurate Blind Flexible Docking

ALBAR: Adversarial Learning approach to mitigate Biases in Action Recognition

Analyzing and Boosting the Power of Fine-Grained Visual Recognition for Multi-modal Large Language Models

How to visualize training dynamics in neural networks

LiNeS: Post-training Layer Scaling Prevents Forgetting and Enhances Model Merging

AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulation

Intent3D: 3D Object Detection in RGB-D Scans Based on Human Intention

Reinforcement Learning from Imperfect Corrective Actions and Proxy Rewards

RecFlow: An Industrial Full Flow Recommendation Dataset

Making Transformer Decoders Better Differentiable Indexers

Deep Kernel Relative Test for Machine-generated Text Detection

UniCBE: An Uniformity-driven Comparing Based Evaluation Framework with Unified Multi-Objective Optimization

UniCoTT: A Unified Framework for Structural Chain-of-Thought Distillation

Identification of Intermittent Temporal Latent Process

Decentralized Optimization with Coupled Constraints

Teaching Human Behavior Improves Content Understanding Abilities Of VLMs

ST-GCond: Self-supervised and Transferable Graph Dataset Condensation

Measuring And Improving Persuasiveness Of Large Language Models

Ask, and it shall be given: On the Turing completeness of prompting

Optimizing Posterior Samples for Bayesian Optimization via Rootfinding

Few for Many: Tchebycheff Set Scalarization for Many-Objective Optimization

Conditional Testing based on Localized Conformal $p$-values

Learning to Generate Diverse Pedestrian Movements from Web Videos with Noisy Labels

SIM: Surface-based fMRI Analysis for Inter-Subject Multimodal Decoding from Movie-Watching Experiments

Offline Model-Based Optimization by Learning to Rank

Boosting Neural Combinatorial Optimization for Large-Scale Vehicle Routing Problems

Measuring And Improving Engagement of Text-to-Image Generation Models

Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refuse

How efficient is LLM-generated code? A rigorous & high-standard benchmark

Alchemy: Amplifying Theorem-Proving Capability Through Symbolic Mutation

SSLAM: Enhancing Self-Supervised Models with Audio Mixtures for Polyphonic Soundscapes

Generalizing Weisfeiler-Lehman Kernels to Subgraphs

PWM: Policy Learning with Multi-Task World Models

Few-Class Arena: A Benchmark for Efficient Selection of Vision Models and Dataset Difficulty Measurement

Where Am I and What Will I See: An Auto-Regressive Model for Spatial Localization and View Prediction

See What You Are Told: Visual Attention Sink in Large Multimodal Models

Feedback Favors the Generalization of Neural ODEs

Towards General-Purpose Model-Free Reinforcement Learning

Interaction Asymmetry: A General Principle for Learning Composable Abstractions

Investigating the Pre-Training Dynamics of In-Context Learning: Task Recognition vs. Task Learning

KinPFN: Bayesian Approximation of RNA Folding Kinetics using Prior-Data Fitted Networks

Beyond Sequence: Impact of Geometric Context for RNA Property Prediction

Relaxed Recursive Transformers: Effective Parameter Sharing with Layer-wise LoRA

Reading Your Heart: Learning ECG Words and Sentences via Pre-training ECG Language Model

What Makes a Maze Look Like a Maze?

Generative Monoculture in Large Language Models

Stabilizing Reinforcement Learning in Differentiable Multiphysics Simulation

Beyond Canonicalization: How Tensorial Messages Improve Equivariant Message Passing

Long-Sequence Recommendation Models Need Decoupled Embeddings

HELM: Hierarchical Encoding for mRNA Language Modeling

Test of Time: A Benchmark for Evaluating LLMs on Temporal Reasoning

Learning from weak labelers as constraints

REFINE: Inversion-Free Backdoor Defense via Model Reprogramming

Cybench: A Framework for Evaluating Cybersecurity Capabilities and Risks of Language Models

Selective Unlearning via Representation Erasure Using Domain Adversarial Training

Democratic Training Against Universal Adversarial Perturbations

Partial Gromov-Wasserstein Metric

MIRAGE: Evaluating and Explaining Inductive Reasoning Process in Language Models

Pre-training of Foundation Adapters for LLM Fine-tuning

$\mathbb{X}$-Sample Contrastive Loss: Improving Contrastive Learning with Sample Similarity Graphs

Proxy Denoising for Source-Free Domain Adaptation

Shape as Line Segments: Accurate and Flexible Implicit Surface Representation

DoF: A Diffusion Factorization Framework for Offline Multi-Agent Reinforcement Learning

CAMEx: Curvature-aware Merging of Experts

Not All LLM-Generated Data Are Equal: Rethinking Data Weighting in Text Classification

FedLWS: Federated Learning with Adaptive Layer-wise Weight Shrinking

DRL: Decomposed Representation Learning for Tabular Anomaly Detection

Hierarchical World Models as Visual Whole-Body Humanoid Controllers

Linear Partial Gromov-Wasserstein Embedding

Population Transformer: Learning Population-level Representations of Neural Activity

COME: Test-time Adaption by Conservatively Minimizing Entropy

AtomSurf: Surface Representation for Learning on Protein Structures

NRGBoost: Energy-Based Generative Boosted Trees

Linear Spherical Sliced Optimal Transport: A Fast Metric for Comparing Spherical Data

Re-Evaluating the Impact of Unseen-Class Unlabeled Data on Semi-Supervised Learning Model

On the Price of Differential Privacy for Hierarchical Clustering

FreCaS: Efficient Higher-Resolution Image Generation via Frequency-aware Cascaded Sampling

Expected Sliced Transport Plans

Contextualizing biological perturbation experiments through language

OpenPRM: Building Open-domain Process-based Reward Models with Preference Trees

Diffusion Policy Policy Optimization

Understanding Matrix Function Normalizations in Covariance Pooling through the Lens of Riemannian Geometry

Neural Dueling Bandits: Preference-Based Optimization with Human Feedback

Multi-Perspective Data Augmentation for Few-shot Object Detection

Lumina-T2X: Scalable Flow-based Large Diffusion Transformer for Flexible Resolution Generation

DICE: Data Influence Cascade in Decentralized Learning

PixWizard: Versatile Image-to-Image Visual Assistant with Open-Language Instructions

LLaVA-MoD: Making LLaVA Tiny via MoE-Knowledge Distillation

Deep Linear Probe Generators for Weight Space Learning

Offline Hierarchical Reinforcement Learning via Inverse Optimization

LancBiO: Dynamic Lanczos-aided Bilevel Optimization via Krylov Subspace

Solving Differential Equations with Constrained Learning

PhyMPGN: Physics-encoded Message Passing Graph Network for spatiotemporal PDE systems

AgentSquare: Automatic LLM Agent Search in Modular Design Space

On the Performance Analysis of Momentum Method: A Frequency Domain Perspective

QMP: Q-switch Mixture of Policies for Multi-Task Behavior Sharing

HR-Extreme: A High-Resolution Dataset for Extreme Weather Forecasting

Wavelet Diffusion Neural Operator

ADIFF: Explaining audio difference using natural language

SCBench: A KV Cache-Centric Analysis of Long-Context Methods

SeCom: On Memory Construction and Retrieval for Personalized Conversational Agents

Humanizing the Machine: Proxy Attacks to Mislead LLM Detectors

KooNPro: A Variance-Aware Koopman Probabilistic Model Enhanced by Neural Process for Time Series Forecasting

ConvCodeWorld: Benchmarking Conversational Code Generation in Reproducible Feedback Environments

NarrativeBridge: Enhancing Video Captioning with Causal-Temporal Narrative

From an LLM Swarm to a PDDL-empowered Hive: Planning Self-executed Instructions in a Multi-modal Jungle

Towards Understanding the Robustness of Diffusion-Based Purification: A Stochastic Perspective

Diff-PIC: Revolutionizing Particle-In-Cell Nuclear Fusion Simulation with Diffusion Models

Youku Dense Caption: A Large-scale Chinese Video Dense Caption Dataset and Benchmarks

InstaTrain: Adaptive Training via Ultra-Fast Natural Annealing within Dynamical Systems

TS-LIF: A Temporal Segment Spiking Neuron Network for Time Series Forecasting

CREAM: Consistency Regularized Self-Rewarding Language Models

DS-LLM: Leveraging Dynamical Systems to Enhance Both Training and Inference of Large Language Models

Latent Space Chain-of-Embedding Enables Output-free LLM Self-Evaluation

Towards Robust Alignment of Language Models: Distributionally Robustifying Direct Preference Optimization

Find A Winning Sign: Sign Is All We Need to Win the Lottery

ASTrA: Adversarial Self-supervised Training with Adaptive-Attacks

SPAM: Spike-Aware Adam with Momentum Reset for Stable LLM Training

AnalogGenie: A Generative Engine for Automatic Discovery of Analog Circuit Topologies

Optimal Brain Apoptosis

GEVRM: Goal-Expressive Video Generation Model For Robust Visual Manipulation

Large Language Models are Interpretable Learners

Influence-Guided Diffusion for Dataset Distillation

Mitigating Modality Prior-Induced Hallucinations in Multimodal Large Language Models via Deciphering Attention Causality

LoRA Done RITE: Robust Invariant Transformation Equilibration for LoRA Optimization

Self-Introspective Decoding: Alleviating Hallucinations for Large Vision-Language Models

INCLUDE: Evaluating Multilingual Language Understanding with Regional Knowledge

DynamicCity: Large-Scale 4D Occupancy Generation from Dynamic Scenes

Certified Robustness Under Bounded Levenshtein Distance

Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems

Learning a Neural Solver for Parametric PDEs to Enhance Physics-Informed Methods

Reflective Gaussian Splatting

PostCast: Generalizable Postprocessing for Precipitation Nowcasting via Unsupervised Blurriness Modeling

UV-Attack: Physical-World Adversarial Attacks on Person Detection via Dynamic-NeRF-based UV Mapping

FlickerFusion: Intra-trajectory Domain Generalizing Multi-agent Reinforcement Learning

Periodic Materials Generation using Text-Guided Joint Diffusion Model

Divergence-enhanced Knowledge-guided Context Optimization for Visual-Language Prompt Tuning

Neural networks on Symmetric Spaces of Noncompact Type

OvercookedV2: Rethinking Overcooked for Zero-Shot Coordination

JudgeLM: Fine-tuned Large Language Models are Scalable Judges

Explanations of GNN on Evolving Graphs via Axiomatic Layer edges

Competing Large Language Models in Multi-Agent Gaming Environments

Direct Distributional Optimization for Provable Alignment of Diffusion Models

MuPT: A Generative Symbolic Music Pretrained Transformer

TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters

Unifying Causal Representation Learning with the Invariance Principle

Repurposing in AI: A Distinct Approach or an Extension of Creative Problem Solving?

SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints

Wasserstein-Regularized Conformal Prediction under General Distribution Shift

MathGAP: Out-of-Distribution Evaluation on Problems with Arbitrarily Complex Proofs

BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions

Three Mechanisms of Feature Learning in a Linear Network

Generalization Guarantees for Representation Learning via Data-Dependent Gaussian Mixture Priors

Remove Symmetries to Control Model Expressivity and Improve Optimization

SaRA: High-Efficient Diffusion Model Fine-tuning with Progressive Sparse Low-Rank Adaptation

Robust Barycenter Estimation using Semi-Unbalanced Neural Optimal Transport

Improving Neural Optimal Transport via Displacement Interpolation

Revisiting Zeroth-Order Optimization: Minimum-Variance Two-Point Estimators and Directionally Aligned Perturbations

GNNs Getting ComFy: Community and Feature Similarity Guided Rewiring

Structural-Entropy-Based Sample Selection for Efficient and Effective Learning

Generating Likely Counterfactuals Using Sum-Product Networks

Natural Language Inference Improves Compositionality in Vision-Language Models

PIN: Prolate Spheroidal Wave Function-based Implicit Neural Representations

Homomorphism Expressivity of Spectral Invariant Graph Neural Networks

Emergent Orientation Maps —— Mechanisms, Coding Efficiency and Robustness

R2Det: Exploring Relaxed Rotation Equivariance in 2D Object Detection

Topological Blindspots: Understanding and Extending Topological Deep Learning Through the Lens of Expressivity

On Minimizing Adversarial Counterfactual Error in Adversarial Reinforcement Learning

Frame-Voyager: Learning to Query Frames for Video Large Language Models

Hybrid Regularization Improves Diffusion-based Inverse Problem Solving

Tracking the Copyright of Large Vision-Language Models through Parameter Learning Adversarial Images

Let Your Features Tell The Differences: Understanding Graph Convolution By Feature Splitting

MELODI: Exploring Memory Compression for Long Contexts

Provable Benefit of Annealed Langevin Monte Carlo for Non-log-concave Sampling

Diffusion Generative Modeling for Spatially Resolved Gene Expression Inference from Histology Images

Trivialized Momentum Facilitates Diffusion Generative Modeling on Lie Groups

SC-OmniGS: Self-Calibrating Omnidirectional Gaussian Splatting

Omni-MATH: A Universal Olympiad Level Mathematic Benchmark for Large Language Models

Efficient Residual Learning with Mixture-of-Experts for Universal Dexterous Grasping

A Meta-Learning Approach to Bayesian Causal Discovery

Forgetting Transformer: Softmax Attention with a Forget Gate

UniCO: On Unified Combinatorial Optimization via Problem Reduction to Matrix-Encoded General TSP

Task-Adaptive Pretrained Language Models via Clustered-Importance Sampling

DistRL: An Asynchronous Distributed Reinforcement Learning Framework for On-Device Control Agent

EcoFace: Audio-Visual Emotional Co-Disentanglement Speech-Driven 3D Talking Face Generation

Value-aligned Behavior Cloning for Offline Reinforcement Learning via Bi-level Optimization

MAST: model-agnostic sparsified training

Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization

A Solvable Attention for Neural Scaling Laws

DyCAST: Learning Dynamic Causal Structure from Time Series

Breaking Class Barriers: Efficient Dataset Distillation via Inter-Class Feature Compensator

SPA-BENCH: A COMPREHENSIVE BENCHMARK FOR SMARTPHONE AGENT EVALUATION

Rethinking Multiple-Instance Learning From Feature Space to Probability Space

Strength Estimation and Human-Like Strength Adjustment in Games

Test-time Adaptation for Regression by Subspace Alignment

Following the Human Thread in Social Navigation

FlexCAD: Unified and Versatile Controllable CAD Generation with Fine-tuned Large Language Models

Iterative Substructure Extraction for Molecular Relational Learning with Interactive Graph Information Bottleneck

Deep Random Features for Scalable Interpolation of Spatiotemporal Data

Can Generative AI Solve Your In-Context Learning Problem? A Martingale Perspective

Tight Time Complexities in Parallel Stochastic Optimization with Arbitrary Computation Dynamics

Improving Complex Reasoning with Dynamic Prompt Corruption: A Soft Prompt Optimization Approach

ClimaQA: An Automated Evaluation Framework for Climate Question Answering Models

Debiasing Federated Learning with Correlated Client Participation

MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering

Predictive Uncertainty Quantification for Bird's Eye View Segmentation: A Benchmark and Novel Loss Function

Dynamic Contrastive Skill Learning with State-Transition Based Skill Clustering and Dynamic Length Adjustment

Tailoring Mixup to Data for Calibration

DreamBench++: A Human-Aligned Benchmark for Personalized Image Generation

VoxDialogue: Can Spoken Dialogue Systems Understand Information Beyond Words?

CAX: Cellular Automata Accelerated in JAX

OmniSep: Unified Omni-Modality Sound Separation with Query-Mixup

OMNI-EPIC: Open-endedness via Models of human Notions of Interestingness with Environments Programmed in Code

OmniBind: Large-scale Omni Multimodal Representation via Binding Spaces

Accelerating Inference of Retrieval-Augmented Generation via Sparse Context Selection

SqueezeAttention: 2D Management of KV-Cache in LLM Inference via Layer-wise Optimal Budget

Decoupled Finetuning for Domain Generalizable Semantic Segmentation

Boundary constrained Gaussian processes for robust physics-informed machine learning of linear partial differential equations

Beyond single neurons: population response geometry in digital twins of mouse visual cortex

Towards Hierarchical Rectified Flow

Online Clustering with Nearly Optimal Consistency

ODE-based Smoothing Neural Network for Reinforcement Learning Tasks

ForecastBench: A Dynamic Benchmark of AI Forecasting Capabilities

Regret-Optimal List Replicable Bandit Learning: Matching Upper and Lower Bounds

Factual Context Validation and Simplification: A Scalable Method to Enhance GPT Trustworthiness and Efficiency

Learning local equivariant representations for quantum operators

Computational Explorations of Total Variation Distance

PEARL: Parallel Speculative Decoding with Adaptive Draft Length

GOttack: Universal Adversarial Attacks on Graph Neural Networks via Graph Orbits Learning

Improving Text-to-Image Consistency via Automatic Prompt Optimization

Hydra-SGG: Hybrid Relation Assignment for One-stage Scene Graph Generation

Do as We Do, Not as You Think: the Conformity of Large Language Models

Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds

BrainOOD: Out-of-distribution Generalizable Brain Network Analysis

Fast Feedforward 3D Gaussian Splatting Compression

Ada-K Routing: Boosting the Efficiency of MoE-based LLMs

Needle In A Video Haystack: A Scalable Synthetic Evaluator for Video MLLMs

CapeX: Category-Agnostic Pose Estimation from Textual Point Explanation

Lightning-Fast Image Inversion and Editing for Text-to-Image Diffusion Models

Interpretable Causal Representation Learning for Biological Data in the Pathway Space

WardropNet: Traffic Flow Predictions via Equilibrium-Augmented Learning

REEF: Representation Encoding Fingerprints for Large Language Models

Dynamic Low-Rank Sparse Adaptation for Large Language Models

Knowledge Graph Finetuning Enhances Knowledge Manipulation in Large Language Models

Learning Robust Representations with Long-Term Information for Generalization in Visual Reinforcement Learning

LLaRA: Supercharging Robot Learning Data for Vision-Language Policy

Denoising Task Difficulty-based Curriculum for Training Diffusion Models

Quality Measures for Dynamic Graph Generative Models

Injective flows for star-like manifolds

A Second-Order Perspective on Model Compositionality and Incremental Learning

Towards Marginal Fairness Sliced Wasserstein Barycenter

X-Drive: Cross-modality Consistent Multi-Sensor Data Synthesis for Driving Scenarios

Statistical Advantages of Perturbing Cosine Router in Mixture of Experts

How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehension

Learning to Communicate Through Implicit Communication Channels

Boltzmann priors for Implicit Transfer Operators

Knowledge Localization: Mission Not Accomplished? Enter Query Localization!

Enhancing Learning with Label Differential Privacy by Vector Approximation

Smoothing the Shift: Towards Stable Test-Time Adaptation under Complex Multimodal Noises

Revisiting Prefix-tuning: Statistical Benefits of Reparameterization among Prompts

Diff-Prompt: Diffusion-driven Prompt Generator with Mask Supervision

Improved Training Technique for Latent Consistency Models

Boosting Multiple Views for pretrained-based Continual Learning

A Theoretical Perspective: How to Prevent Model Collapse in Self-consuming Training Loops

Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning

Select before Act: Spatially Decoupled Action Repetition for Continuous Control

Implicit In-context Learning

Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson–Romberg Extrapolation

Merging LoRAs like Playing LEGO: Pushing the Modularity of LoRA to Extremes Through Rank-Wise Clustering

Do LLMs have Consistent Values?

Feast Your Eyes: Mixture-of-Resolution Adaptation for Multimodal Large Language Models

Scaling FP8 training to trillion-token LLMs

Learning Interleaved Image-Text Comprehension in Vision-Language Large Models

Faster Inference of Flow-Based Generative Models via Improved Data-Noise Coupling

Determine-Then-Ensemble: Necessity of Top-k Union for Large Language Model Ensembling

ParetoFlow: Guided Flows in Multi-Objective Optimization

AgentRefine: Enhancing Agent Generalization through Refinement Tuning

CS-Bench: A Comprehensive Benchmark for Large Language Models towards Computer Science Mastery

PIG: Physics-Informed Gaussians as Adaptive Parametric Mesh Representations

NoVo: Norm Voting off Hallucinations with Attention Heads in Large Language Models

A General Framework for Off-Policy Learning with Partially-Observed Reward

Multi-Task Corrupted Prediction for Learning Robust Audio-Visual Speech Representation

A3D: Does Diffusion Dream about 3D Alignment?

Can LLMs Separate Instructions From Data? And What Do We Even Mean By That?

Influence Functions for Scalable Data Attribution in Diffusion Models

RFWave: Multi-band Rectified Flow for Audio Waveform Reconstruction

OS-ATLAS: Foundation Action Model for Generalist GUI Agents

Implicit Search via Discrete Diffusion: A Study on Chess

Post-hoc Reward Calibration: A Case Study on Length Bias

Layerwise Recurrent Router for Mixture-of-Experts

Precise Localization of Memories: A Fine-grained Neuron-level Knowledge Editing Technique for LLMs

Turning Up the Heat: Min-p Sampling for Creative and Coherent LLM Outputs

Cafe-Talk: Generating 3D Talking Face Animation with Multimodal Coarse- and Fine-grained Control

BOFormer: Learning to Solve Multi-Objective Bayesian Optimization via Non-Markovian RL

Segment Any 3D Object with Language

Multi-Robot Motion Planning with Diffusion Models

VLAS: Vision-Language-Action Model with Speech Instructions for Customized Robot Manipulation

Sharpness-Aware Minimization Efficiently Selects Flatter Minima Late In Training

Restyling Unsupervised Concept Based Interpretable Networks with Generative Models

Towards Understanding Why FixMatch Generalizes Better Than Supervised Learning

Exploring the Camera Bias of Person Re-identification

End-to-end Learning of Gaussian Mixture Priors for Diffusion Sampler

Linear Bandits with Memory

Underdamped Diffusion Bridges with Applications to Sampling

Efficient Off-Policy Learning for High-Dimensional Action Spaces

Geometry-aware RL for Manipulation of Varying Shapes and Deformable Objects

Sequential Controlled Langevin Diffusions

Noisy Test-Time Adaptation in Vision-Language Models

TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement Learning

A Training-Free Sub-quadratic Cost Transformer Model Serving Framework with Hierarchically Pruned Attention

Efficient Distribution Matching of Representations via Noise-Injected Deep InfoMax

Collaborative Discrete-Continuous Black-Box Prompt Learning for Language Models

ELICIT: LLM Augmentation Via External In-context Capability

HOPE for a Robust Parameterization of Long-memory State Space Models

Dynamic-SUPERB Phase-2: A Collaboratively Expanding Benchmark for Measuring the Capabilities of Spoken Language Models with 180 Tasks

CarbonSense: A Multimodal Dataset and Baseline for Carbon Flux Modelling

Tuning Frequency Bias of State Space Models

LLM-wrapper: Black-Box Semantic-Aware Adaptation of Vision-Language Models for Referring Expression Comprehension

A Unified Framework for Forward and Inverse Problems in Subsurface Imaging using Latent Space Translations

Image Watermarks are Removable using Controllable Regeneration from Clean Noise

TopoNets: High performing vision and language models with brain-like topography

Temporal Heterogeneous Graph Generation with Privacy, Utility, and Efficiency

CLDyB: Towards Dynamic Benchmarking for Continual Learning with Pre-trained Models

MolSpectra: Pre-training 3D Molecular Representation with Multi-modal Energy Spectra

InversionGNN: A Dual Path Network for Multi-Property Molecular Optimization

CirT: Global Subseasonal-to-Seasonal Forecasting with Geometry-inspired Transformer

Atomas: Hierarchical Adaptive Alignment on Molecule-Text for Unified Molecule Understanding and Generation

SymmCD: Symmetry-Preserving Crystal Generation with Diffusion Models

SD-LoRA: Scalable Decoupled Low-Rank Adaptation for Class Incremental Learning

Optimality of Matrix Mechanism on $\ell_p^p$-metric

Learning on One Mode: Addressing Multi-modality in Offline Reinforcement Learning

GOPlan: Goal-conditioned Offline Reinforcement Learning by Planning with Learned Models

Gumbel Counterfactual Generation From Language Models

CONGO: Compressive Online Gradient Optimization

NUDGE: Lightweight Non-Parametric Fine-Tuning of Embeddings for Retrieval

Minimax Optimal Reinforcement Learning with Quasi-Optimism

Lasso Bandit with Compatibility Condition on Optimal Arm

Real-time design of architectural structures with differentiable mechanics and neural networks

Your Weak LLM is Secretly a Strong Teacher for Alignment

Dynamic-LLaVA: Efficient Multimodal Large Language Models via Dynamic Vision-language Context Sparsification

Neural Sampling from Boltzmann Densities: Fisher-Rao Curves in the Wasserstein Geometry

Slot-Guided Adaptation of Pre-trained Diffusion Models for Object-Centric Learning and Compositional Generation

Is Your Model Really A Good Math Reasoner? Evaluating Mathematical Reasoning with Checklist

Cut Your Losses in Large-Vocabulary Language Models

Joint Graph Rewiring and Feature Denoising via Spectral Resonance

TimeSuite: Improving MLLMs for Long Video Understanding via Grounded Tuning

Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models

COAT: Compressing Optimizer states and Activations for Memory-Efficient FP8 Training

Competitive Fair Scheduling with Predictions

SANA: Efficient High-Resolution Text-to-Image Synthesis with Linear Diffusion Transformers

HART: Efficient Visual Generation with Hybrid Autoregressive Transformer

Guaranteed Generation from Large Language Models

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving

Transformers Struggle to Learn to Search

Can We Trust Embodied Agents? Exploring Backdoor Attacks against Embodied LLM-Based Decision-Making Systems

Attention as a Hypernetwork

MiniPLM: Knowledge Distillation for Pre-training Language Models

Entropy-based Activation Function Optimization: A Method on Searching Better Activation Functions

Modality-Specialized Synergizers for Interleaved Vision-Language Generalists

SPARTUN3D: Situated Spatial Understanding of 3D World in Large Language Model

Poisson-Dirac Neural Networks for Modeling Coupled Dynamical Systems across Domains

VL-ICL Bench: The Devil in the Details of Multimodal In-Context Learning

Understanding Long Videos with Multimodal Language Models

Inner Information Analysis Algorithm for Deep Neural Network based on Community

Balancing Act: Diversity and Consistency in Large Language Model Ensembles

OmniEdit: Building Image Editing Generalist Models Through Specialist Supervision

GS-CPR: Efficient Camera Pose Refinement via 3D Gaussian Splatting

Think Then React: Towards Unconstrained Action-to-Reaction Motion Generation

LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

Mixture of Parrots: Experts improve memorization more than reasoning

Integrating Protein Dynamics into Structure-Based Drug Design via Full-Atom Stochastic Flows

Learning to Explore and Exploit with GNNs for Unsupervised Combinatorial Optimization

Zero-shot Imputation with Foundation Inference Models for Dynamical Systems

OLMoE: Open Mixture-of-Experts Language Models

What should a neuron aim for? Designing local objective functions based on information theory

High-Dynamic Radar Sequence Prediction for Weather Nowcasting Using Spatiotemporal Coherent Gaussian Representation

Fourier Sliced-Wasserstein Embedding for Multisets and Measures

SELF-EVOLVED REWARD LEARNING FOR LLMS

Training Language Models to Self-Correct via Reinforcement Learning

KOR-Bench: Benchmarking Language Models on Knowledge-Orthogonal Reasoning Tasks

MRS: A Fast Sampler for Mean Reverting Diffusion based on ODE and SDE Solvers

MaxCutPool: differentiable feature-aware Maxcut for pooling in graph neural networks

RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment

Continuous Diffusion for Mixed-Type Tabular Data

Large Convolutional Model Tuning via Filter Subspace

Does Refusal Training in LLMs Generalize to the Past Tense?

Is In-Context Learning Sufficient for Instruction Following in LLMs?

Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks

Do Deep Neural Network Solutions Form a Star Domain?

MMTEB: Massive Multilingual Text Embedding Benchmark

Bootstrapping Language-Guided Navigation Learning with Self-Refining Data Flywheel

Deep Learning Alternatives Of The Kolmogorov Superposition Theorem

Field-DiT: Diffusion Transformer on Unified Video, 3D, and Game Field Generation

Can Neural Networks Achieve Optimal Computational-statistical Tradeoff? An Analysis on Single-Index Model

A New Perspective on Shampoo's Preconditioner

SOAP: Improving and Stabilizing Shampoo using Adam for Language Modeling

Graph Assisted Offline-Online Deep Reinforcement Learning for Dynamic Workflow Scheduling

Improved Sampling Of Diffusion Models In Fluid Dynamics With Tweedie's Formula

Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning

IterGen: Iterative Semantic-aware Structured LLM Generation with Backtracking

Support is All You Need for Certified VAE Training

Certifying Counterfactual Bias in LLMs

NatureLM-audio: an Audio-Language Foundation Model for Bioacoustics

Towards Federated RLHF with Aggregated Client Preference for LLMs

VL-Cache: Sparsity and Modality-Aware KV Cache Compression for Vision-Language Model Inference Acceleration

Minimal Variance Model Aggregation: A principled, non-intrusive, and versatile integration of black box models

Towards Self-Supervised Covariance Estimation in Deep Heteroscedastic Regression

Temporal Difference Learning: Why It Can Be Fast and How It Will Be Faster

Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline Data

Towards Learning High-Precision Least Squares Algorithms with Sequence Models

Adaptive Rank Allocation: Speeding Up Modern Transformers with RaNA Adapters

Restructuring Vector Quantization with the Rotation Trick

Learning and aligning single-neuron invariance manifolds in visual cortex

Optimal Strong Regret and Violation in Constrained MDPs via Policy Optimization

Procedural Synthesis of Synthesizable Molecules

Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning

A deep inverse-mapping model for a flapping robotic wing

Exploring the Effectiveness of Object-Centric Representations in Visual Question Answering: Comparative Insights with Foundation Models

Multi-Modal and Multi-Attribute Generation of Single Cells with CFGen

A Theoretically-Principled Sparse, Connected, and Rigid Graph Representation of Molecules

Optimizing Neural Network Representations of Boolean Networks

Bridging the Data Provenance Gap Across Text, Speech, and Video

Cross-Attention Head Position Patterns Can Align with Human Visual Concepts in Text-to-Image Generative Models

Beyond Single Concept Vector: Modeling Concept Subspace in LLMs with Gaussian Distribution

Weakly Supervised Video Scene Graph Generation via Natural Language Supervision

Efficient Top-m Data Values Identification for Data Selection

PIED: Physics-Informed Experimental Design for Inverse Problems

Broaden your SCOPE! Efficient Multi-turn Conversation Planning for LLMs with Semantic Space

Group-robust Sample Reweighting for Subpopulation Shifts via Influence Functions

Model Risk-sensitive Offline Reinforcement Learning

Generalizable Motion Planning via Operator Learning

Reconstructive Visual Instruction Tuning

BrainACTIV: Identifying visuo-semantic properties driving cortical selectivity using diffusion-based image manipulation

LeanVec: Searching vectors faster by making them fit

Harnessing Diversity for Important Data Selection in Pretraining Large Language Models

Near-Optimal Policy Identification in Robust Constrained Markov Decision Processes via Epigraph Form

On Designing General and Expressive Quantum Graph Neural Networks with Applications to MILP Instance Representation

Boosting the visual interpretability of CLIP via adversarial fine-tuning

From Decoupling to Adaptive Transformation: a Wider Optimization Space for PTQ

Understanding Fairness Surrogate Functions in Algorithmic Fairness

Rethinking LLM Unlearning Objectives: A Gradient Perspective and Go Beyond

ScImage: How good are multimodal large language models at scientific text-to-image generation?

Apollo-MILP: An Alternating Prediction-Correction Neural Solving Framework for Mixed-Integer Linear Programming

Reveal Object in Lensless Photography via Region Gaze and Amplification

Do LLMs Recognize Your Preferences? Evaluating Personalized Preference Following in LLMs

Aria-MIDI: A Dataset of Piano MIDI Files for Symbolic Music Modeling

A Riemannian Framework for Learning Reduced-order Lagrangian Dynamics

Arithmetic Transformers Can Length-Generalize in Both Operand Length and Count

Arithmetic Without Algorithms: Language Models Solve Math with a Bag of Heuristics

$F^3Set$: Towards Analyzing Fast, Frequent, and Fine-grained Events from Videos

$R^2$-Guard: Robust Reasoning Enabled LLM Guardrail via Knowledge-Enhanced Logical Reasoning

{$\tau$}-bench: A Benchmark for \underline{T}ool-\underline{A}gent-\underline{U}ser Interaction in Real-World Domains

$\text{D}_{2}\text{O}$: Dynamic Discriminative Operations for Efficient Long-Context Inference of Large Language Models

3D-AffordanceLLM: Harnessing Large Language Models for Open-Vocabulary Affordance Detection in 3D Worlds

3DGS-Drag: Dragging Gaussians for Intuitive Point-Based 3D Editing

3DIS: Depth-Driven Decoupled Image Synthesis for Universal Multi-Instance Generation

3DitScene: Editing Any Scene via Language-guided Disentangled Gaussian Splatting

3DMolFormer: A Dual-channel Framework for Structure-based Drug Discovery

3D-SPATIAL MULTIMODAL MEMORY

3D StreetUnveiler with Semantic-aware 2DGS - a simple baseline

4K4DGen: Panoramic 4D Generation at 4K Resolution

A Black Swan Hypothesis: The Role of Human Irrationality in AI Safety

A Causal Lens for Learning Long-term Fair Policies

ACC-Collab: An Actor-Critic Approach to Multi-Agent LLM Collaboration

Accelerated Over-Relaxation Heavy-Ball Method: Achieving Global Accelerated Convergence with Broad Generalization

Accelerated training through iterative gradient propagation along the residual path

Accelerating 3D Molecule Generation via Jointly Geometric Optimal Transport

Accelerating Auto-regressive Text-to-Image Generation with Training-free Speculative Jacobi Decoding

Accelerating Diffusion Transformers with Token-wise Feature Caching

Accelerating Task Generalisation with Multi-Level Skill Hierarchies

Accessing Vision Foundation Models via ImageNet-1K

Accurate and Scalable Graph Neural Networks via Message Invariance

ACE: All-round Creator and Editor Following Instructions via Diffusion Transformer

A CLIP-Powered Framework for Robust and Generalizable Data Selection

A Closer Look at Machine Unlearning for Large Language Models

A Coefficient Makes SVRG Effective

A Common Pitfall of Margin-based Language Model Alignment: Gradient Entanglement

A Computational Framework for Modeling Emergence of Color Vision in the Human Brain

Action abstractions for amortized sampling

ActionReasoningBench: Reasoning about Actions with and without Ramification Constraints

Action Sequence Augmentation for Action Anticipation

Activation Gradient based Poisoned Sample Detection Against Backdoor Attacks

Active Learning for Continual Learning: Keeping the Past Alive in the Present

Active Learning for Neural PDE Solvers

ACTIVE: Offline Reinforcement Learning via Adaptive Imitation and In-sample $V$-Ensemble

ActSafe: Active Exploration with Safety Constraints for Reinforcement Learning

A Curious Case of the Missing Measure: Better Scores and Worse Generation

AdaFisher: Adaptive Second Order Optimization via Fisher Information

AdaGrad under Anisotropic Smoothness

ADAM: An Embodied Causal Agent in Open-World Environments

Adam Exploits $\ell_\infty$-geometry of Loss Landscape via Coordinate-wise Adaptivity

Adam-mini: Use Fewer Learning Rates To Gain More

ADAM Optimization with Adaptive Batch Selection

ADAPT: Attentive Self-Distillation and Dual-Decoder Prediction Fusion for Continual Panoptic Segmentation

Adapters for Altering LLM Vocabularies: What Languages Benefit the Most?

Adapting Multi-modal Large Language Model to Concept Drift From Pre-training Onwards

Adaptive $Q$-Network: On-the-fly Target Selection for Deep Reinforcement Learning

Adaptive backtracking for faster optimization

Adaptive Batch Size for Privately Finding Second-Order Stationary Points

Adaptive Camera Sensor for Vision Models

Adaptive Deployment of Untrusted LLMs Reduces Distributed Threats

Adaptive Gradient Clipping for Robust Federated Learning

Adaptive Length Image Tokenization via Recurrent Allocation

Adaptive Methods through the Lens of SDEs: Theoretical Insights on the Role of Noise

Adaptive Pruning of Pretrained Transformer via Differential Inclusions

Adaptive Retention & Correction: Test-Time Training for Continual Learning

Adaptive Shrinkage Estimation for Personalized Deep Kernel Regression in Modeling Brain Trajectories

Adaptive teachers for amortized samplers

Adaptive Transformer Programs: Bridging the Gap Between Performance and Interpretability in Transformers

AdaWM: Adaptive World Model based Planning for Autonomous Driving

ADBM: Adversarial Diffusion Bridge Model for Reliable Adversarial Purification

Addax: Utilizing Zeroth-Order Gradients to Improve Memory Efficiency and Performance of SGD for Fine-Tuning Language Models

Addressing Label Shift in Distributed Learning via Entropy Regularization

A Decade's Battle on Dataset Bias: Are We There Yet?

A Deep Generative Learning Approach for Two-stage Adaptive Robust Optimization

ADePT: Adaptive Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning

A Differentiable Metric for Discovering Groups and Unitary Representations

Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control

ADMM for Nonconvex Optimization under Minimal Continuity Assumption

ADMM for Structured Fractional Minimization

Advancing Graph Generation through Beta Diffusion

Advancing LLM Reasoning Generalists with Preference Trees

Advancing Out-of-Distribution Detection via Local Neuroplasticity

Advantage Alignment Algorithms

Adversarial Latent Feature Augmentation for Fairness

Adversarially Robust Anomaly Detection through Spurious Negative Pair Mitigation

Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized Embeddings

Adversarial Mixup Unlearning

Adversarial Score identity Distillation: Rapidly Surpassing the Teacher in One Step

Adversarial Training Can Provably Improve Robustness: Theoretical Analysis of Feature Learning Process Under Structured Data

Adversaries With Incentives: A Strategic Alternative to Adversarial Robustness

AdvWave: Stealthy Adversarial Jailbreak Attack against Large Audio-Language Models

Affine Steerable Equivariant Layer for Canonicalization of Neural Networks

A Formal Framework for Understanding Length Generalization in Transformers

A General Framework for Producing Interpretable Semantic Text Embeddings

AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents

Agent-Oriented Planning in Multi-Agent Systems

Agent S: An Open Agentic Framework that Uses Computers Like a Human

Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents

Agents' Room: Narrative Generation through Multi-step Collaboration

Agent-to-Sim: Learning Interactive Behavior Models from Casual Longitudinal Videos

AgentTrek: Agent Trajectory Synthesis via Guiding Replay with Web Tutorials

A Geometric Framework for Understanding Memorization in Generative Models

A Graph Enhanced Symbolic Discovery Framework For Efficient Logic Optimization

Agree to Disagree: Demystifying Homogeneous Deep Ensembles through Distributional Equivalence

AHA: A Vision-Language-Model for Detecting and Reasoning Over Failures in Robotic Manipulation

AI2TALE: An Innovative Information Theory-based Approach for Learning to Localize Phishing Attacks

AI as Humanity’s Salieri: Quantifying Linguistic Creativity of Language Models via Systematic Attribution of Machine Text against Web Text

AIMS.au: A Dataset for the Analysis of Modern Slavery Countermeasures in Corporate Statements

Aioli: A Unified Optimization Framework for Language Model Data Mixing

AI Sandbagging: Language Models can Strategically Underperform on Evaluations

A Large-scale Training Paradigm for Graph Generative Models

Algorithmic Stability Based Generalization Bounds for Adversarial Training

Aligned Better, Listen Better For Audio-Visual Large Language Models

Aligned Datasets Improve Detection of Latent Diffusion-Generated Images

Aligned LLMs Are Not Aligned Browser Agents

Aligning Generative Denoising with Discriminative Objectives Unleashes Diffusion for Visual Perception

Aligning Human Motion Generation with Human Perceptions

Aligning Language Models with Demonstrated Feedback

A Little Goes a Long Way: Efficient Long Context Training and Inference with Partial Contexts

ALLaM: Large Language Models for Arabic and English

Almost Optimal Batch-Regret Tradeoff for Batch Linear Contextual Bandits

AlphaEdit: Null-Space Constrained Model Editing for Language Models

Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models Trained on Corrupted Data

Amulet: ReAlignment During Test Time for Personalized Preference Adaptation of LLMs

Amortized Control of Continuous State Space Feynman-Kac Model for Irregular Time Series

A Multiscale Frequency Domain Causal Framework for Enhanced Pathological Analysis

ANaGRAM: A Natural Gradient Relative to Adapted Model for efficient PINNs learning

Analysing The Spectral Biases in Generative Models

Analyzing Neural Scaling Laws in Two-Layer Networks with Power-Law Data Spectra

An Asynchronous Bundle Method for Distributed Learning Problems

An Auditing Test to Detect Behavioral Shift in Language Models

AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents

An Efficient Framework for Crediting Data Contributors of Diffusion Models

An Effective Theory of Bias Amplification

An Empirical Analysis of Uncertainty in Large Language Model Evaluations

An Evolved Universal Transformer Memory

A new framework for evaluating model out-of-distribution generalisation for the biochemical domain

An Illustrated Guide to Automatic Sparse Differentiation

An Image is Worth More Than 16x16 Patches: Exploring Transformers on Individual Pixels

Animate-X: Universal Character Image Animation with Enhanced Motion Representation

An Information Criterion for Controlled Disentanglement of Multimodal Data

AnoLLM: Large Language Models for Tabular Anomaly Detection

A Non-Contrastive Learning Framework for Sequential Recommendation with Preference-Preserving Profile Generation

An Online Learning Theory of Trading-Volume Maximization

Answer, Assemble, Ace: Understanding How LMs Answer Multiple Choice Questions

Anti-Exposure Bias in Diffusion Models

APE: Faster and Longer Context-Augmented Generation via Adaptive Parallel Encoding

A Percolation Model of Emergence: Analyzing Transformers Trained on a Formal Language

A Policy-Gradient Approach to Solving Imperfect-Information Games with Best-Iterate Convergence

Approximating Full Conformal Prediction for Neural Network Regression with Gauss-Newton Influence

Approximation algorithms for combinatorial optimization with predictions

A Quantum Circuit-Based Compression Perspective for Parameter-Efficient Learning

A Rainbow in Deep Network Black Boxes

Are Large Vision Language Models Good Game Players?

ARLON: Boosting Diffusion Transformers with Autoregressive Models for Long Video Generation

A Sanity Check for AI-generated Image Detection

A Simple Framework for Open-Vocabulary Zero-Shot Segmentation

AssembleFlow: Rigid Flow Matching with Inertial Frames for Molecular Assembly

As Simple as Fine-tuning: LLM Alignment via Bidirectional Negative Feedback Loss

A Stochastic Approach to the Subset Selection Problem via Mirror Descent

A Statistical Framework for Ranking LLM-based Chatbots

A Theoretical Analysis of Self-Supervised Learning for Vision Transformers

A Theory for Token-Level Harmonization in Retrieval-Augmented Generation

A Theory of Initialisation's Impact on Specialisation

Atlas Gaussians Diffusion for 3D Generation

A Transfer Attack to Image Watermarks

A transfer learning framework for weak to strong generalization

A Truncated Newton Method for Optimal Transport

Attention in Large Language Models Yields Efficient Zero-Shot Re-Rankers

Attention with Markov: A Curious Case of Single-layer Transformers

Audio Large Language Models Can Be Descriptive Speech Quality Evaluators

A Unified Theory of Quantum Neural Network Loss Landscapes

A Unifying Framework for Representation Learning

AutoCGP: Closed-Loop Concept-Guided Policies from Unlabeled Demonstrations

AutoCLIP: Auto-tuning Zero-Shot Classifiers for Vision-Language Models

AutoDAN-Turbo: A Lifelong Agent for Strategy Self-Exploration to Jailbreak LLMs

Auto-GDA: Automatic Domain Adaptation for Efficient Grounding Verification in Retrieval-Augmented Generation

AutoG: Towards automatic graph construction from tabular data

Automated Filtering of Human Feedback Data for Aligning Text-to-Image Diffusion Models

Autoregressive Pretraining with Mamba in Vision

Autoregressive Video Generation without Vector Quantization

AutoUAD: Hyper-parameter Optimization for Unsupervised Anomaly Detection

BaB-ND: Long-Horizon Motion Planning with Branch-and-Bound and Neural Dynamics

Backdooring Vision-Language Models with Out-Of-Distribution Data

Backtracking Improves Generation Safety

Balanced Neural ODEs: nonlinear model order reduction and Koopman operator approximations

Balanced Ranking with Relative Centrality: A multi-core periphery perspective

Balancing Bias in Two-sided Markets for Fair Stable Matchings

Basis Sharing: Cross-Layer Parameter Sharing for Large Language Model Compression

Bayesian Analysis of Combinatorial Gaussian Process Bandits

Bayesian Experimental Design Via Contrastive Diffusions

Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences

Bayesian Optimization via Continual Variational Last Layer Training

Bayesian Regularization of Latent Representation

Bayesian Treatment of the Spectrum of the Empirical Kernel in (Sub)Linear-Width Neural Networks

BEEM: Boosting Performance of Early Exit DNNs using Multi-Exit Classifiers as Experts

Behavioral Entropy-Guided Dataset Generation for Offline Reinforcement Learning

Be More Diverse than the Most Diverse: Optimal Mixtures of Generative Models via Mixture-UCB Bandit Algorithms

Benchmarking Vision Language Model Unlearning via Fictitious Facial Identity Dataset

Benign Overfitting in Out-of-Distribution Generalization of Linear Models

BenTo: Benchmark Reduction with In-Context Transferability

Better autoregressive regression with LLMs via regression-aware fine-tuning

Better Instruction-Following Through Minimum Bayes Risk

Better than Your Teacher: LLM Agents that learn from Privileged AI Feedback

Beyond Autoregression: Discrete Diffusion for Complex Reasoning and Planning

Beyond Autoregression: Fast LLMs via Self-Distillation Through Time

Beyond Content Relevance: Evaluating Instruction Following in Retrieval Models

Beyond correlation: The impact of human uncertainty in measuring the effectiveness of automatic evaluation and LLM-as-a-judge

Beyond FVD: An Enhanced Evaluation Metrics for Video Generation Distribution Quality

Beyond Graphs: Can Large Language Models Comprehend Hypergraphs?

Beyond Interpretability: The Gains of Feature Monosemanticity on Model Robustness

Beyond Linear Approximations: A Novel Pruning Approach for Attention Matrix

Beyond Model Collapse: Scaling Up with Synthesized Data Requires Verification

Beyond Next Token Prediction: Patch-Level Training for Large Language Models

Beyond Random Masking: When Dropout meets Graph Convolutional Networks

Beyond Squared Error: Exploring Loss Design for Enhanced Training of Generative Flow Networks

Beyond Surface Structure: A Causal Assessment of LLMs' Comprehension ability

Beyond Worst-Case Dimensionality Reduction for Sparse Vectors

Bidirectional Decoding: Improving Action Chunking via Guided Test-Time Sampling

Bi-Factorial Preference Optimization: Balancing Safety-Helpfulness in Language Models

BiGR: Harnessing Binary Latent Codes for Image Generation and Improved Visual Representation Capabilities

Bilinear MLPs enable weight-based mechanistic interpretability

BinaryDM: Accurate Weight Binarization for Efficient Diffusion Models

Binary Losses for Density Ratio Estimation

BingoGuard: LLM Content Moderation Tools with Risk Levels

BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments

Biologically Constrained Barrel Cortex Model Integrates Whisker Inputs and Replicates Key Brain Network Dynamics

Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences

BIRD: A Trustworthy Bayesian Inference Framework for Large Language Models

Bisimulation Metric for Model Predictive Control

BitStack: Any-Size Compression of Large Language Models in Variable Memory Environments

BLEND: Behavior-guided Neural Population Dynamics Modeling via Privileged Knowledge Distillation

Block-Attention for Efficient Prefilling

Block Diffusion: Interpolating Between Autoregressive and Diffusion Language Models

BodyGen: Advancing Towards Efficient Embodiment Co-Design

Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions

BOND: Aligning LLMs with Best-of-N Distillation

Booster: Tackling Harmful Fine-tuning for Large Language Models via Attenuating Harmful Perturbation

Boosting Latent Diffusion with Perceptual Objectives

Boosting Methods for Interval-censored Data with Regression and Classification

Boosting Perturbed Gradient Ascent for Last-Iterate Convergence in Games

Boosting Ray Search Procedure of Hard-label Attacks with Transfer-based Priors

Bootstrapped Energy Based Models: What are they good for?

Bootstrapped Model Predictive Control

Bounds on $L_p$ Errors in Density Ratio Estimation via $f$-Divergence Loss Functions

BP-Modified Local Loss for Efficient Training of Deep Neural Networks

BRAID: Input-driven Nonlinear Dynamical Modeling of Neural-Behavioral Data

Brain Bandit: A Biologically Grounded Neural Network for Efficient Control of Exploration

Breaking Free from MMI: A New Frontier in Rationalization by Probing Input Utilization

Breaking Mental Set to Improve Reasoning through Diverse Multi-Agent Debate

Breaking the $\log(1/\Delta_2)$ Barrier: Better Batched Best Arm Identification with Adaptive Grids

Bridging Compressed Image Latents and Multimodal Large Language Models

Bridging Jensen Gap for Max-Min Group Fairness Optimization in Recommendation

Bridging the Gap Between f-divergences and Bayes Hilbert Spaces

Bridging the Gap between Variational Inference and Stochastic Gradient MCMC in Function Space

Bridging the Semantic Gap Between Text and Table: A Case Study on NL2SQL

Bringing NeRFs to the Latent Space: Inverse Graphics Autoencoder

Broadening Target Distributions for Accelerated Diffusion Models via a Novel Analysis Approach

B-STaR: Monitoring and Balancing Exploration and Exploitation in Self-Taught Reasoners

Building Blocks of Differentially Private Training

Building Math Agents with Multi-Turn Iterative Preference Learning

Building, Reusing, and Generalizing Abstract Representations from Concrete Sequences

Cached Multi-Lora Composition for Multi-Concept Image Generation

C-Adapter: Adapting Deep Classifiers for Efficient Conformal Prediction Sets

Can a MISL Fly? Analysis and Ingredients for Mutual Information Skill Learning

Can In-context Learning Really Generalize to Out-of-distribution Tasks?

Can Large Language Models Understand Symbolic Graphics Programs?

Can LLM Simulations Truly Reflect Humanity? A Deep Dive

Can LLMs Really Learn to Translate a Low-Resource Language from One Grammar Book?

Can LLMs Solve Longer Math Word Problems Better?

Can LLMs Understand Time Series Anomalies?

Can One Modality Model Synergize Training of Other Modality Models?

Can Reinforcement Learning Solve Asymmetric Combinatorial-Continuous Zero-Sum Games?

Can Transformers Do Enumerative Geometry?

Can Video LLMs Refuse to Answer? Alignment for Answerability in Video Large Language Models

Can Watermarked LLMs be Identified by Users via Crafted Prompts?

Can We Ignore Labels in Out of Distribution Detection?

CaPo: Cooperative Plan Optimization for Efficient Embodied Multi-Agent Cooperation

CARTS: Advancing Neural Theorem Proving with Diversified Tactic Calibration and Bias-Resistant Tree Search

Catastrophic Failure of LLM Unlearning via Quantization

CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models

Cauchy-Schwarz Regularizers

Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning

Causal Discovery via Bayesian Optimization

Causal Effect Estimation with Mixed Latent Confounders and Post-treatment Variables

Causal Identification for Complex Functional Longitudinal Studies

Causal Information Prioritization for Efficient Reinforcement Learning

CausalRivers - Scaling up benchmarking of causal discovery for real-world time-series

CBMA: Improving Conformal Prediction through Bayesian Model Averaging

CBraMod: A Criss-Cross Brain Foundation Model for EEG Decoding

C-CLIP: Multimodal Continual Learning for Vision-Language Model

CEB: Compositional Evaluation Benchmark for Fairness in Large Language Models

Century: A Framework and Dataset for Evaluating Historical Contextualisation of Sensitive Images

CertainlyUncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awareness

CFD: Learning Generalized Molecular Representation via Concept-Enhanced Feedback Disentanglement

CFG++: Manifold-constrained Classifier Free Guidance for Diffusion Models

CG-Bench: Clue-grounded Question Answering Benchmark for Long Video Understanding

Chain-of-region: Visual Language Models Need Details for Diagram Analysis

CHAMP: Conformalized 3D Human Multi-Hypothesis Pose Estimators

Charting the Design Space of Neural Graph Representations for Subgraph Matching

CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL

ChatQA 2: Bridging the Gap to Proprietary LLMs in Long Context and RAG Capabilities

Cheating Automatic LLM Benchmarks: Null Models Achieve High Win Rates

Chemistry-Inspired Diffusion with Non-Differentiable Guidance

CHiP: Cross-modal Hierarchical Direct Preference Optimization for Multimodal LLMs

CipherPrune: Efficient and Scalable Private Transformer Inference

Circuit Representation Learning with Masked Gate Modeling and Verilog-AIG Alignment

CityAnchor: City-scale 3D Visual Grounding with Multi-modality LLMs

Class Distribution-induced Attention Map for Open-vocabulary Semantic Segmentations

Classic but Everlasting: Traditional Gradient-Based Algorithms Converges Fast Even in Time-Varying Multi-Player Games

ClawMachine: Learning to Fetch Visual Tokens for Referential Comprehension

CLIPDrag: Combining Text-based and Drag-based Instructions for Image Editing

CLIPure: Purification in Latent Space via CLIP for Adversarially Robust Zero-Shot Classification

Closed-Form Merging of Parameter-Efficient Modules for Federated Continual Learning

Co$^{\mathbf{3}}$Gesture: Towards Coherent Concurrent Co-speech 3D Gesture Generation with Interactive Diffusion

CodePlan: Unlocking Reasoning Potential in Large Language Models by Scaling Code-form Planning

COFlowNet: Conservative Constraints on Flows Enable High-Quality Candidate Generation

CogCoM: A Visual Language Model with Chain-of-Manipulations Reasoning

CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer

CoInD: Enabling Logical Compositions in Diffusion Models

Collab: Controlled Decoding using Mixture of Agents for LLM Alignment

CollabEdit: Towards Non-destructive Collaborative Knowledge Editing

Collapsed Language Models Promote Fairness

ColPali: Efficient Document Retrieval with Vision Language Models

ComaDICE: Offline Cooperative Multi-Agent Reinforcement Learning with Stationary Distribution Shift Regularization

Combatting Dimensional Collapse in LLM Pre-Training Data via Submodular File Selection

Combining Induction and Transduction for Abstract Reasoning

COMBO: Compositional World Models for Embodied Multi-Agent Cooperation

Commit0: Library Generation from Scratch

CO-MOT: Boosting End-to-end Transformer-based Multi-Object Tracking via Coopetition Label Assignment and Shadow Sets

CoMotion: Concurrent Multi-person 3D Motion

Comparing noisy neural population dynamics using optimal transport distances

ComPC: Completing a 3D Point Cloud with 2D Diffusion Priors

Competition Dynamics Shape Algorithmic Phases of In-Context Learning

Composable Interventions for Language Models

Compositional 4D Dynamic Scenes Understanding with Physics Priors for Video Question Answering

Compositional Entailment Learning for Hyperbolic Vision-Language Models

Compositional simulation-based inference for time series

Computational Limits of Low-Rank Adaptation (LoRA) Fine-Tuning for Transformer Models

Computationally Efficient RL under Linear Bellman Completeness for Deterministic Dynamics

Compute-Constrained Data Selection

Computing Circuits Optimization via Model-Based Circuit Genetic Evolution

Subtask-Aware Visual Reward Learning from Segmented Demonstrations

Concept Bottleneck Language Models For Protein Design

ConceptPrune: Concept Editing in Diffusion Models via Skilled Neuron Pruning

Concept-ROT: Poisoning Concepts in Large Language Models with Model Editing

CONDA: Adaptive Concept Bottleneck for Foundation Models Under Distribution Shifts

Conditional Diffusion with Ordinal Regression: Longitudinal Data Generation for Neurodegenerative Disease Studies

Confidence Elicitation: A New Attack Vector for Large Language Models

Conformal Generative Modeling with Improved Sample Efficiency through Sequential Greedy Filtering

Conformalized Interactive Imitation Learning: Handling Expert Shift and Intermittent Feedback

Conformal Structured Prediction

ConMix: Contrastive Mixup at Representation Level for Long-tailed Deep Clustering

Conservative Contextual Bandits: Beyond Linear Representations

Consistency Checks for Language Model Forecasters

Constraint-Conditioned Actor-Critic for Offline Safe Reinforcement Learning

Constructing Confidence Intervals for Average Treatment Effects from Multiple Datasets

Content-Style Learning from Unaligned Domains: Identifiability under Unknown Latent Dimensions

Context-Alignment: Activating and Enhancing LLMs Capabilities in Time Series

Context-aware Dynamic Pruning for Speech Foundation Models

Context Steering: Controllable Personalization at Inference Time

Contextual Document Embeddings

Continual Slow-and-Fast Adaptation of Latent Neural Dynamics (CoSFan): Meta-Learning What-How & When to Adapt

Continuous Autoregressive Modeling with Stochastic Monotonic Alignment for Speech Synthesis

Continuous Ensemble Weather Forecasting with Diffusion models

Continuous Exposure Learning for Low-light Image Enhancement using Neural ODEs

Contractive Dynamical Imitation Policies for Efficient Out-of-Sample Recovery

ContraDiff: Planning Towards High Return States via Contrastive Learning

Contrastive Learning from Synthetic Audio Doppelgängers

ControlAR: Controllable Image Generation with Autoregressive Models

Controllable Blur Data Augmentation Using 3D-Aware Motion Estimation

Controllable Generation via Locally Constrained Resampling

Controllable Satellite-to-Street-View Synthesis with Precise Pose Alignment and Zero-Shot Environmental Control

Controllable Unlearning for Image-to-Image Generative Models via $\epsilon$-Constrained Optimization

Controlled LLM Decoding via Discrete Auto-regressive Biasing

Controlling Language and Diffusion Models by Transporting Activations

Controlling Space and Time with Diffusion Models

Controlling the Fidelity and Diversity of Deep Generative Models via Pseudo Density

Control-oriented Clustering of Visual Latent Representation

Convergence and Implicit Bias of Gradient Descent on Continual Linear Classification

Convergence of Distributed Adaptive Optimization with Local Updates

Convergence of Score-Based Discrete Diffusion Models: A Discrete-Time Analysis

Convergent Privacy Loss of Noisy-SGD without Convexity and Smoothness

Convex Formulations for Training Two-Layer ReLU Neural Networks

Coreset Spectral Clustering

CoRNStack: High-Quality Contrastive Data for Better Code Retrieval and Reranking

Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization

Correlation and Navigation in the Vocabulary Key Representation Space of Language Models

CoTFormer: A Chain of Thought Driven Architecture with Budget-Adaptive Computation Cost at Inference

Counterfactual Generative Modeling with Variational Causal Inference

Counterfactual Realizability

CPSample: Classifier Protected Sampling for Guarding Training Data During Diffusion

CR2PQ: Continuous Relative Rotary Positional Query for Dense Visual Representation Learning

CR-CTC: Consistency regularization on CTC for improved speech recognition

Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification

Credit-based self organizing maps: training deep topographic networks with minimal performance degradation

Cross-Domain Off-Policy Evaluation and Learning for Contextual Bandits

Cross-Embodiment Dexterous Grasping with Reinforcement Learning

Cross-Entropy Is All You Need To Invert the Data Generating Process

Cross-Modal Safety Mechanism Transfer in Large Vision-Language Models

CryoFM: A Flow-based Foundation Model for Cryo-EM Densities

CryoGEN: Generative Energy-based Models for Cryogenic Electron Tomography Reconstruction

CtD: Composition through Decomposition in Emergent Communication

CtrLoRA: An Extensible and Efficient Framework for Controllable Image Generation

CTSyn: A Foundation Model for Cross Tabular Data Generation

CubeDiff: Repurposing Diffusion-Based Image Models for Panorama Generation

CURIE: Evaluating LLMs on Multitask Scientific Long-Context Understanding and Reasoning

Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems

CViT: Continuous Vision Transformer for Operator Learning

CyberHost: A One-stage Diffusion Framework for Audio-driven Talking Body Generation

Cyclic Contrastive Knowledge Transfer for Open-Vocabulary Object Detection

DailyDilemmas: Revealing Value Preferences of LLMs with Quandaries of Daily Life

DAMO: Decoding by Accumulating Activations Momentum for Mitigating Hallucinations in Vision-Language Models

Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning

Data Center Cooling System Optimization Using Offline Reinforcement Learning

Data-centric Prediction Explanation via Kernelized Stein Discrepancy

Data Distillation for extrapolative protein design through exact preference optimization

DataMan: Data Manager for Pre-training Large Language Models

Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance

Data Pruning by Information Maximization

Data Scaling Laws in Imitation Learning for Robotic Manipulation

Data Selection via Optimal Control for Language Models

Dataset Distillation via Knowledge Distillation: Towards Efficient Self-Supervised Pre-training of Deep Networks

Dataset Ownership Verification in Contrastive Pre-trained Models

Data Taggants: Dataset Ownership Verification Via Harmless Targeted Data Poisoning

Data Unlearning in Diffusion Models

DAWN: Dynamic Frame Avatar with Non-autoregressive Diffusion Framework for Talking head Video Generation

DCT-CryptoNets: Scaling Private Inference in the Frequency Domain

DebGCD: Debiased Learning with Distribution Guidance for Generalized Category Discovery

Debiasing Mini-Batch Quadratics for Applications in Deep Learning

dEBORA: Efficient Bilevel Optimization-based low-Rank Adaptation

Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees

DeciMamba: Exploring the Length Extrapolation Potential of Mamba

Decision Tree Induction Through LLMs via Semantically-Aware Evolution

Decoding Game: On Minimax Optimality of Heuristic Text Generation Strategies

Decomposition Polyhedra of Piecewise Linear Functions

Decoupled Graph Energy-based Model for Node Out-of-Distribution Detection on Heterophilic Graphs

Decoupled Subgraph Federated Learning

Decoupling Angles and Strength in Low-rank Adaptation

DEEM: Diffusion models serve as the eyes of large language models for image perception

DeeperForward: Enhanced Forward-Forward Training for Deeper and Better Performance

DeepGate4: Efficient and Effective Representation Learning for Circuit Design at Scale

Deep Incomplete Multi-view Learning via Cyclic Permutation of VAEs

Deep Kernel Posterior Learning under Infinite Variance Prior Weights

Deep MMD Gradient Flow without adversarial training

DeepTAGE: Deep Temporal-Aligned Gradient Enhancement for Optimizing Spiking Neural Networks

Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries

DeFT: Decoding with Flash Tree-attention for Efficient Tree-structured LLM Inference

DELIFT: Data Efficient Language model Instruction Fine-Tuning

DelTA: An Online Document-Level Translation Agent Based on Multi-Level Memory

DELTA: DENSE EFFICIENT LONG-RANGE 3D TRACKING FOR ANY VIDEO

Demystifying the Token Dynamics of Deep Selective State Space Models

Demystifying Topological Message-Passing with Relational Structures: A Case Study on Oversquashing in Simplicial Message-Passing

Denoising as Adaptation: Noise-Space Domain Adaptation for Image Restoration

Denoising Autoregressive Transformers for Scalable Text-to-Image Generation

Denoising Levy Probabilistic Models

Denoising with a Joint-Embedding Predictive Architecture

Dense Video Object Captioning from Disjoint Supervision

Density estimation with LLMs: a geometric investigation of in-context learning trajectories

Depth Any Video with Scalable Synthetic Data

Depth Pro: Sharp Monocular Metric Depth in Less Than a Second

Descent with Misaligned Gradients and Applications to Hidden Convexity

Designing Mechanical Meta-Materials by Learning Equivariant Flows

DexTrack: Towards Generalizable Neural Tracking Control for Dexterous Manipulation from Human References

D-FINE: Redefine Regression Task of DETRs as Fine-grained Distribution Refinement

DICE: End-to-end Deformation Capture of Hand-Face Interactions from a Single Image

Diff-2-in-1: Bridging Generation and Dense Perception with Diffusion Models

Difference-of-submodular Bregman Divergence

Differentiable and Learnable Wireless Simulation with Geometric Transformers

Differentiable Optimization of Similarity Scores Between Models and Brains

Differential learning kinetics govern the transition from memorization to generalization during in-context learning

Differentially private learners for heterogeneous treatment effects

Differentially private optimization for non-decomposable objective functions

Differentially Private Steering for Large Language Model Alignment

Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning

Diffusion Actor-Critic: Formulating Constrained Policy Iteration as Diffusion Noise Regression for Offline Reinforcement Learning

Diffusion Attribution Score: Evaluating Training Data Influence in Diffusion Model

Diffusion-based Decoupled Deterministic and Uncertain Framework for Probabilistic Multivariate Time Series Forecasting

Diffusion-Based Planning for Autonomous Driving with Flexible Guidance

Diffusion Bridge AutoEncoders for Unsupervised Representation Learning

Diffusion Bridge Implicit Models

Diffusion Models and Gaussian Flow Matching: Two Sides of the Same Coin

Diffusion Models are Evolutionary Algorithms

Diffusion Models as Cartoonists: The Curious Case of High Density Regions

Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion Models

Diffusion On Syntax Trees For Program Synthesis

Diffusion Transformer Captures Spatial-Temporal Dependencies: A Theory for Gaussian Process Data

Dimension Agnostic Neural Processes

Direct Post-Training Preference Alignment for Multi-Agent Motion Generation Model Using Implicit Feedback from Pre-training Demonstrations

Discovering Clone Negatives via Adaptive Contrastive Learning for Image-Text Matching

Discovering Temporally Compositional Neural Manifolds with Switching Infinite GPFA

Discrete Copula Diffusion

Discrete GCBF Proximal Policy Optimization for Multi-agent Safe Optimal Control

Discrete Latent Plans via Semantic Skill Abstractions

Discretization-invariance? On the Discretization Mismatch Errors in Neural Operators

Discriminating image representations with principal distortions

Discriminator-Guided Embodied Planning for LLM Agent

Disentangled Representation Learning with the Gromov-Monge Gap

Disentangling 3D Animal Pose Dynamics with Scrubbed Conditional Latent Variables

DisEnvisioner: Disentangled and Enriched Visual Prompt for Customized Image Generation

DiSK: Differentially Private Optimizer with Simplified Kalman Filter for Noise Reduction

DisPose: Disentangling Pose Guidance for Controllable Human Image Animation

Dissecting Adversarial Robustness of Multimodal LM Agents

Distilled Decoding 1: One-step Sampling of Image Auto-regressive Models with Flow Matching

Distilling Dataset into Neural Field

Distilling Reinforcement Learning Algorithms for In-Context Model-Based Planning

Dist Loss: Enhancing Regression in Few-Shot Region through Distribution Distance Constraint

Distributed Speculative Inference (DSI): Speculation Parallelism for Provably Faster Lossless Language Model Inference

Distributional Associations vs In-Context Reasoning: A Study of Feed-forward and Attention Layers

Distribution-Free Data Uncertainty for Neural Network Regression

DiTTo-TTS: Diffusion Transformers for Scalable Text-to-Speech without Domain-Specific Factors

Divergence of Neural Tangent Kernel in Classification Problems

Divergence-Regularized Discounted Aggregation: Equilibrium Finding in Multiplayer Partially Observable Stochastic Games

Diversity Empowers Intelligence: Integrating Expertise of Software Engineering Agents

Diversity-Rewarded CFG Distillation

Do as I do (Safely): Mitigating Task-Specific Fine-tuning Risks in Large Language Models

Dobi-SVD: Differentiable SVD for LLM Compression and Some New Perspectives

DocMIA: Document-Level Membership Inference Attacks against DocVQA Models

Do Contemporary Causal Inference Models Capture Real-World Heterogeneity? Findings from a Large-Scale Benchmark

Does Editing Provide Evidence for Localization?

Does Safety Training of LLMs Generalize to Semantically Related Natural Prompts?

Does SGD really happen in tiny subspaces?

Do I Know This Entity? Knowledge Awareness and Hallucinations in Language Models

Do LLM Agents Have Regret? A Case Study in Online Learning and Games

Do LLMs estimate uncertainty well in instruction-following?

Do LLMs ``know'' internally when they follow instructions?

Domain Guidance: A Simple Transfer Approach for a Pre-trained Diffusion Model

Do Mice Grok? Glimpses of Hidden Progress in Sensory Cortex

Do not write that jailbreak paper

Don't flatten, tokenize! Unlocking the key to SoftMoE's efficacy in deep RL

DON’T STOP ME NOW: EMBEDDING BASED SCHEDULING FOR LLMS

DOTS: Learning to Reason Dynamically in LLMs via Optimal Reasoning Trajectories Search

Do vision models perceive objects like toddlers ?

Do WGANs succeed because they minimize the Wasserstein Distance? Lessons from Discrete Generators

DPLM-2: A Multimodal Diffusion Protein Language Model

Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter Efficient

DreamCatalyst: Fast and High-Quality 3D Editing via Controlling Editability and Identity Preservation

Dream to Manipulate: Compositional World Models Empowering Robot Imitation Learning with Imagination

Dreamweaver: Learning Compositional World Models from Pixels

DRoP: Distributionally Robust Data Pruning

Drop-Upcycling: Training Sparse Mixture of Experts with Partial Re-initialization

DSBench: How Far Are Data Science Agents from Becoming Data Science Experts?

DSPO: Direct Score Preference Optimization for Diffusion Model Alignment

Dualformer: Controllable Fast and Slow Thinking by Learning with Randomized Reasoning Traces

DUALFormer: Dual Graph Transformer

Dual Process Learning: Controlling Use of In-Context vs. In-Weights Strategies with Weight Forgetting

Durable Quantization Conditioned Misalignment Attack on Large Language Models

DynAlign: Unsupervised Dynamic Taxonomy Alignment for Cross-Domain Segmentation

DynaMath: A Dynamic Visual Benchmark for Evaluating Mathematical Reasoning Robustness of Vision Language Models

Dynamic Diffusion Transformer

Dynamic Gaussians Mesh: Consistent Mesh Reconstruction from Dynamic Scenes

Dynamic Modeling of Patients, Modalities and Tasks via Multi-modal Multi-task Mixture of Experts

Dynamic Negative Guidance of Diffusion Models

Dynamics of Concept Learning and Compositional Generalization

Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustness

DynaPrompt: Dynamic Test-Time Prompt Tuning

DynFrs: An Efficient Framework for Machine Unlearning in Random Forest

E(3)-equivariant models cannot learn chirality: Field-based molecular generation

Eagle: Exploring The Design Space for Multimodal LLMs with Mixture of Encoders

Earlier Tokens Contribute More: Learning Direct Preference Optimization From Temporal Decay Perspective

ECD: A Machine Learning Benchmark for Predicting Enhanced-Precision Electronic Charge Density in Crystalline Inorganic Materials

EC-Diffuser: Multi-Object Manipulation via Entity-Centric Behavior Generation

EC-DIT: Scaling Diffusion Transformers with Adaptive Expert-Choice Routing

econSG: Efficient and Multi-view Consistent Open-Vocabulary 3D Semantic Gaussians

EdgeRunner: Auto-regressive Auto-encoder for Artistic Mesh Generation

EDiT: A Local-SGD-Based Efficient Distributed Training Method for Large Language Models

Effective and Efficient Time-Varying Counterfactual Prediction with State-Space Models

Effective Interplay between Sparsity and Quantization: From Theory to Practice

Effective post-training embedding compression via temperature control in contrastive training

Efficient Active Imitation Learning with Random Network Distillation

Efficient Alternating Minimization with Applications to Weighted Low Rank Approximation

Efficient and Context-Aware Label Propagation for Zero-/Few-Shot Training-Free Adaptation of Vision-Language Model

Efficient and Robust Neural Combinatorial Optimization via Wasserstein-Based Coresets

Efficient and Trustworthy Causal Discovery with Latent Variables and Complex Relations

Efficient Cross-Episode Meta-RL

Efficient Dictionary Learning with Switch Sparse Autoencoders

Efficient Diffusion Transformer Policies with Mixture of Expert Denoisers for Multitask Learning

Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNets

Efficient Exploration and Discriminative World Model Learning with an Object-Centric Abstraction

Efficient Imitation under Misspecification

Efficient Interpolation between Extragradient and Proximal Methods for Weak MVIs

Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs

Efficient Neuron Segmentation in Electron Microscopy by Affinity-Guided Queries

Efficient Online Pruning and Abstraction for Imperfect Information Extensive-Form Games

Efficient Perplexity Bound and Ratio Matching in Discrete Diffusion Language Models

Efficient Policy Evaluation with Safety Constraint for Reinforcement Learning

Efficient stagewise pretraining via progressive subnetworks

Efficient Training of Neural Stochastic Differential Equations by Matching Finite Dimensional Distributions

EffoVPR: Effective Foundation Model Utilization for Visual Place Recognition

EgoSim: Egocentric Exploration in Virtual Worlds with Multi-modal Conditioning

ElasticTok: Adaptive Tokenization for Image and Video

ELBOing Stein: Variational Bayes with Stein Mixture Inference

ELFS: Label-Free Coreset Selection with Proxy Training Dynamics

Eliciting Human Preferences with Language Models

Eliminating Oversaturation and Artifacts of High Guidance Scales in Diffusion Models

Eliminating Position Bias of Language Models: A Mechanistic Approach

Elucidating the Preconditioning in Consistency Distillation

EmbedLLM: Learning Compact Representations of Large Language Models

EmbodiedSAM: Online Segment Any 3D Thing in Real Time

Emergence of meta-stable clustering in mean-field transformer models

EMMA: Empowering Multi-modal Mamba with Structural and Hierarchical Alignment

Empowering LLM Agents with Zero-Shot Optimal Decision-Making through Q-learning

Empowering Users in Digital Privacy Management through Interactive LLM-Based Agents

Enabling Realtime Reinforcement Learning at Scale with Staggered Asynchronous Inference

Endless Jailbreaks with Bijection Learning

Endowing Visual Reprogramming with Adversarial Robustness

E(n) Equivariant Topological Neural Networks

Energy-Weighted Flow Matching for Offline Reinforcement Learning

Enhanced Diffusion Sampling via Extrapolation with Multiple ODE Solutions

Enhancing Clustered Federated Learning: Integration of Strategies and Improved Methodologies

Enhancing Compositional Text-to-Image Generation with Reliable Random Seeds

Enhancing Federated Domain Adaptation with Multi-Domain Prototype-Based Federated Fine-Tuning

Enhancing Language Model Agents using Diversity of Thoughts

Enhancing Multilingual Reasoning in LLMs: Insights from Cross-Linguistic Correlations and Optimal Data Proportions

Enhancing Pre-trained Representation Classifiability can Boost its Interpretability

Enhancing Robust Fairness via Confusional Spectral Regularization

Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems

Enhancing Uncertainty Estimation and Interpretability with Bayesian Non-negative Decision Layer

Enhancing Vision-Language Model with Unmasked Token Alignment

Ensembles of Low-Rank Expert Adapters

Episodic Memories Generation and Evaluation Benchmark for Large Language Models

EqNIO: Subequivariant Neural Inertial Odometry

Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Models

Equivariant Masked Position Prediction for Efficient Molecular Representation

Equivariant Symmetry Breaking Sets

Error-quantified Conformal Inference for Time Series

ESE: Espresso Sentence Embeddings

Estimating the Probabilities of Rare Outputs in Language Models

Estimation of single-cell and tissue perturbation effect in spatial transcriptomics via Spatial Causal Disentanglement

ETA: Evaluating Then Aligning Safety of Vision Language Models at Inference Time

Euler Characteristic Tools for Topological Data Analysis

EvA: Erasing Spurious Correlations with Activations

EVA: Geometric Inverse Design for Fast Protein Motif-Scaffolding with Coupled Flow

Evaluating Semantic Variation in Text-to-Image Synthesis: A Causal Perspective

Event-Driven Online Vertical Federated Learning

Everything, Everywhere, All at Once: Is Mechanistic Interpretability Identifiable?

Exact Byte-Level Probabilities from Tokenized Language Models for FIM-Tasks and Model Ensembles

Exact Computation of Any-Order Shapley Interactions for Graph Neural Networks

ExACT: Teaching AI Agents to Explore with Reflective-MCTS and Exploratory Learning

Examining Alignment of Large Language Models through Representative Heuristics: the case of political stereotypes

Execution-guided within-prompt search for programming-by-example

Expected Return Symmetries

Exploiting Hankel-Toeplitz Structures for Fast Computation of Kernel Precision Matrices

Exploiting Hidden Symmetry to Improve Objective Perturbation for DP linear learners with a nonsmooth L1-norm

Exploiting Structure in Offline Multi-Agent RL: The Benefits of Low Interaction Rank

Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF

Exploring channel distinguishability in local neighborhoods of the model space in quantum neural networks

Exploring Learning Complexity for Efficient Downstream Dataset Pruning

Exploring The Loss Landscape Of Regularized Neural Networks Via Convex Duality

Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement Learning

Exposure Bracketing Is All You Need For A High-Quality Image

Extendable and Iterative Structure Learning Strategy for Bayesian Networks

FaceShot: Bring Any Character into Life

Facilitating Multi-turn Function Calling for LLMs via Compositional Instruction Tuning

Factor Graph-based Interpretable Neural Networks

FACTS: A Factored State-Space Framework for World Modelling

Fair Clustering in the Sliding Window Model

FairDen: Fair Density-Based Clustering

Fair Submodular Cover

FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows"

FakeShield: Explainable Image Forgery Detection and Localization via Multi-modal Large Language Models

Fantastic Targets for Concept Erasure in Diffusion Models and Where To Find Them

Faster Algorithms for Structured Linear and Kernel Support Vector Machines

Faster Cascades via Speculative Decoding

Faster Diffusion Sampling with Randomized Midpoints: Sequential and Parallel

Faster, More Efficient RLHF through Off-Policy Asynchronous Learning

Fast training and sampling of Restricted Boltzmann Machines

Fast Uncovering of Protein Sequence Diversity from Structure

Fast unsupervised ground metric learning with tree-Wasserstein distance

Feature Averaging: An Implicit Bias of Gradient Descent Leading to Non-Robustness in Neural Networks

Feature-Based Online Bilateral Trade

Feature Responsiveness Scores: Model-Agnostic Explanations for Recourse

Federated Continual Learning Goes Online: Uncertainty-Aware Memory Management for Vision Tasks and Beyond

Federated Few-Shot Class-Incremental Learning

Federated Granger Causality Learning For Interdependent Clients With State Space Representation

Federated Residual Low-Rank Adaption of Large Language Models

Fengbo: a Clifford Neural Operator pipeline for 3D PDEs in Computational Fluid Dynamics

Ferret-UI 2: Mastering Universal User Interface Understanding Across Platforms

Fewer May Be Better: Enhancing Offline Reinforcement Learning with Reduced Dataset

F-Fidelity: A Robust Framework for Faithfulness Evaluation of Explainable AI

Fictitious Synthetic Data Can Improve LLM Factuality via Prerequisite Learning

FIG: Flow with Interpolant Guidance for Linear Inverse Problems

Finally Rank-Breaking Conquers MNL Bandits: Optimal and Efficient Algorithms for MNL Assortment

Finding and Only Finding Differential Nash Equilibria by Both Pretending to be a Follower

Finding Shared Decodable Concepts and their Negations in the Brain

Fine-Tuning Attention Modules Only: Enhancing Weight Disentanglement in Task Arithmetic

Fine-tuning can cripple your foundation model; preserving features may be the solution

Fine-tuning can Help Detect Pretraining Data from Large Language Models

Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design

Fine-Tuning Token-Based Large Multimodal Models: What Works, What Doesn’t and What's Next

Fine-tuning with Reserved Majority for Noise Reduction

First-Person Fairness in Chatbots

Fitting Networks with a Cancellation Trick

FlashMask: Efficient and Rich Mask Extension of FlashAttention

Flat Reward in Policy Parameter Space Implies Robust Reinforcement Learning

Flavors of Margin: Implicit Bias of Steepest Descent in Homogeneous Neural Networks

FlexPrefill: A Context-Aware Sparse Attention Mechanism for Efficient Long-Sequence Inference

FLOPS: Forward Learning with OPtimal Sampling

Flow-based Variational Mutual Information: Fast and Flexible Approximations

Flow matching achieves almost minimax optimal convergence

Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective

Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokens

Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems

For Better or For Worse? Learning Minimum Variance Features With Label Augmentation

Forewarned is Forearmed: Harnessing LLMs for Data Synthesis via Failure-induced Exploration

Forget the Data and Fine-Tuning! Just Fold the Network to Compress

Forking Paths in Neural Text Generation

Formation of Representations in Neural Networks

Forte : Finding Outliers with Representation Typicality Estimation

FOSP: Fine-tuning Offline Safe Policy through World Models

Foundation Models Secretly Understand Neural Network Weights: Enhancing Hypernetwork Architectures with Foundation Models

Fourier Head: Helping Large Language Models Learn Complex Probability Distributions

FreeCG: Free the Design Space of Clebsch-Gordan Transform for Machine Learning Force Fields

Free Hunch: Denoiser Covariance Estimation for Diffusion Models Without Extra Costs

FreeVS: Generative View Synthesis on Free Driving Trajectory

Frequency-Guided Masking for Enhanced Vision Self-Supervised Learning

FreSh: Frequency Shifting for Accelerated Neural Representation Learning

From Artificial Needles to Real Haystacks: Improving Retrieval Capabilities in LLMs by Finetuning on Synthetic Data

From Attention to Activation: Unraveling the Enigmas of Large Language Models

From Complexity to Clarity: Analytical Expressions of Deep Neural Network Weights via Clifford Algebra and Convexity

From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation

From Isolated Conversations to Hierarchical Schemas: Dynamic Tree Memory Representation for LLMs

From Layers to States: A State Space Model Perspective to Deep Neural Network Layer Dynamics

From Lazy to Rich: Exact Learning Dynamics in Deep Linear Networks

From Models to Microtheories: Distilling a Model's Topical Knowledge for Grounded Question-Answering

From Pixels to Tokens: Byte-Pair Encoding on Quantized Visual Modalities

From Probability to Counterfactuals: the Increasing Complexity of Satisfiability in Pearl's Causal Hierarchy

From Promise to Practice: Realizing High-performance Decentralized Training

From Risk to Uncertainty: Generating Predictive Uncertainty Measures via Bayesian Estimation

From Tokens to Lattices: Emergent Lattice Structures in Language Models

From Tokens to Words: On the Inner Lexicon of LLMs

Fully-inductive Node Classification on Arbitrary Graphs

Functional Homotopy: Smoothing Discrete Optimization via Continuous Parameters for LLM Jailbreak Attacks

Fundamental Limitations on Subquadratic Alternatives to Transformers

Fundamental Limits of Prompt Tuning Transformers: Universality, Capacity and Efficiency

GALA: Geometry-Aware Local Adaptive Grids for Detailed 3D Generation

GameArena: Evaluating LLM Reasoning through Live Computer Games

Gap Preserving Distillation by Building Bidirectional Mappings with A Dynamic Teacher

Gated Delta Networks: Improving Mamba2 with Delta Rule

GaussianAnything: Interactive Point Cloud Flow Matching for 3D Generation

GaussianBlock: Building Part-Aware Compositional and Editable 3D Scene by Primitives and Gaussians

Gaussian-Det: Learning Closed-Surface Gaussians for 3D Object Detection

Gaussian Differentially Private Human Faces Under a Face Radial Curve Representation

Gaussian Splatting Lucas-Kanade

GDrag:Towards General-Purpose Interactive Editing with Anti-ambiguity Point Diffusion

GenARM: Reward Guided Generation with Autoregressive Reward Model for Test-Time Alignment

Generalizability of Neural Networks Minimizing Empirical Risk Based on Expressive Power

Generalization Bounds for Canonicalization: A Comparative Study with Group Averaging

Generalization, Expressivity, and Universality of Graph Neural Networks on Attributed Graphs

Generalization through variance: how noise shapes inductive biases in diffusion models

Generalized Behavior Learning from Diverse Demonstrations

Generalized Consistency Trajectory Models for Image Manipulation

Generalizing Reasoning Problems to Longer Lengths

General Scene Adaptation for Vision-and-Language Navigation

Generating CAD Code with Vision-Language Models for 3D Designs

Generating Freeform Endoskeletal Robots

Generating Graphs via Spectral Diffusion

Generating with Confidence: Uncertainty Quantification for Black-box Large Language Models

Generation and Comprehension Hand-in-Hand: Vision-guided Expression Diffusion for Boosting Referring Expression Generation and Comprehension

Generative Classifiers Avoid Shortcut Solutions

Generative Inbetweening: Adapting Image-to-Video Models for Keyframe Interpolation

Generative Verifiers: Reward Modeling as Next-Token Prediction

Generative World Explorer

Generator Matching: Generative modeling with arbitrary Markov processes

GenSE: Generative Speech Enhancement via Language Models using Hierarchical Modeling

GenVP: Generating Visual Puzzles with Contrastive Hierarchical VAEs

GeoLoRA: Geometric integration for parameter efficient fine-tuning

Geometric Inductive Biases of Deep Networks: The Role of Data and Architecture

Geometry-Aware Approaches for Balancing Performance and Theoretical Guarantees in Linear Bandits

Geometry Image Diffusion: Fast and Data-Efficient Text-to-3D with Image-Based Surface Representation

Geometry of Long-Tailed Representation Learning: Rebalancing Features for Skewed Distributions

GeoX: Geometric Problem Solving Through Unified Formalized Vision-Language Pre-training

GI-GS: Global Illumination Decomposition on Gaussian Splatting for Inverse Rendering

Glad: A Streaming Scene Generator for Autonomous Driving

Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection

G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language Model

Global Convergence of Policy Gradient in Average Reward MDPs

Global Identifiability of Overcomplete Dictionary Learning via L1 and Volume Minimization

Global Well-posedness and Convergence Analysis of Score-based Generative Models via Sharp Lipschitz Estimates

GLOMA: Global Video Text Spotting with Morphological Association

GOAL: A Generalist Combinatorial Optimization Agent Learner

GOFA: A Generative One-For-All Model for Joint Graph Language Modeling

GoodDrag: Towards Good Practices for Drag Editing with Diffusion Models

GotenNet: Rethinking Efficient 3D Equivariant Graph Neural Networks

GPromptShield: Elevating Resilience in Graph Prompt Tuning Against Adversarial Attacks

GPUDrive: Data-driven, multi-agent driving simulation at 1 million FPS

Gradient correlation is a key ingredient to accelerate SGD with momentum

Gradient descent with generalized Newton’s method

Gradient-Free Generation for Hard-Constrained Systems

GRAIN: Exact Graph Reconstruction from Gradients

Grammar Reinforcement Learning: path and cycle counting in graphs with a Context-Free Grammar and Transformer approach

GraphArena: Evaluating and Exploring Large Language Models on Graph Computation

Graph-based Document Structure Analysis

GraphEval: A Lightweight Graph-Based LLM Framework for Idea Evaluation

Graph Neural Networks Gone Hogwild

Graph Neural Ricci Flow: Evolving Feature from a Curvature Perspective

GraphRouter: A Graph-based Router for LLM Selections

Graph Transformers Dream of Electric Flow

GReaTer: Gradients Over Reasoning Makes Smaller Language Models Strong Prompt Optimizers

Greener GRASS: Enhancing GNNs with Encoding, Rewiring, and Attention

gRNAde: Geometric Deep Learning for 3D RNA inverse design

Grokking at the Edge of Numerical Stability

GROOT-2: Weakly Supervised Multimodal Instruction Following Agents

Grounding Multimodal Large Language Model in GUI World

Grounding Video Models to Actions through Goal Conditioned Exploration

Group Distributionally Robust Dataset Distillation with Risk Minimization

Group Downsampling with Equivariant Anti-aliasing

Group Ligands Docking to Protein Pockets

GSBA$^K$: $top$-$K$ Geometric Score-based Black-box Attack

GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models

GTR: Improving Large 3D Reconstruction Models through Geometry and Texture Refinement

Guided Score identity Distillation for Data-Free One-Step Text-to-Image Generation

h4rm3l: A Language for Composable Jailbreak Attack Synthesis

HaDeMiF: Hallucination Detection and Mitigation in Large Language Models

HALL-E: Hierarchical Neural Codec Language Model for Minute-Long Zero-Shot Text-to-Speech Synthesis

HAMSTER: Hierarchical Action Models for Open-World Robot Manipulation

Handling Delay in Real-Time Reinforcement Learning

HARDMath: A Benchmark Dataset for Challenging Problems in Applied Mathematics

Has the Deep Neural Network learned the Stochastic Process? An Evaluation Viewpoint

Heavy-Tailed Diffusion Models

HelpSteer2-Preference: Complementing Ratings with Preferences

Herald: A Natural Language Annotated Lean 4 Dataset

HERO: Human-Feedback Efficient Reinforcement Learning for Online Diffusion Model Finetuning

Hessian Free Efficient Single Loop Iterative Differentiation Methods for Bi-Level Optimization Problems

Hessian-Free Online Certified Unlearning

HexGen-2: Disaggregated Generative Inference of LLMs in Heterogeneous Environment

HGM³: Hierarchical Generative Masked Motion Modeling with Hard Token Mining

HiBug2: Efficient and Interpretable Error Slice Discovery for Comprehensive Model Debugging

Hierarchical Uncertainty Estimation for Learning-based Registration in Neuroimaging

High-dimensional Analysis of Knowledge Distillation: Weak-to-Strong Generalization and Scaling Laws

High-Dimensional Bayesian Optimisation with Gaussian Process Prior Variational Autoencoders

Higher-Order Graphon Neural Networks: Approximation and Cut Distance

Highly Efficient Self-Adaptive Reward Shaping for Reinforcement Learning

High-Precision Dichotomous Image Segmentation via Probing Diffusion Capacity

High-quality Text-to-3D Character Generation with SparseCubes and Sparse Transformers.

HiSplat: Hierarchical 3D Gaussian Splatting for Generalizable Sparse-View Reconstruction

HMoRA: Making LLMs More Effective with Hierarchical Mixture of LoRA Experts

Holistically Evaluating the Environmental Impact of Creating Language Models

Holistic Reasoning with Long-Context LMs: A Benchmark for Database Operations on Massive Textual Data

Holographic Node Representations: Pre-training Task-Agnostic Node Embeddings

Hot-pluggable Federated Learning: Bridging General and Personalized FL via Dynamic Selection

How Discrete and Continuous Diffusion Meet: Comprehensive Analysis of Discrete Diffusion Models via a Stochastic Integral Framework

How DNNs break the Curse of Dimensionality: Compositionality and Symmetry Learning

How Does Vision-Language Adaptation Impact the Safety of Vision Language Models?

How do we interpret the outputs of a neural network trained on classification?

How Far Are We from True Unlearnability?

How Feature Learning Can Improve Neural Scaling Laws

How Gradient descent balances features: A dynamical analysis for two-layer neural networks

How Learnable Grids Recover Fine Detail in Low Dimensions: A Neural Tangent Kernel Analysis of Multigrid Parametric Encodings

How many samples are needed to train a deep neural network?

How Much is a Noisy Image Worth? Data Scaling Laws for Ambient Diffusion.

How Much is Unseen Depends Chiefly on Information About the Seen

How new data permeates LLM knowledge and how to dilute it

How to Evaluate Reward Models for RLHF

How to Find the Exact Pareto Front for Multi-Objective MDPs?

How to Verify Any (Reasonable) Distribution Property: Computationally Sound Argument Systems for Distributions

How Two-Layer Neural Networks Learn, One (Giant) Step at a Time

HQ-Edit: A High-Quality Dataset for Instruction-based Image Editing

Human-Aligned Chess With a Bit of Search

Human-inspired Episodic Memory for Infinite Context LLMs

Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment

Hymba: A Hybrid-head Architecture for Small Language Models

Hyperbolic Genome Embeddings

HyperDAS: Towards Automating Mechanistic Interpretability with Hypernetworks

HyperFace: Generating Synthetic Face Recognition Datasets by Exploring Face Embedding Hypersphere

Hypothetical Minds: Scaffolding Theory of Mind for Multi-Agent Tasks with Large Language Models

I2VControl-Camera: Precise Video Camera Control with Adjustable Motion Strength

I Can Hear You: Selective Robust Training for Deepfake Audio Detection

ICLR: In-Context Learning of Representations

IDArb: Intrinsic Decomposition for Arbitrary Number of Input Views and Illuminations

Identifiability for Gaussian Processes with Holomorphic Kernels

Identifying latent state transitions in non-linear dynamical systems

IGL-Bench: Establishing the Comprehensive Benchmark for Imbalanced Graph Learning

Image and Video Tokenization with Binary Spherical Quantization

Image-level Memorization Detection via Inversion-based Inference Perturbation

IMDPrompter: Adapting SAM to Image Manipulation Detection by Cross-View Automated Prompt Learning

Immunogenicity Prediction with Dual Attention Enables Vaccine Target Selection

Implicit Neural Surface Deformation with Explicit Velocity Fields

Improved Algorithms for Kernel Matrix-Vector Multiplication Under Sparsity Assumptions

Improved Approximation Algorithms for $k$-Submodular Maximization via Multilinear Extension

Improved Convergence Rate for Diffusion Probabilistic Models

Improved Diffusion-based Generative Model with Better Adversarial Robustness

Improved Finite-Particle Convergence Rates for Stein Variational Gradient Descent

Improved Regret Bounds for Linear Adversarial MDPs via Linear Optimization

Improved Techniques for Optimization-Based Jailbreaking on Large Language Models

ImProver: Agent-Based Automated Proof Optimization

Improving Data Efficiency via Curating LLM-Driven Rating Systems

Improving Deep Regression with Tightness

Improving Equivariant Networks with Probabilistic Symmetry Breaking

Improving Generalization and Robustness in SNNs Through Signed Rate Encoding and Sparse Encoding Attacks

Improving Graph Neural Networks by Learning Continuous Edge Directions

Improving Instruction-Following in Language Models through Activation Steering

Improving Long-Text Alignment for Text-to-Image Diffusion Models

Improving Neural Network Accuracy by Concurrently Training with a Twin Network

Improving Pretraining Data Using Perplexity Correlations

Improving Reasoning Performance in Large Language Models via Representation Engineering

Improving Semantic Understanding in Speech Language Models via Brain-tuning

Improving Uncertainty Estimation through Semantically Diverse Language Generation

Improving Unsupervised Constituency Parsing via Maximizing Semantic Information

In-Context Editing: Learning Knowledge from Self-Induced Distributions

In-context Time Series Predictor

Incremental Causal Effect for Time to Treatment Initialization

INFER: A Neural-symbolic Model For Extrapolation Reasoning on Temporal Knowledge Graph

Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models

Inference Scaling for Long-Context Retrieval Augmented Generation

Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for LLM Problem-Solving

Infilling Score: A Pretraining Data Detection Algorithm for Large Language Models

Infinite-Resolution Integral Noise Warping for Diffusion Models

InfoGS: Efficient Structure-Aware 3D Gaussians via Lightweight Information Shaping

Information Theoretic Text-to-Image Alignment

Injecting Universal Jailbreak Backdoors into LLMs in Minutes

Innovative Thinking, Infinite Humor: Humor Research of Large Language Models through Structured Thought Leaps

Input Space Mode Connectivity in Deep Neural Networks

In Search of Forgotten Domain Generalization

INS: Interaction-aware Synthesis to Enhance Offline Multi-agent Reinforcement Learning

Inspection and Control of Self-Generated-Text Recognition Ability in Llama3-8b-Instruct

Instant Policy: In-Context Imitation Learning via Graph Diffusion

InstantPortrait: One-Step Portrait Editing via Diffusion Multi-Objective Distillation

InstaSHAP: Interpretable Additive Models Explain Shapley Values Instantly

Instructional Segment Embedding: Improving LLM Safety with Instruction Hierarchy

Integrative Decoding: Improving Factuality via Implicit Self-consistency

Interactive Adjustment for Human Trajectory Prediction with Individual Feedback

Interactive Speculative Planning: Enhance Agent Efficiency through Co-design of System and User Interface

Interference Among First-Price Pacing Equilibria: A Bias and Variance Analysis

Intermediate Layer Classifiers for OOD generalization

InterMask: 3D Human Interaction Generation via Collaborative Masked Modeling

Interpretable Bilingual Multimodal Large Language Model for Diverse Biomedical Tasks

Interpretable Compressed Descriptions For Image Generation

Interpretable Unsupervised Joint Denoising and Enhancement for Real-World low-light Scenarios

Interpretable Vision-Language Survival Analysis with Ordinal Inductive Bias for Computational Pathology

Interpreting the Second-Order Effects of Neurons in CLIP

IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning

Intervening Anchor Token: Decoding Strategy in Alleviating Hallucinations for MLLMs

Intrinsic User-Centric Interpretability through Global Mixture of Experts

Inverse Attention Agents for Multi-Agent Systems

Inverse Constitutional AI: Compressing Preferences into Principles

Inverse decision-making using neural amortized Bayesian actors

Inverse Rendering using Multi-Bounce Path Tracing and Reservoir Sampling

Inverse Scaling: When Bigger Isn't Better

IPDreamer: Appearance-Controllable 3D Object Generation with Complex Image Prompts

IRIS: LLM-Assisted Static Analysis for Detecting Security Vulnerabilities

Is Factuality Enhancement a Free Lunch For LLMs? Better Factuality Can Lead to Worse Context-Faithfulness

Is Large-scale Pretraining the Secret to Good Domain Generalization?

Is Your Multimodal Language Model Oversensitive to Safe Queries?

Iterative Dual-RL: An Optimal Discriminator Weighted Imitation Perspective for Reinforcement Learning

Iterative Label Refinement Matters More than Preference Optimization under Weak Supervision

It Helps to Take a Second Opinion: Teaching Smaller LLMs To Deliberate Mutually via Selective Rationale Optimisation

JetFormer: An autoregressive generative model of raw images and text

Joint Reward and Policy Learning with Demonstrations and Human Feedback Improves Alignment

Judge Decoding: Faster Speculative Sampling Requires Going Beyond Model Alignment

Jump Your Steps: Optimizing Sampling Schedule of Discrete Diffusion Models

KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language Models

Kernel-based Optimally Weighted Conformal Time-Series Prediction

KGARevion: An AI Agent for Knowledge-Intensive Biomedical QA

Kinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control Tasks

KinFormer: Generalizable Dynamical Symbolic Regression for Catalytic Organic Reaction Kinetics

kNN Attention Demystified: A Theoretical Exploration for Scalable Transformers

Knowledge Entropy Decay during Language Model Pretraining Hinders New Knowledge Acquisition

Kolmogorov-Arnold Transformer

Kronecker Mask and Interpretive Prompts are Language-Action Video Learners

LaGeM: A Large Geometry Model for 3D Representation Learning and Diffusion

LaMP: Language-Motion Pretraining for Motion Generation, Retrieval, and Captioning

Language Guided Skill Discovery

Language-Image Models with 3D Understanding

Language Model Alignment in Multilingual Trolley Problems

Language Models are Advanced Anonymizers

Language Models Learn to Mislead Humans via RLHF

Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice

Language Representations Can be What Recommenders Need: Findings and Potentials

LANTERN: Accelerating Visual Autoregressive Models with Relaxed Speculative Decoding

Laplace Sample Information: Data Informativeness Through a Bayesian Lens

Large Language Models Assume People are More Rational than We Really are

Large Language Models can Become Strong Self-Detoxifiers

Large Language Models Often Say One Thing and Do Another

Large Scale Knowledge Washing

Large (Vision) Language Models are Unsupervised In-Context Learners

LARP: Tokenizing Videos with a Learned Autoregressive Generative Prior

LASER: A Neuro-Symbolic Framework for Learning Spatio-Temporal Scene Graphs with Weak Supervision

LASeR: Towards Diversified and Generalizable Robot Design with Large Language Models

Last Iterate Convergence of Incremental Methods as a Model of Forgetting

Last-Iterate Convergence Properties of Regret-Matching Algorithms in Games

Latent Action Pretraining from Videos

Latent Bayesian Optimization via Autoregressive Normalizing Flows

Latent-EnSF: A Latent Ensemble Score Filter for High-Dimensional Data Assimilation with Sparse Observation Data

Latent Radiance Fields with 3D-aware 2D Representations

Latent Safety-Constrained Policy Approach for Safe Offline Reinforcement Learning

Law of the Weakest Link: Cross Capabilities of Large Language Models

Lawma: The Power of Specialization for Legal Annotation

LayerDAG: A Layerwise Autoregressive Diffusion Model for Directed Acyclic Graph Generation

Layer Swapping for Zero-Shot Cross-Lingual Transfer in Large Language Models

Layout-your-3D: Controllable and Precise 3D Generation with 2D Blueprint

LDAdam: Adaptive Optimization from Low-Dimensional Gradient Statistics

LeanAgent: Lifelong Learning for Formal Theorem Proving

LeanQuant: Accurate and Scalable Large Language Model Quantization with Loss-error-aware Grid

Lean-STaR: Learning to Interleave Thinking and Proving

Learn-by-interact: A Data-Centric Framework For Self-Adaptive Agents in Realistic Environments

Learn hybrid prototypes for multivariate time series anomaly detection

Learning 3D Perception from Others' Predictions

Learning a Fast Mixing Exogenous Block MDP using a Single Trajectory

Learning-Augmented Frequent Directions

Learning-Augmented Search Data Structures

Learning Causal Alignment for Reliable Disease Diagnosis

Learning Chaos In A Linear Way

Learning Clustering-based Prototypes for Compositional Zero-Shot Learning

Learning Color Equivariant Representations

Learning Diagrams: A Graphical Language for Compositional Training Regimes

Learning Diverse Attacks on Large Language Models for Robust Red-Teaming and Safety Tuning

Learning Dynamics of Deep Matrix Factorization Beyond the Edge of Stability

Learning Dynamics of LLM Finetuning

Learning Equivariant Non-Local Electron Density Functionals

Learning Fine-Grained Representations through Textual Token Disentanglement in Composed Video Retrieval

Learning from End User Data with Shuffled Differential Privacy over Kernel Densities

Learning from negative feedback, or positive feedback or both

Learning General-purpose Biomedical Volume Representations using Randomized Synthesis

Learning Graph Quantized Tokenizers

Learning-Guided Rolling Horizon Optimization for Long-Horizon Flexible Job-Shop Scheduling

Learning Hierarchical Polynomials of Multiple Nonlinear Features

Learning High-Degree Parities: The Crucial Role of the Initialization

Learning How Hard to Think: Input-Adaptive Allocation of LM Computation

Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data

Learning LLM-as-a-Judge for Preference Alignment

Learning Mask Invariant Mutual Information for Masked Image Modeling

Learning mirror maps in policy mirror descent

Learning Molecular Representation in a Cell

Learning Multi-Index Models with Neural Networks via Mean-Field Langevin Dynamics

Learning Neural Networks with Distribution Shift: Efficiently Certifiable Guarantees

Learning Partial Graph Matching via Optimal Partial Transport

Learning Randomized Algorithms with Transformers

Learning Regularized Graphon Mean-Field Games with Unknown Graphons

Learning Spatial-Semantic Features for Robust Video Object Segmentation

Learning Spatiotemporal Dynamical Systems from Point Process Observations

Learning Splitting Heuristics in Divide-and-Conquer SAT Solvers with Reinforcement Learning

Learning Structured Universe Graph with Outlier OOD Detection for Partial Matching

Learning Successor Features with Distributed Hebbian Temporal Memory

Learning system dynamics without forgetting

Learning to Adapt Frozen CLIP for Few-Shot Test-Time Domain Adaptation

Learning to Clarify: Multi-turn Conversations with Action-Based Contrastive Self-Training

Learning to Discretize Denoising Diffusion ODEs

Learning to engineer protein flexibility

Learning to Search from Demonstration Sequences

Learning to Select Nodes in Branch and Bound with Sufficient Tree Representation

Learning to Solve Differential Equation Constrained Optimization Problems

Learning to Steer Markovian Agents under Model Uncertainty

Learning under Temporal Label Noise

Learning Video-Conditioned Policy on Unlabelled Data with Joint Embedding Predictive Transformer

Leave-One-Out Stable Conformal Prediction

Less is More: Masking Elements in Image Condition Features Avoids Content Leakages in Style Transfer Diffusion Models

Let Me Grok for You: Accelerating Grokking via Embedding Transfer from a Weaker Model

Let the Code LLM Edit Itself When You Edit the Code

Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Prediction

Leveraging Flatness to Improve Information-Theoretic Generalization Bounds for SGD

Leveraging Submodule Linearity Enhances Task Arithmetic Performance in LLMs

Leveraging Variable Sparsity to Refine Pareto Stationarity in Multi-Objective Optimization

LICO: Large Language Models for In-Context Molecular Optimization

LICORICE: Label-Efficient Concept-Based Interpretable Reinforcement Learning

Lie Algebra Canonicalization: Equivariant Neural Operators under arbitrary Lie Groups

LIFe-GoM: Generalizable Human Rendering with Learned Iterative Feedback Over Multi-Resolution Gaussians-on-Mesh

Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space

Lightweight Predictive 3D Gaussian Splats

Linear Combination of Saved Checkpoints Makes Consistency and Diffusion Models Better

Linear combinations of latents in generative models: subspaces and beyond

Linear Multistep Solver Distillation for Fast Sampling of Diffusion Models

Linear Representations of Political Perspective Emerge in Large Language Models

Linear SCM Identification in the Presence of Confounders and Gaussian Noise

Linear Transformer Topological Masking with Graph Random Features

Lines of Thought in Large Language Models

Lipschitz Bandits in Optimal Space

LiveBench: A Challenging, Contamination-Limited LLM Benchmark

LLaMaFlex: Many-in-one LLMs via Generalized Pruning and Weight Sharing

LLaMA-Omni: Seamless Speech Interaction with Large Language Models

LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One Vision Token

LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models

LLMs Can Plan Only If We Tell Them

LLMs Know More Than They Show: On the Intrinsic Representation of LLM Hallucinations

LLM-SR: Scientific Equation Discovery via Programming with Large Language Models

LLM Unlearning via Loss Adjustment with Only Forget Data

Locality Alignment Improves Vision-Language Models

Locality-aware Gaussian Compression for Fast and High-quality Rendering

Locality Sensitive Avatars From Video

Local Loss Optimization in the Infinite Width: Stable Parameterization of Predictive Coding Networks and Target Propagation

Local Patterns Generalize Better for Novel Anomalies

LocoVR: Multiuser Indoor Locomotion Dataset in Virtual Reality

Logically Consistent Language Models via Neuro-Symbolic Integration

Logicbreaks: A Framework for Understanding Subversion of Rule-based Inference

Logic-Logit: A Logic-Based Approach to Choice Modeling

LOIRE: LifelOng learning on Incremental data via pre-trained language model gRowth Efficiently

LoLCATs: On Low-Rank Linearizing of Large Language Models

Long-Context Linear System Identification

Long-Context LLMs Meet RAG: Overcoming Challenges for Long Inputs in RAG

Long-horizon Visual Instruction Generation with Logic and Attribute Self-reflection

Longhorn: State Space Models are Amortized Online Learners

LongMamba: Enhancing Mamba's Long-Context Capabilities via Training-Free Receptive Field Enlargement

Long-Short Decision Transformer: Bridging Global and Local Dependencies for Generalized Decision-Making

Long-time asymptotics of noisy SVGD outside the population limit

LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs

Look Before You Leap: Universal Emergent Mechanism for Retrieval in Language Models

Looking Backward: Streaming Video-to-Video Translation with Feature Banks

Looking into User’s Long-term Interests through the Lens of Conservative Evidential Learning

Looking Inward: Language Models Can Learn About Themselves by Introspection

Loopy: Taming Audio-Driven Portrait Avatar with Long-Term Motion Dependency

LoRA Learns Less and Forgets Less

LoRA-Pro: Are Low-Rank Adapters Properly Optimized?

LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation

Lossy Compression with Pretrained Diffusion Models

Lost in Prediction: Why Social Media Narratives Don't Help Macroeconomic Forecasting?

Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction

LucidPPN: Unambiguous Prototypical Parts Network for User-centric Interpretable Computer Vision

L-WISE: Boosting human visual category learning through model-based image selection and enhancement

M^3PC: Test-time Model Predictive Control using Pretrained Masked Trajectory Model

Machine Unlearning Fails to Remove Data Poisoning Attacks

Machine Unlearning via Simulated Oracle Matching

MACPO: Weak-to-Strong Alignment via Multi-Agent Contrastive Preference Optimization

MADGEN: Mass-Spec attends to De Novo Molecular generation

MAD-TD: Model-Augmented Data stabilizes High Update Ratio RL

MAESTRO: Masked Encoding Set Transformer with Self-Distillation

MaestroMotif: Skill Design from Artificial Intelligence Feedback

MagicPIG: LSH Sampling for Efficient LLM Generation

Magnetic Preference Optimization: Achieving Last-iterate Convergence for Language Model Alignment

MAGNet: Motif-Agnostic Generation of Molecules from Scaffolds

Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing

Maintaining Structural Integrity in Parameter Spaces for Parameter Efficient Fine-tuning

Make Haste Slowly: A Theory of Emergent Structured Mixed Selectivity in Feature Learning ReLU Networks

MallowsPO: Fine-Tune Your LLM with Preference Dispersions

MambaExtend: A Training-Free Approach to Improve Long Context Extension of Mamba

MambaPEFT: Exploring Parameter-Efficient Fine-Tuning for Mamba

MamBEV: Enabling State Space Models to Learn Birds-Eye-View Representations

MamKO: Mamba-based Koopman operator for modeling and predictive control

ManiSkill-HAB: A Benchmark for Low-Level Manipulation in Home Rearrangement Tasks

MANTRA: The Manifold Triangulations Assemblage

Many-Objective Multi-Solution Transport

MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation

MAPS: Advancing Multi-Modal Reasoning in Expert-Level Physical Science

MA-RLHF: Reinforcement Learning from Human Feedback with Macro Actions

MaskBit: Embedding-free Image Generation via Bit Tokens

Mask-DPO: Generalizable Fine-grained Factuality Alignment of LLMs

Masked Diffusion Models are Secretly Time-Agnostic Masked Models and Exploit Inaccurate Categorical Sampling

MaskGCT: Zero-Shot Text-to-Speech with Masked Generative Codec Transformer

Mask in the Mirror: Implicit Sparsification

Mastering Task Arithmetic: $\tau$Jp as a Key Indicator for Weight Disentanglement

Matcha: Mitigating Graph Structure Shifts with Test-Time Adaptation

MathCoder2: Better Math Reasoning from Continued Pretraining on Model-translated Mathematical Code

MatryoshkaKV: Adaptive KV Compression via Trainable Orthogonal Projection

Matryoshka Multimodal Models

MAVIS: Mathematical Visual Instruction Tuning with an Automatic Data Engine

Maximizing the Potential of Synthetic Data: Insights from Random Matrix Theory

MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization

McEval: Massively Multilingual Code Evaluation

Measuring memorization in RLHF for code completion

Mechanism and emergence of stacked attention heads in multi-layer transformers

Mechanistic Interpretability Meets Vision Language Models: Insights and Limitations

MediConfusion: Can you trust your AI radiologist? Probing the reliability of multimodal medical foundation models

MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine

Memory Efficient Transformer Adapter for Dense Predictions

Memory Mosaics

Mentored Learning: Improving Generalization and Convergence of Student Learner

metabench - A Sparse Benchmark of Reasoning and Knowledge in Large Language Models

Meta-Continual Learning of Neural Fields

MetaDesigner: Advancing Artistic Typography through AI-Driven, User-Centric, and Multilingual WordArt Synthesis

Meta-Dynamical State Space Models for Integrative Neural Data Analysis

MetaMetrics: Calibrating Metrics for Generation Tasks Using Human Preferences

Metamizer: A Versatile Neural Optimizer for Fast and Accurate Physics Simulations

MetaOOD: Automatic Selection of OOD Detection Models

MetaUrban: An Embodied AI Simulation Platform for Urban Micromobility

MeteoRA: Multiple-tasks Embedded LoRA for Large Language Models

MeToken: Uniform Micro-environment Token Boosts Post-Translational Modification Prediction

MGDA Converges under Generalized Smoothness, Provably

MIND: Math Informed syNthetic Dialogues for Pretraining LLMs

MIND over Body: Adaptive Thinking using Dynamic Computation

MindSimulator: Exploring Brain Concept Localization via Synthetic fMRI

Mind the Gap: Examining the Self-Improvement Capabilities of Large Language Models

Mind the GAP: Glimpse-based Active Perception improves generalization and sample efficiency of visual reasoning

Mini-batch Coresets for Memory-efficient Language Model Training on Data Mixtures

Minimalistic Predictions for Online Class Constraint Scheduling

Minimax Optimal Two-Stage Algorithm For Moment Estimation Under Covariate Shift

Mini-Monkey: Alleviating the Semantic Sawtooth Effect for Lightweight MLLMs via Complementary Image Pyramid

MIRACLE 3D: Memory-efficient Integrated Robust Approach for Continual Learning on 3D Point Clouds via Shape Model Construction

(Mis)Fitting Scaling Laws: A Survey of Scaling Law Fitting Techniques in Deep Learning

Misspecified $Q$-Learning with Sparse Linear Function Approximation: Tight Bounds on Approximation Error

Mitigate the Gap: Improving Cross-Modal Alignment in CLIP

Mitigating Hallucination in Large Vision-Language Models via Modular Attribution and Intervention

Mitigating Reward Over-Optimization in RLHF via Behavior-Supported Regularization

Mitigating the Backdoor Effect for Multi-Task Model Merging via Safety-Aware Subspace

Mix-CPT: A Domain Adaptation Framework via Decoupling Knowledge Learning and Format Alignment

Mix-LN: Unleashing the Power of Deeper Layers by Combining Pre-LN and Post-LN

Mixture Compressor for Mixture-of-Experts LLMs Gains More

Mixture-of-Agents Enhances Large Language Model Capabilities

Mixture of Attentions For Speculative Decoding

Mixture of Experts Made Personalized: Federated Prompt Learning for Vision-Language Models

Mixture of In-Context Prompters for Tabular PFNs

MLLM as Retriever: Interactively Learning Multimodal Retrieval for Embodied Agents

MLPs Learn In-Context on Regression and Classification Tasks

MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning

MMAD: A Comprehensive Benchmark for Multimodal Large Language Models in Industrial Anomaly Detection

MMEgo: Towards Building Egocentric Multimodal LLMs for Video QA

MM-EMBED: UNIVERSAL MULTIMODAL RETRIEVAL WITH MULTIMODAL LLMS

MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?

MMFakeBench: A Mixed-Source Multimodal Misinformation Detection Benchmark for LVLMs

MMIU: Multimodal Multi-image Understanding for Evaluating Large Vision-Language Models

MMKE-Bench: A Multimodal Editing Benchmark for Diverse Visual Knowledge

MMRole: A Comprehensive Framework for Developing and Evaluating Multimodal Role-Playing Agents

MMWorld: Towards Multi-discipline Multi-faceted World Model Evaluation in Videos

MOCA: Self-supervised Representation Learning by Predicting Masked Online Codebook Assignments

MoDeGPT: Modular Decomposition for Large Language Model Compression

Model-agnostic meta-learners for estimating heterogeneous treatment effects over time

Model Equality Testing: Which Model is this API Serving?

Model-Free Offline Reinforcement Learning with Enhanced Robustness

Modeling Fine-Grained Hand-Object Dynamics for Egocentric Video Representation Learning

Modeling Future Conversation Turns to Teach LLMs to Ask Clarifying Questions

Modeling Unseen Environments with Language-guided Composable Causal Components in Reinforcement Learning

Model merging with SVD to tie the Knots

MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts

MoLEx: Mixture of Layer Experts for Fine-tuning with Sparse Upcycling

Moner: Motion Correction in Undersampled Radial MRI with Unsupervised Neural Representation

Monitoring Latent World States in Language Models with Propositional Probes

MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion

Montessori-Instruct: Generate Influential Training Data Tailored for Student Learning

MOOSE-Chem: Large Language Models for Rediscovering Unseen Chemistry Scientific Hypotheses

Moral Alignment for LLM Agents

More RLHF, More Trust? On The Impact of Preference Alignment On Trustworthiness

MorphoDiff: Cellular Morphology Painting with Diffusion Models

MOS: Model Synergy for Test-Time Adaptation on LiDAR-Based 3D Object Detection

MotherNet: Fast Training and Inference via Hyper-Network Transformers

Motion-Agent: A Conversational Framework for Human Motion Generation with LLMs

MotionClone: Training-Free Motion Cloning for Controllable Video Generation

Motion Control of High-Dimensional Musculoskeletal Systems with Hierarchical Model-Based Planning

MotionDreamer: One-to-Many Motion Synthesis with Localized Generative Masked Transformer

mPLUG-Owl3: Towards Long Image-Sequence Understanding in Multi-Modal Large Language Models

MP-Mat: A 3D-and-Instance-Aware Human Matting and Editing Framework with Multiplane Representation

MR-GSM8K: A Meta-Reasoning Benchmark for Large Language Model Evaluation

MrSteve: Instruction-Following Agents in Minecraft with What-Where-When Memory

MS-Diffusion: Multi-subject Zero-shot Image Personalization with Layout Guidance

MTU-Bench: A Multi-granularity Tool-Use Benchmark for Large Language Models

Mufu: Multilingual Fused Learning for Low-Resource Translation with LLM

MuHBoost: Multi-Label Boosting For Practical Longitudinal Human Behavior Modeling

Multi-Accurate CATE is Robust to Unknown Covariate Shifts

Multiagent Finetuning: Self Improvement with Diverse Reasoning Chains

Multi-Draft Speculative Sampling: Canonical Decomposition and Theoretical Limits

Multi-Field Adaptive Retrieval

Multi-Label Node Classification with Label Influence Propagation

Multi-Label Test-Time Adaptation with Bound Entropy Minimization

Multi-LLM-Agents Debate - Performance, Efficiency, and Scaling Challenges

Multi-modal brain encoding models for multi-modal stimuli

Multi-modal Learning: A Look Back and the Road Ahead

Multimodal Lego: Model Merging and Fine-Tuning Across Topologies and Modalities in Biomedicine

Multimodal Quantitative Language for Generative Recommendation

Multimodal Unsupervised Domain Generalization by Retrieving Across the Modality Gap

Multi-objective antibody design with constrained preference optimization

Multi-objective Differentiable Neural Architecture Search

Multiplicative Logit Adjustment Approximates Neural-Collapse-Aware Decision Boundary Adjustment

Multi-Scale Fusion for Object Representation

Multi-Task Dense Predictions via Unleashing the Power of Diffusion

MuseGNN: Forming Scalable, Convergent GNN Layers that Minimize a Sampling-Based Energy

Mutual Effort for Efficiency: A Similarity-based Token Pruning for Vision Transformers in Self-Supervised Learning

Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solver

Narrowing Information Bottleneck Theory for Multimodal Image-Text Representations Interpretability

Navigating Neural Space: Revisiting Concept Activation Vectors to Overcome Directional Divergence

ND-SDF: Learning Normal Deflection Fields for High-Fidelity Indoor Reconstruction

NEAR: A Training-Free Pre-Estimator of Machine Learning Model Performance

Near-Exact Privacy Amplification for Matrix Mechanisms

Near, far: Patch-ordering enhances vision foundation models' scene understanding

Near-optimal Active Regression of Single-Index Models

Near-Optimal Online Learning for Multi-Agent Submodular Coordination: Tight Approximation and Communication Efficiency

NetFormer: An interpretable model for recovering dynamical connectivity in neuronal population dynamics

NetMoE: Accelerating MoE Training through Dynamic Sample Placement

Neural Approximate Mirror Maps for Constrained Diffusion Models

Neural Causal Graph for Interpretable and Intervenable Classification

Neural Context Flows for Meta-Learning of Dynamical Systems

Neural Exploratory Landscape Analysis for Meta-Black-Box-Optimization

Neural Interactive Proofs

Neural ODE Transformers: Analyzing Internal Dynamics and Adaptive Fine-tuning

NeuralPlane: Structured 3D Reconstruction in Planar Primitives with Neural Fields

Neural Spacetimes for DAG Representation Learning

Neural Stochastic Differential Equations for Uncertainty-Aware Offline RL

NeurFlow: Interpreting Neural Networks through Neuron Groups and Functional Interactions

Neuron-based Multifractal Analysis of Neuron Interaction Dynamics in Large Models

Neuron based Personality Trait Induction in Large Language Models

Neuron Platonic Intrinsic Representation From Dynamics Using Contrastive Learning

New Algorithms for the Learning-Augmented k-means Problem

NextBestPath: Efficient 3D Mapping of Unseen Environments

NExUME: Adaptive Training and Inference for DNNs under Intermittent Power Environments

N-ForGOT: Towards Not-forgetting and Generalization of Open Temporal Graph Learning

nGPT: Normalized Transformer with Representation Learning on the Hypersphere

NL-Eye: Abductive NLI For Images

Node Identifiers: Compact, Discrete Representations for Efficient Graph Learning

Node Similarities under Random Projections: Limits and Pathological Cases

Node-Time Conditional Prompt Learning in Dynamic Graphs

No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs

No Free Lunch: Fundamental Limits of Learning Non-Hallucinating Generative Models

Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation

Noise Stability Optimization for Finding Flat Minima: A Hessian-based Regularization Approach

No Location Left Behind: Measuring and Improving the Fairness of Implicit Representations for Earth Data

No Need to Talk: Asynchronous Mixture of Language Models

Nonconvex Stochastic Optimization under Heavy-Tailed Noises: Optimal Convergence without Gradient Clipping

Non-Equilibrium Dynamics of Hybrid Continuous-Discrete Ground-State Sampling

Nonlinear multiregion neural dynamics with parametric impulse response communication channels

Non-myopic Generation of Language Models for Reasoning and Planning

Non-Stationary Dueling Bandits Under a Weighted Borda Criterion

Normed Spaces for Graph Embedding

Not All Heads Matter: A Head-Level KV Cache Compression Method with Integrated Retrieval and Reasoning

Not All Language Model Features Are One-Dimensionally Linear

No Training, No Problem: Rethinking Classifier-Free Guidance for Diffusion Models

Not-So-Optimal Transport Flows for 3D Point Cloud Generation

NovelQA: Benchmarking Question Answering on Documents Exceeding 200K Tokens

Null Counterfactual Factor Interactions for Goal-Conditioned Reinforcement Learning

Number Cookbook: Number Understanding of Language Models and How to Improve It

NutriBench: A Dataset for Evaluating Large Language Models in Nutrition Estimation from Meal Descriptions

NVS-Solver: Video Diffusion Model as Zero-Shot Novel View Synthesizer

Object-Centric Pretraining via Target Encoder Bootstrapping

ObscuraCoder: Powering Efficient Code LM Pre-Training Via Obfuscation Grounding

OCCAM: Towards Cost-Efficient and Accuracy-Aware Classification Inference

OCEAN: Offline Chain-of-thought Evaluation and Alignment in Large Language Models

O(d/T) Convergence Theory for Diffusion Probabilistic Models under Minimal Assumptions

Offline RL with Smooth OOD Generalization in Convex Hull and its Neighborhood

OMG: Opacity Matters in Material Modeling with Gaussian Splatting

OmniCorpus: A Unified Multimodal Corpus of 10 Billion-Level Images Interleaved with Text

OmnixR: Evaluating Omni-modality Language Models on Reasoning across Modalities

On a Connection Between Imitation Learning and RLHF

On Bits and Bandits: Quantifying the Regret-Information Trade-off

On Calibration of LLM-based Guard Models for Reliable Content Moderation

On Conformal Isometry of Grid Cells: Learning Distance-Preserving Position Embedding

On Discriminative Probabilistic Modeling for Self-Supervised Representation Learning

On Disentangled Training for Nonlinear Transform in Learned Image Compression

One-for-All Few-Shot Anomaly Detection via Instance-Induced Prompt Learning

One Hundred Neural Networks and Brains Watching Videos: Lessons from Alignment

One Model Transfer to All: On Robust Jailbreak Prompts Generation against LLMs

One Step Diffusion via Shortcut Models

On Evaluating the Durability of Safeguards for Open-Weight LLMs

On Large Language Model Continual Unlearning

On Linear Representations and Pretraining Data Frequency in Language Models

ONLINE EPSILON NET & PIERCING SET FOR GEOMETRIC CONCEPTS

Online-to-Offline RL for Agent Alignment

On LLM Knowledge Distillation - A Comparison between Forward KL and Reverse KL

On Quantizing Neural Representation for Variable-Rate Video Coding

On Rollouts in Model-Based Reinforcement Learning

On Statistical Rates of Conditional Diffusion Transformers: Approximation, Estimation and Minimax Optimality

On Stochastic Contextual Bandits with Knapsacks in Small Budget Regime

On Targeted Manipulation and Deception when Optimizing LLMs for User Feedback

On the Adversarial Risk of Test Time Adaptation: An Investigation into Realistic Test-Time Data Poisoning

On the Adversarial Vulnerability of Label-Free Test-Time Adaptation

On the Almost Sure Convergence of the Stochastic Three Points Algorithm

On the Benefits of Attribute-Driven Graph Domain Adaptation

On the Benefits of Memory for Modeling Time-Dependent PDEs

On the Completeness of Invariant Geometric Deep Learning Models

On the Convergence of Adaptive Gradient Methods for Nonconvex Optimization

On the Expressiveness of Rational ReLU Neural Networks With Bounded Depth

On the Expressive Power of Sparse Geometric MPNNs

On the Feature Learning in Diffusion Models

On-the-fly Preference Alignment via Principle-Guided Decoding

On the Fourier analysis in the SO(3) space : the EquiLoPO Network

On the Hölder Stability of Multiset and Graph Neural Networks

On the Importance of Language-driven Representation Learning for Heterogeneous Federated Learning

On the Inherent Privacy Properties of Discrete Denoising Diffusion Models

On the Learn-to-Optimize Capabilities of Transformers in In-Context Sparse Recovery

On the Modeling Capabilities of Large Language Models for Sequential Decision Making

On the Optimal Memorization Capacity of Transformers

On the Optimization and Generalization of Multi-head Attention

On the Optimization Landscape of Low Rank Adaptation Methods for Large Language Models

On the Role of Attention Heads in Large Language Model Safety

On the Transfer of Object-Centric Representation Learning

Open-CK: A Large Multi-Physics Fields Coupling benchmarks in Combustion Kinetics

OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data

OpenRCA: Can Large Language Models Locate the Root Cause of Software Failures?

OpenVid-1M: A Large-Scale High-Quality Dataset for Text-to-video Generation

Open-YOLO 3D: Towards Fast and Accurate Open-Vocabulary 3D Instance Segmentation

OPTAMI: Global Superlinear Convergence of High-order Methods

OptiBench Meets ReSocratic: Measure and Improve LLMs for Optimization Modeling

Optimal Flow Transport and its Entropic Regularization: a GPU-friendly Matrix Iterative Algorithm for Flow Balance Satisfaction

Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression

Optimal Learning of Kernel Logistic Regression for Complex Classification Scenarios

Optimal Non-Asymptotic Rates of Value Iteration for Average-Reward Markov Decision Processes

Optimal Protocols for Continual Learning via Statistical Physics and Control Theory

Optimization by Parallel Quasi-Quantum Annealing with Gradient-Based Sampling

Optimization with Access to Auxiliary Information

Optimizing $(L_0, L_1)$-Smooth Functions by Gradient Methods

Optimizing importance weighting in the presence of sub-population shifts

OptionZero: Planning with Learned Options

Oracle efficient truncated statistics

Oryx MLLM: On-Demand Spatial-Temporal Understanding at Arbitrary Resolution

OSCAR: Operating System Control via State-Aware Reasoning and Re-Planning

OSDA Agent: Leveraging Large Language Models for De Novo Design of Organic Structure Directing Agents

Out-of-distribution Generalization for Total Variation based Invariant Risk Minimization

Overcoming False Illusions in Real-World Face Restoration with Multi-Modal Guided Diffusion Model

Overcoming Slow Decision Frequencies in Continuous Control: Model-Based Sequence Reinforcement Learning for Model-Free Control

OVTR: End-to-End Open-Vocabulary Multiple Object Tracking with Transformer

Pacmann: Efficient Private Approximate Nearest Neighbor Search

PADRe: A Unifying Polynomial Attention Drop-in Replacement for Efficient Vision Transformer

Pairwise Elimination with Instance-Dependent Guarantees for Bandits with Cost Subsidy

PALMBENCH: A COMPREHENSIVE BENCHMARK OF COMPRESSED LARGE LANGUAGE MODELS ON MOBILE PLATFORMS

PAL: Sample-Efficient Personalized Reward Modeling for Pluralistic Alignment

Palu: KV-Cache Compression with Low-Rank Projection

PaPaGei: Open Foundation Models for Optical Physiological Signals

Param$\Delta$ for Direct Mixing: Post-Train Large Language Model At Zero Cost

Parameter and Memory Efficient Pretraining via Low-rank Riemannian Optimization

Parameter-Efficient and Stable Singular Value Adaptation for Pre-Trained Models

Parameter Expanded Stochastic Gradient Markov Chain Monte Carlo

Pareto Low-Rank Adapters: Efficient Multi-Task Learning with Preferences

ParFam -- (Neural Guided) Symbolic Regression via Continuous Global Optimization

Partially Observed Trajectory Inference using Optimal Transport and a Dynamics Prior

PARTNR: A Benchmark for Planning and Reasoning in Embodied Multi-agent Tasks

PEARL: Towards Permutation-Resilient LLMs

PEAR: Primitive Enabled Adaptive Relabeling for Boosting Hierarchical Reinforcement Learning

Pedestrian Motion Reconstruction: A Large-scale Benchmark via Mixed Reality Rendering with Multiple Perspectives and Modalities

Perm: A Parametric Representation for Multi-Style 3D Hair Modeling

Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models

Persistent Pre-training Poisoning of LLMs

Personalized Representation from Personalized Generation

Personalized Visual Instruction Tuning

PersonalLLM: Tailoring LLMs to Individual Preferences

Perturbation-Restrained Sequential Model Editing

PhiNets: Brain-inspired Non-contrastive Learning Based on Temporal Prediction Hypothesis

Physics-Informed Diffusion Models

Physics-informed Temporal Difference Metric Learning for Robot Motion Planning

Physics of Language Models: Part 2.1, Grade-School Math and the Hidden Reasoning Process

Physics of Language Models: Part 2.2, How to Learn From Mistakes on Grade-School Math Problems

Physics of Language Models: Part 3.2, Knowledge Manipulation

Physics of Language Models: Part 3.3, Knowledge Capacity Scaling Laws

Physiome-ODE: A Benchmark for Irregularly Sampled Multivariate Time-Series Forecasting Based on Biological ODEs

PhysPDE: Rethinking PDE Discovery and a Physical HYpothesis Selection Benchmark

PianoMotion10M: Dataset and Benchmark for Hand Motion Generation in Piano Performance

PiCO: Peer Review in LLMs based on Consistency Optimization

PINP: Physics-Informed Neural Predictor with latent estimation of fluid flows

Pitfalls of Evidence-Based AI Policy

Planning Anything with Rigor: General-Purpose Zero-Shot Planning with LLM-based Formalized Programming

Plastic Learning with Deep Fourier Features

pMoE: Prompting Diverse Experts Together Wins More in Visual Adaptation

POGEMA: A Benchmark Platform for Cooperative Multi-Agent Pathfinding

Point Cluster: A Compact Message Unit for Communication-Efficient Collaborative Perception

Point-based Instance Completion with Scene Constraints

Point-SAM: Promptable 3D Segmentation Model for Point Clouds

Poison-splat: Computation Cost Attack on 3D Gaussian Splatting

PolaFormer: Polarity-aware Linear Attention for Vision Transformers

Policy Decorator: Model-Agnostic Online Refinement for Large Policy Model

Policy Design in Long-run Welfare Dynamics

Policy Gradient with Kernel Quadrature

Policy Optimization under Imperfect Human Interactions with Agent-Gated Shared Autonomy

PolyNet: Learning Diverse Solution Strategies for Neural Combinatorial Optimization

PortLLM: Personalizing Evolving Large Language Models with Training-Free and Portable Model Patches

Positive-Unlabeled Diffusion Models for Preventing Sensitive Data Generation

POTEC: Off-Policy Contextual Bandits for Large Action Spaces via Policy Decomposition

PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation

Preble: Efficient Distributed Prompt Scheduling for LLM Serving

Precedence-Constrained Winter Value for Effective Graph Data Valuation

Prediction Risk and Estimation Risk of the Ridgeless Least Squares Estimator under General Assumptions on Regression Errors

Predictive Inverse Dynamics Models are Scalable Learners for Robotic Manipulation

Preference Diffusion for Recommendation

Preference Elicitation for Offline Reinforcement Learning

Preference Optimization for Reasoning with Pseudo Feedback

Preserving Deep Representations in One-Shot Pruning: A Hessian-Free Second-Order Optimization Framework

Preserving Diversity in Supervised Fine-Tuning of Large Language Models

Privacy Auditing of Large Language Models

Privacy-Aware Lifelong Learning

Privately Counting Partially Ordered Data

Private Mechanism Design via Quantile Estimation

Proactive Privacy Amnesia for Large Language Models: Safeguarding PII with Negligible Impact on Model Utility

Probabilistic Conformal Prediction with Approximate Conditional Validity

Probabilistic Learning to Defer: Handling Missing Expert Annotations and Controlling Workload Distribution

Probabilistic Neural Pruning via Sparsity Evolutionary Fokker-Planck-Kolmogorov Equation

Probe before You Talk: Towards Black-box Defense against Backdoor Unalignment for Large Language Models

Problem-Parameter-Free Federated Learning

Process Reward Model with Q-value Rankings

Progressive Compositionality in Text-to-Image Generative Models

Progressive Compression with Universally Quantized Diffusion Models

Progressive distillation induces an implicit curriculum

Progress or Regress? Self-Improvement Reversal in Post-training

Projection Head is Secretly an Information Bottleneck

Promptriever: Instruction-Trained Retrievers Can Be Prompted Like Language Models

Protecting against simultaneous data poisoning attacks

ProteinBench: A Holistic Evaluation of Protein Foundation Models

Protein Language Model Fitness is a Matter of Preference

ProtoSnap: Prototype Alignment For Cuneiform Signs

ProtPainter: Draw or Drag Protein via Topology-guided Diffusion

Provable Convergence and Limitations of Geometric Tempering for Langevin Dynamics

Provable Convergence Bounds for Hybrid Dynamical Sampling and Optimization

Provable Robust Overfitting Mitigation in Wasserstein Distributionally Robust Optimization

Provable Uncertainty Decomposition via Higher-Order Calibration

Provable weak-to-strong generalization via benign overfitting

Provably Robust Explainable Graph Neural Networks against Graph Perturbation Attacks

Provence: efficient and robust context pruning for retrieval-augmented generation

Proving Olympiad Inequalities by Synergizing LLMs and Symbolic Reasoning

Proximal Mapping Loss: Understanding Loss Functions in Crowd Counting & Localization

P-SPIKESSM: HARNESSING PROBABILISTIC SPIKING STATE SPACE MODELS FOR LONG-RANGE DEPENDENCY TASKS

Pushing the Limits of All-Atom Geometric Graph Neural Networks: Pre-Training, Scaling, and Zero-Shot Transfer

PuzzleFusion++: Auto-agglomerative 3D Fracture Assembly by Denoise and Verify

PvNeXt: Rethinking Network Design and Temporal Motion for Point Cloud Video Recognition

Pyramidal Flow Matching for Efficient Video Generative Modeling

QA-Calibration of Language Model Confidence Scores

Q-Adapter: Customizing Pre-trained LLMs to New Preferences with Forgetting Mitigation

QERA: an Analytical Framework for Quantization Error Reconstruction

Qinco2: Vector Compression and Search with Improved Implicit Neural Codebooks

qNBO: quasi-Newton Meets Bilevel Optimization

QP-SNN: Quantized and Pruned Spiking Neural Networks

Q-SFT: Q-Learning for Language Models via Supervised Fine-Tuning

QuaDiM: A Conditional Diffusion Model For Quantum State Property Estimation

Quality over Quantity in Attention Layers: When Adding More Heads Hurts

Quamba: A Post-Training Quantization Recipe for Selective State Space Models

Quantitative Approximation for Neural Operators in Nonlinear Parabolic Equations

Quantum (Inspired) $D^2$-sampling with Applications

Query-based Knowledge Transfer for Heterogeneous Learning Environments

Radar: Fast Long-Context Decoding for Any Transformer

RAG-SR: Retrieval-Augmented Generation for Neural Symbolic Regression

RainbowPO: A Unified Framework for Combining Improvements in Preference Optimization

Random Is All You Need: Random Noise Injection on Feature Statistics for Generalizable Deep Image Denoising

Range, not Independence, Drives Modularity in Biologically Inspired Representations

Ranking-aware adapter for text-driven image ordering with CLIP

RankSHAP: Shapley Value Based Feature Attributions for Learning to Rank

Rapidly Adapting Policies to the Real-World via Simulation-Guided Fine-Tuning

RAPID: Retrieval Augmented Training of Differentially Private Diffusion Models

Rapid Selection and Ordering of In-Context Demonstrations via Prompt Embedding Clustering

Rare event modeling with self-regularized normalizing flows: what can we learn from a single failure?

Rare-to-Frequent: Unlocking Compositional Generation Power of Diffusion Models on Rare Concepts with LLM Guidance

RaSA: Rank-Sharing Low-Rank Adaptation

Rational Decision-Making Agent with Learning Internal Utility Judgment

Rationalizing and Augmenting Dynamic Graph Neural Networks

RA-TTA: Retrieval-Augmented Test-Time Adaptation for Vision-Language Models

RB-Modulation: Training-Free Stylization using Reference-Based Modulation

RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation

Real2Code: Reconstruct Articulated Objects via Code Generation

Re-Aligning Language to Visual Objects with an Agentic Workflow

Realistic Evaluation of Deep Partial-Label Learning Algorithms

Real-Time Video Generation with Pyramid Attention Broadcast

Reasoning Elicitation in Language Models via Counterfactual Feedback

Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval

Reasoning with Latent Thoughts: On the Power of Looped Transformers

Reassessing How to Compare and Improve the Calibration of Machine Learning Models

ReAttention: Training-Free Infinite Context with Finite Attention Scope

REBIND: Enhancing Ground-state Molecular Conformation Prediction via Force-Based Graph Rewiring

RECAST: Reparameterized, Compact weight Adaptation for Sequential Tasks

Recognize Any Surgical Object: Unleashing the Power of Weakly-Supervised Data

Reconciling Model Multiplicity for Downstream Decision Making

Reconsidering Faithfulness in Regular, Self-Explainable and Domain Invariant GNNs

Reconstruction-Guided Policy: Enhancing Decision-Making through Agent-Wise State Consistency

Recovering Manifold Structure Using Ollivier Ricci Curvature

Recovery of Causal Graph Involving Latent Variables via Homologous Surrogates

Rectified Diffusion: Straightness Is Not Your Need in Rectified Flow

ReDeEP: Detecting Hallucination in Retrieval-Augmented Generation via Mechanistic Interpretability

Redefining the task of Bioactivity Prediction

Re-evaluating Open-ended Evaluation of Large Language Models

Reexamining the Aleatoric and Epistemic Uncertainty Dichotomy

RefactorBench: Evaluating Stateful Reasoning in Language Agents Through Code

Refine-by-Align: Reference-Guided Artifacts Refinement through Semantic Alignment

Refining CLIP's Spatial Awareness: A Visual-Centric Perspective

Reframing Structure-Based Drug Design Model Evaluation via Metrics Correlated to Practical Needs

ReGenesis: LLMs can Grow into Reasoning Generalists via Self-Improvement

Regressing the Relative Future: Efficient Policy Optimization for Multi-turn RLHF

Regret Bounds for Episodic Risk-Sensitive Linear Quadratic Regulator

Regretful Decisions under Label Noise

Regularization by Texts for Latent Diffusion Inverse Solvers

Regularizing Energy among Training Samples for Out-of-Distribution Generalization

Regulatory DNA Sequence Design with Reinforcement Learning

Re-Imagining Multimodal Instruction Tuning: A Representation View

Reinforcement Learning for Control of Non-Markovian Cellular Population Dynamics

Relation-Aware Diffusion for Heterogeneous Graphs with Partially Observed Features

RelCon: Relative Contrastive Learning for a Motion Foundation Model for Wearable Data

ReMatching Dynamic Reconstruction Flow

REMEDY: Recipe Merging Dynamics in Large Vision-Language Models

ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing

Repetition Improves Language Model Embeddings

RepoGraph: Enhancing AI Software Engineering with Repository-level Code Graph

Representational Similarity via Interpretable Visual Concepts

Representative Guidance: Diffusion Model Sampling with Coherence

Repulsive Latent Score Distillation for Solving Inverse Problems

RESfM: Robust Deep Equivariant Structure from Motion

ReSi: A Comprehensive Benchmark for Representational Similarity Measures

Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNs

Residual Deep Gaussian Processes on Manifolds

Residual Kernel Policy Network: Enhancing Stability and Robustness in RKHS-Based Reinforcement Learning

Residual-MPPI: Online Policy Customization for Continuous Control

Residual Stream Analysis with Multi-Layer SAEs

Restating the Proof of Linear Convergence for Linear GNNs

RESuM: A Rare Event Surrogate Model for Physics Detector Design

Rethinking and Improving Autoformalization: Towards a Faithful Metric and a Dependency Retrieval-based Approach

Rethinking Artistic Copyright Infringements In the Era Of Text-to-Image Generative Models

Rethinking Audio-Visual Adversarial Vulnerability from Temporal and Modality Perspectives

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior

Rethinking Evaluation of Sparse Autoencoders through the Representation of Polysemous Words

Rethinking Fair Representation Learning for Performance-Sensitive Tasks

Rethinking Graph Prompts: Unraveling the Power of Data Manipulation in Graph Neural Networks

Rethinking Invariance in In-context Learning

Rethinking Invariance Regularization in Adversarial Training to Improve Robustness-Accuracy Trade-off

Rethinking Reward Model Evaluation: Are We Barking up the Wrong Tree?

Rethinking Reward Modeling in Preference-based Large Language Model Alignment

Rethinking Self-Distillation: Label Averaging and Enhanced Soft Label Refinement with Partial Labels

Rethinking Shapley Value for Negative Interactions in Non-convex Games

Rethinking Spiking Neural Networks from an Ensemble Learning Perspective

Rethinking the generalization of drug target affinity prediction algorithms via similarity aware evaluation

Rethinking the role of frames for SE(3)-invariant crystal structure modeling

Reti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion Model

Retri3D: 3D Neural Graphics Representation Retrieval

Retrieval Head Mechanistically Explains Long-Context Factuality

RetroInText: A Multimodal Large Language Model Enhanced Framework for Retrosynthetic Planning via In-Context Representation Learning

Revealing and Reducing Gender Biases in Vision and Language Assistants (VLAs)

Revisiting a Design Choice in Gradient Temporal Difference Learning

Revisiting Convolution Architecture in the Realm of DNA Foundation Models

Revisiting Energy Based Models as Policies: Ranking Noise Contrastive Estimation and Interpolating Energy Models

Revisiting Feature Prediction for Learning Visual Representations from Video

Revisiting In-context Learning Inference Circuit in Large Language Models

Revisiting Large-Scale Non-convex Distributionally Robust Optimization

Revisiting Mode Connectivity in Neural Networks with Bezier Surface

REVISITING MULTI-PERMUTATION EQUIVARIANCE THROUGH THE LENS OF IRREDUCIBLE REPRESENTATIONS

Revisiting Nearest Neighbor for Tabular Data: A Deep Tabular Baseline Two Decades Later

Revisit Large-Scale Image-Caption Data in Pre-training Multimodal Foundation Models

Revolutionizing EMCCD Denoising through a Novel Physics-Based Learning Framework for Noise Modeling

REvolve: Reward Evolution with Large Language Models using Human Feedback

Reward Dimension Reduction for Scalable Multi-Objective Reinforcement Learning

Reward Guided Latent Consistency Distillation

Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning

Reward Learning from Multiple Feedback Types

Risk-Sensitive Diffusion: Robustly Optimizing Diffusion Models with Noisy Samples

Risk-Sensitive Variational Actor-Critic: A Model-Based Approach

RMB: Comprehensively benchmarking reward models in LLM alignment

RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and Style

RMP-SAM: Towards Real-Time Multi-Purpose Segment Anything

RNNs are not Transformers (Yet): The Key Bottleneck on In-Context Retrieval

RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation

Robots Pre-train Robots: Manipulation-Centric Robotic Representation from Large-Scale Robot Datasets

RobuRCDet: Enhancing Robustness of Radar-Camera Fusion in Bird's Eye View for 3D Object Detection

Robust Conformal Prediction with a Single Binary Certificate

Robust Feature Learning for Multi-Index Models in High Dimensions

Robust Function-Calling for On-Device Language Model via Function Masking

Robust Gymnasium: A Unified Modular Benchmark for Robust Reinforcement Learning

RobustKV: Defending Large Language Models against Jailbreak Attacks via KV Eviction

Robustness Auditing for Linear Regression: To Singularity and Beyond

Robustness Inspired Graph Backdoor Defense

Robustness of Quantum Algorithms for Nonconvex Optimization

Robustness Reprogramming for Representation Learning

Robust-PIFu: Robust Pixel-aligned Implicit Function for 3D Human Digitalization from a Single Image

Robust Simulation-Based Inference under Missing Data via Neural Processes

Robust System Identification: Finite-sample Guarantees and Connection to Regularization

Robust Transfer of Safety-Constrained Reinforcement Learning Agents

Robust Weight Initialization for Tanh Neural Networks with Fixed Point Analysis

Rodimus*: Breaking the Accuracy-Efficiency Trade-Off with Efficient Attentions

Root Cause Analysis of Anomalies in Multivariate Time Series through Granger Causal Discovery

Rotated Runtime Smooth: Training-Free Activation Smoother for accurate INT4 inference

Round and Round We Go! What makes Rotary Positional Encodings useful?

Routing Experts: Learning to Route Dynamic Experts in Existing Multi-modal Large Language Models

RRM: Robust Reward Model Training Mitigates Reward Hacking

R-Sparse: Rank-Aware Activation Sparsity for Efficient LLM Inference

RTDiff: Reverse Trajectory Synthesis via Diffusion for Offline Reinforcement Learning

RTop-K: Ultra-Fast Row-Wise Top-K Selection for Neural Network Acceleration on GPUs

RuAG: Learned-rule-augmented Generation for Large Language Models

Safety Alignment Should be Made More Than Just a Few Tokens Deep

Safety Layers in Aligned Large Language Models: The Key to LLM Security

Safety-Prioritizing Curricula for Constrained Reinforcement Learning

SafeWatch: An Efficient Safety-Policy Following Video Guardrail Model with Transparent Explanations

SageAttention: Accurate 8-Bit Attention for Plug-and-play Inference Acceleration

SaLoRA: Safety-Alignment Preserved Low-Rank Adaptation

SAM 2: Segment Anything in Images and Videos

Samba: Synchronized Set-of-Sequences Modeling for Multiple Object Tracking

SAM-CP: Marrying SAM with Composable Prompts for Versatile Segmentation

SaMer: A Scenario-aware Multi-dimensional Evaluator for Large Language Models

Sample then Identify: A General Framework for Risk Control and Assessment in Multimodal Large Language Models

SAMRefiner: Taming Segment Anything Model for Universal Mask Refinement

SANER: Annotation-free Societal Attribute Neutralizer for Debiasing CLIP

Satisficing Regret Minimization in Bandits

SBSC: Step-by-Step Coding for Improving Mathematical Olympiad Performance

Scalable and Certifiable Graph Unlearning: Overcoming the Approximation Error Barrier

Scalable Benchmarking and Robust Learning for Noise-Free Ego-Motion and 3D Reconstruction from Noisy Video

Scalable Decentralized Learning with Teleportation

Scalable Decision-Making in Stochastic Environments through Learned Temporal Abstraction

Scalable Discrete Diffusion Samplers: Combinatorial Optimization and Statistical Physics

Scalable Extraction of Training Data from Aligned, Production Language Models

Scalable Influence and Fact Tracing for Large Language Model Pretraining

Scalable Universal T-Cell Receptor Embeddings from Adaptive Immune Repertoires

Scale-Aware Contrastive Reverse Distillation for Unsupervised Medical Anomaly Detection

Scale-aware Recognition in Satellite Images under Resource Constraints

Scale-Free Graph-Language Models

Scaling and evaluating sparse autoencoders

Scaling Autonomous Agents via Automatic Reward Modeling And Planning

Scaling In-the-Wild Training for Diffusion-based Illumination Harmonization and Editing by Imposing Consistent Light Transport

Scaling Laws for Adversarial Attacks on Language Model Activations and Tokens

Scaling Laws for Downstream Task Performance in Machine Translation

Scaling LLM Test-Time Compute Optimally Can be More Effective than Scaling Parameters for Reasoning

Scaling Long Context Training Data by Long-Distance Referrals

Scaling Offline Model-Based RL via Jointly-Optimized World-Action Model Pretraining

Scaling Optimal LR Across Token Horizons

Scaling Speech-Text Pre-training with Synthetic Interleaved Data

Scaling Stick-Breaking Attention: An Efficient Implementation and In-depth Study

Scaling Transformers for Low-Bitrate High-Quality Speech Coding

Scaling up the Banded Matrix Factorization Mechanism for Large Scale Differentially Private ML

Scaling Wearable Foundation Models

Schur's Positive-Definite Network: Deep Learning in the SPD cone with structure

ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery

SCOPE: A Self-supervised Framework for Improving Faithfulness in Conditional Text Generation

Score-based free-form architectures for high-dimensional Fokker-Planck equations

Score Forgetting Distillation: A Swift, Data-Free Method for Machine Unlearning in Diffusion Models

SEAL: Safety-enhanced Aligned LLM Fine-tuning via Bilevel Data Selection

SEBRA : Debiasing through Self-Guided Bias Ranking

Second-Order Min-Max Optimization with Lazy Hessians

SecureGS: Boosting the Security and Fidelity of 3D Gaussian Splatting Steganography

SeedLM: Compressing LLM Weights into Seeds of Pseudo-Random Generators

Seeing Eye to AI: Human Alignment via Gaze-Based Response Rewards for Large Language Models

SegLLM: Multi-round Reasoning Segmentation with Large Language Models

SelectFormer: Private and Practical Data Selection for Transformers

Selective induction Heads: How Transformers Select Causal Structures in Context

Self-Attention-Based Contextual Modulation Improves Neural System Identification

Self-Boosting Large Language Models with Synthetic Preference Data

Self-Evolving Multi-Agent Collaboration Networks for Software Development

Self-Improvement in Language Models: The Sharpening Mechanism

Self-Improving Robust Preference Optimization

Self-MoE: Towards Compositional Large Language Models with Self-Specialized Experts

Self-Play Preference Optimization for Language Model Alignment

Self-Supervised Diffusion Models for Electron-Aware Molecular Representation Learning

Self-supervised Monocular Depth Estimation Robust to Reflective Surface Leveraged by Triplet Mining

Self-Updatable Large Language Models by Integrating Context into Model Parameters

SelKD: Selective Knowledge Distillation via Optimal Transport Perspective

Semantic Image Inversion and Editing using Rectified Stochastic Differential Equations

Semantic Loss Guided Data Efficient Supervised Fine Tuning for Safe Responses in LLMs

Semantics-Adaptive Activation Intervention for LLMs via Dynamic Steering Vectors

Semantix: An Energy-guided Sampler for Semantic Style Transfer

SEMDICE: Off-policy State Entropy Maximization via Stationary Distribution Correction Estimation

Semialgebraic Neural Networks: From roots to representations

Semi-Parametric Retrieval via Binary Bag-of-Tokens Index

Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations

Sensitivity-Aware Amortized Bayesian Inference

Sensor-Invariant Tactile Representation

SEPARATE: A Simple Low-rank Projection for Gradient Compression in Modern Large-scale Model Training Process

Separation Power of Equivariant Neural Networks

Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced Reasoning

SeRA: Self-Reviewing and Alignment of LLMs using Implicit Reward Margins

SFESS: Score Function Estimators for $k$-Subset Sampling

SFS: Smarter Code Space Search improves LLM Inference Scaling

SGD with memory: fundamental properties and stochastic acceleration

Shallow diffusion networks provably learn hidden low-dimensional structure

Shapley-Guided Utility Learning for Effective Graph Inference Data Valuation

Shared-AE: Automatic Identification of Shared Subspaces in High-dimensional Neural and Behavioral Activity

Sharper Guarantees for Learning Neural Network Classifiers with Gradient Methods

Sharpness-Aware Minimization: General Analysis and Improved Rates

ShEPhERD: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design

Shifting the Paradigm: A Diffeomorphism Between Time Series Data Manifolds for Achieving Shift-Invariancy in Deep Learning

ShortcutsBench: A Large-Scale Real-world Benchmark for API-based Agents

Should VLMs be Pre-trained with Image Data?

Show-o: One Single Transformer to Unify Multimodal Understanding and Generation

SigDiffusions: Score-Based Diffusion Models for Time Series via Log-Signature Embeddings

SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning

SimPER: A Minimalist Approach to Preference Alignment without Hyperparameters

Simple, Good, Fast: Self-Supervised World Models Free of Baggage

Simple Guidance Mechanisms for Discrete Diffusion Models

Simple is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation

Simple ReFlow: Improved Techniques for Fast Flow Models

Simple yet Effective Incomplete Multi-view Clustering: Similarity-level Imputation and Intra-view Hybrid-group Prototype Construction

Simplifying Deep Temporal Difference Learning

Simplifying, Stabilizing and Scaling Continuous-time Consistency Models

Simulating Human-like Daily Activities with Desire-driven Autonomy

Simulating Training Dynamics to Reconstruct Training Data from Deep Neural Networks

SimulPL: Aligning Human Preferences in Simultaneous Machine Translation

SINGER: Stochastic Network Graph Evolving Operator for High Dimensional PDEs

Single Teacher, Multiple Perspectives: Teacher Knowledge Augmentation for Enhanced Knowledge Distillation

Singular Subspace Perturbation Bounds via Rectangular Random Matrix Diffusions

SiReRAG: Indexing Similar and Related Information for Multihop Reasoning

Sitcom-Crafter: A Plot-Driven Human Motion Generation System in 3D Scenes

Sketch2Diagram: Generating Vector Diagrams from Hand-Drawn Sketches

Sketching for Convex and Nonconvex Regularized Least Squares with Sharp Guarantees

SleepSMC: Ubiquitous Sleep Staging via Supervised Multimodal Coordination

SLMRec: Distilling Large Language Models into Small for Sequential Recommendation

SLoPe: Double-Pruned Sparse Plus Lazy Low-Rank Adapter Pretraining of LLMs

SlowFast-VGen: Slow-Fast Learning for Action-Driven Long Video Generation

Smaller, Weaker, Yet Better: Training LLM Reasoners via Compute-Optimal Sampling

Small Models are LLM Knowledge Triggers for Medical Tabular Prediction

Small-to-Large Generalization: Training Data Influences Models Consistently Across Scale

SmartPretrain: Model-Agnostic and Dataset-Agnostic Representation Learning for Motion Prediction

SmartRAG: Jointly Learn RAG-Related Tasks From the Environment Feedback

SMI-Editor: Edit-based SMILES Language Model with Fragment-level Supervision

SMITE: Segment Me In TimE

SMT: Fine-Tuning Large Language Models with Sparse Matrices

SoftCVI: Contrastive variational inference with self-generated soft labels

Soft Merging of Experts with Adaptive Routing

Solving Inverse Problems with Model Mismatch using Untrained Neural Networks within Model-based Architectures

Solving New Tasks by Adapting Internet Video Knowledge

Solving Video Inverse Problems Using Image Diffusion Models

SonicSim: A customizable simulation platform for speech processing in moving sound source scenarios

SOO-Bench: Benchmarks for Evaluating the Stability of Offline Black-Box Optimization

SOREL: A Stochastic Algorithm for Spectral Risks Minimization

SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal

SoundCTM: Unifying Score-based and Consistency Models for Full-band Text-to-Sound Generation

SpaceGNN: Multi-Space Graph Neural Network for Node Anomaly Detection with Extremely Limited Labels

Sparse autoencoders reveal selective remapping of visual concepts during adaptation

Sparse Autoencoders Reveal Temporal Difference Learning in Large Language Models

Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models

Sparse Learning for State Space Models on Mobile

SPaR: Self-Play with Tree-Search Refinement to Improve Instruction-Following in Large Language Models

Spatial-Mamba: Effective Visual State Space Models via Structure-Aware State Fusion

Spectral Compressive Imaging via Unmixing-driven Subspace Diffusion Refinement

Spectro-Riemannian Graph Neural Networks

Speculative Knowledge Distillation: Bridging the Teacher-Student Gap Through Interleaved Sampling

Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting

Speech Robust Bench: A Robustness Benchmark For Speech Recognition

SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks

SpikeLLM: Scaling up Spiking Neural Network to Large Language Models via Saliency-based Spiking

Spiking Vision Transformer with Saccadic Attention

SpinQuant: LLM Quantization with Learned Rotations

SplatFormer: Point Transformer for Robust 3D Gaussian Splatting

Sports-Traj: A Unified Trajectory Generation Model for Multi-Agent Movement in Sports

SPORTU: A Comprehensive Sports Understanding Benchmark for Multimodal Large Language Models

Spreading Out-of-Distribution Detection on Graphs

Spread Preference Annotation: Direct Preference Judgment for Efficient LLM Alignment

SRSA: Skill Retrieval and Adaptation for Robotic Assembly Tasks

SSOLE: Rethinking Orthogonal Low-rank Embedding for Self-Supervised Learning

Stabilized Neural Prediction of Potential Outcomes in Continuous Time

Stable Segment Anything Model

STAMP: Scalable Task- And Model-agnostic Collaborative Perception

Standard Gaussian Process is All You Need for High-Dimensional Bayesian Optimization

Standardizing Structural Causal Models

STAR: Synthesis of Tailored Architectures

State Space Models are Provably Comparable to Transformers in Dynamic Token Selection

Statistical Tractability of Off-policy Evaluation of History-dependent Policies in POMDPs

STBLLM: Breaking the 1-Bit Barrier with Structured Binary LLMs

Stealthy Shield Defense: A Conditional Mutual Information-Based Approach against Black-Box Model Inversion Attacks

Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction

Stiefel Flow Matching for Moment-Constrained Structure Elucidation

Stochastic Polyak Step-sizes and Momentum: Convergence Guarantees and Practical Performance

Stochastic variance-reduced Gaussian variational inference on the Bures-Wasserstein manifold

Storybooth: Training-Free Multi-Subject Consistency for Improved Visual Storytelling

Straightness of Rectified Flow: A Theoretical Insight into Wasserstein Convergence

Straight to Zero: Why Linearly Decaying the Learning Rate to Zero Works Best for LLMs

STRAP: Robot Sub-Trajectory Retrieval for Augmented Policy Learning

Strategic Classification With Externalities

Streaming Algorithms For $\ell_p$ Flows and $\ell_p$ Regression

Streaming Video Question-Answering with In-context Video KV-Cache Retrieval

Streaming Video Understanding and Multi-round Interaction with Memory-enhanced Knowledge

Streamlining Prediction in Bayesian Deep Learning

StringLLM: Understanding the String Processing Capability of Large Language Models

Strong Model Collapse

Strong Preferences Affect the Robustness of Preference Models and Value Alignment

Style Outweighs Substance: Failure Modes of LLM Judges in Alignment Benchmarking

Supervised and Semi-Supervised Diffusion Maps with Label-Driven Diffusion

SurFhead: Affine Rig Blending for Geometrically Accurate 2D Gaussian Surfel Head Avatars

SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression

SVG: 3D Stereoscopic Video Generation via Denoising Frame Matrix

SV-RAG: LoRA-Contextualizing Adaptation of MLLMs for Long Document Understanding

SWE-Search: Enhancing Software Agents with Monte Carlo Tree Search and Iterative Refinement

Swift4D: Adaptive divide-and-conquer Gaussian Splatting for compact and efficient reconstruction of dynamic scene

SWIFT: On-the-Fly Self-Speculative Decoding for LLM Inference Acceleration

SyllableLM: Learning Coarse Semantic Units for Speech Language Models

Symbolic regression via MDLformer-guided search: from minimizing prediction error to minimizing description length

SymmetricDiffusers: Learning Discrete Diffusion Models over Finite Symmetric Groups

SynCamMaster: Synchronizing Multi-Camera Video Generation from Diverse Viewpoints

Synergy and Diversity in CLIP: Enhancing Performance Through Adaptive Backbone Ensembling

SynQ: Accurate Zero-shot Quantization by Synthesis-aware Fine-tuning

Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo

Synthesizing Programmatic Reinforcement Learning Policies with Large Language Model Guided Search

Synthesizing Realistic fMRI: A Physiological Dynamics-Driven Hierarchical Diffusion Model for Efficient fMRI Acquisition

Synthetic continued pretraining

SysCaps: Language Interfaces for Simulation Surrogates of Complex Systems

Systematic Outliers in Large Language Models

Systematic Relational Reasoning With Epistemic Graph Neural Networks

T2V-Turbo-v2: Enhancing Video Model Post-Training through Data, Reward, and Conditional Guidance Design

TabM: Advancing tabular deep learning with parameter-efficient ensembling

TabReD: Analyzing Pitfalls and Filling the Gaps in Tabular Deep Learning Benchmarks

Tackling Data Corruption in Offline Reinforcement Learning via Sequence Modeling

Talking Turns: Benchmarking Audio Foundation Models on Turn-Taking Dynamics

TANGO: Co-Speech Gesture Video Reenactment with Hierarchical Audio Motion Embedding and Diffusion Interpolation

Targeted Attack Improves Protection against Unauthorized Diffusion Customization

TD-Paint: Faster Diffusion Inpainting Through Time Aware Pixel Conditioning

TeaserGen: Generating Teasers for Long Documentaries

TEASER: Token Enhanced Spatial Modeling for Expressions Reconstruction

TempMe: Video Temporal Token Merging for Efficient Text-Video Retrieval

Temporal Flexibility in Spiking Neural Networks: Towards Generalization Across Time Steps and Deployment Friendliness

TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data

TestGenEval: A Real World Unit Test Generation and Test Completion Benchmark

Test-Time Adaptation for Combating Missing Modalities in Egocentric Videos

Test-time Adaptation for Cross-modal Retrieval with Query Shift

Test-time Adaptation for Image Compression with Distribution Regularization

Test-time Alignment of Diffusion Models without Reward Over-optimization

Test-Time Ensemble via Linear Mode Connectivity: A Path to Better Adaptation

TetSphere Splatting: Representing High-Quality Geometry with Lagrangian Volumetric Meshes

Text-to-Image Rectified Flow as Plug-and-Play Priors

TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics

The 3D-PC: a benchmark for visual perspective taking in humans and machines

The adaptive complexity of parallelized log-concave sampling

The AdEMAMix Optimizer: Better, Faster, Older

The Case for Cleaner Biosignals: High-fidelity Neural Compressor Enables Transfer from Cleaner iEEG to Noisier EEG

The Complexity of Two-Team Polymatrix Games with Independent Adversaries

The Computational Complexity of Circuit Discovery for Inner Interpretability

The Crystal Ball Hypothesis in diffusion models: Anticipating object positions from initial noise

The Directionality of Optimization Trajectories in Neural Networks

The Effectiveness of Curvature-Based Rewiring and the Role of Hyperparameters in GNNs Revisited

The Hidden Cost of Waiting for Accurate Predictions

The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation

The Illustrated AlphaFold

The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws

The KoLMogorov Test: Compression by Code Generation

The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD

The "Law'' of the Unconscious Contrastive Learner: Probabilistic Alignment of Unpaired Modalities

The Loss Landscape of Deep Linear Neural Networks: a Second-order Analysis

The OMG dataset: An Open MetaGenomic corpus for mixed-modality genomic language modeling

The Optimization Landscape of SGD Across the Feature Learning Strength

Theory, Analysis, and Best Practices for Sigmoid Self-Attention

Theory on Score-Mismatched Diffusion Models and Zero-Shot Conditional Samplers

Theory on Mixture-of-Experts in Continual Learning

The Pitfalls of Memorization: When Memorization Hurts Generalization

The Power of LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions

The Ramanujan Library - Automated Discovery on the Hypergraph of Integer Relations

The Rise and Down of Babel Tower: Investigating the Evolution Process of Multilingual Code Large Language Model

ThermalGaussian: Thermal 3D Gaussian Splatting

The Same but Different: Structural Similarities and Differences in Multilingual Language Modeling

The Unreasonable Ineffectiveness of the Deeper Layers

The Utility and Complexity of In- and Out-of-Distribution Machine Unlearning

The Value of Sensory Information to a Robot

ThinkBot: Embodied Instruction Following with Thought Chain Reasoning

Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation

Think Thrice Before You Act: Progressive Thought Refinement in Large Language Models

Think Twice Before Claiming Your Optimization Algorithm Outperformance - Review and Beyond

Three-in-One: Fast and Accurate Transducer for Hybrid-Autoregressive ASR

ThunderKittens: Simple, Fast, and $\textit{Adorable}$ Kernels

TidalDecode: Fast and Accurate LLM Decoding with Position Persistent Sparse Attention

TIGER: Time-frequency Interleaved Gain Extraction and Reconstruction for Efficient Speech Separation

Tight Clusters Make Specialized Experts

Time After Time: Deep-Q Effect Estimation for Interventions on When and What to do

TimeInf: Time Series Data Contribution via Influence Functions

TimeKAN: KAN-based Frequency Decomposition Learning Architecture for Long-term Time Series Forecasting

Timer-XL: Long-Context Transformers for Unified Time Series Forecasting

Time-to-Event Pretraining for 3D Medical Imaging

TIPS: Text-Image Pretraining with Spatial awareness

TIS-DPO: Token-level Importance Sampling for Direct Preference Optimization With Estimated Weights

T-JEPA: Augmentation-Free Self-Supervised Learning for Tabular Data

TLDR: Token-Level Detective Reward Model for Large Vision Language Models

To Clip or not to Clip: the Dynamics of SGD with Gradient Clipping in High-Dimensions

To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning

ToddlerDiffusion: Interactive Structured Image Generation with Cascaded Schrödinger Bridge

Gaussian-Based Instance-Adaptive Intensity Modeling for Point-Supervised Facial Expression Spotting

Token Pruning Meets Audio: Investigating Unique Behaviors in Vision Transformer-Based Audio Classification

TOMATO: Assessing Visual Temporal Reasoning Capabilities in Multimodal Foundation Models

ToolACE: Winning the Points of LLM Function Calling

TopoDiffusionNet: A Topology-aware Diffusion Model

TopoGaussian: Inferring Internal Topology Structures from Visual Clues

TopoLM: brain-like spatio-functional organization in a topographic language model

Topological Zigzag Spaghetti for Diffusion-based Generation and Prediction on Graphs

TorchTitan: One-stop PyTorch native solution for production ready LLM pretraining

ToVE: Efficient Vision-Language Learning via Knowledge Transfer from Vision Experts

Toward Efficient Multi-Agent Exploration With Trajectory Entropy Maximization

Toward Generalizing Visual Brain Decoding to Unseen Subjects

Toward Guidance-Free AR Visual Generation via Condition Contrastive Alignment

Towards a Complete Logical Framework for GNN Expressiveness

Towards a learning theory of representation alignment

Towards a Theoretical Understanding of Synthetic Data in LLM Post-Training: A Reverse-Bottleneck Perspective

Towards a Unified and Verified Understanding of Group-Operation Networks

Towards Bridging Generalization and Expressivity of Graph Neural Networks

Towards Certification of Uncertainty Calibration under Adversarial Attacks

Towards counterfactual fairness through auxiliary variables

Towards Domain Adaptive Neural Contextual Bandits

Towards Effective Evaluations and Comparisons for LLM Unlearning Methods

Towards Empowerment Gain through Causal Structure Learning in Model-Based Reinforcement Learning

Towards Explaining the Power of Constant-depth Graph Neural Networks for Structured Linear Programming

Towards Faster Decentralized Stochastic Optimization with Communication Compression

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians

Towards Foundation Models for Mixed Integer Linear Programming

Towards Generalizable Reinforcement Learning via Causality-Guided Self-Adaptive Representations

Towards Generalization Bounds of GCNs for Adversarially Robust Node Classification

Towards Homogeneous Lexical Tone Decoding from Heterogeneous Intracranial Recordings

Towards hyperparameter-free optimization with differential privacy

Towards Improving Exploration through Sibling Augmented GFlowNets

Towards Interpreting Visual Information Processing in Vision-Language Models

Towards more rigorous evaluations of language models

Towards Optimal Multi-draft Speculative Decoding

Towards Principled Evaluations of Sparse Autoencoders for Interpretability and Control

Towards Realistic Data Generation for Real-World Super-Resolution

Towards Realistic UAV Vision-Language Navigation: Platform, Benchmark, and Methodology

Towards Robust and Parameter-Efficient Knowledge Unlearning for LLMs

Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning

Towards Unbiased Calibration using Meta-Regularization

Towards Understanding the Universality of Transformers for Next-Token Prediction

Towards Understanding Why Label Smoothing Degrades Selective Classification and How to Fix It

Towards Unified Human Motion-Language Understanding via Sparse Interpretable Characterization

Towards Universality: Studying Mechanistic Similarity Across Language Model Architectures

TPO: Aligning Large Language Models with Multi-branch & Multi-step Preference Trees

Toward Understanding In-context vs. In-weight Learning

TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policies

Tracing Representation Progression: Analyzing and Enhancing Layer-Wise Similarity

Tracking objects that change in appearance with phase synchrony

Tractable Multi-Agent Reinforcement Learning through Behavioral Economics

Training-Free Activation Sparsity in Large Language Models

Trained Transformer Classifiers Generalize and Exhibit Benign Overfitting In-Context

Training-Free Dataset Pruning for Instance Segmentation

Training Free Exponential Context Extension via Cascading KV Cache

Training Free Guided Flow-Matching with Optimal Control

Training Language Models on Synthetic Edit Sequences Improves Code Synthesis

Training LLMs over Neurally Compressed Text

Training Nonlinear Transformers for Chain-of-Thought Inference: A Theoretical Generalization Analysis

Training One-Dimensional Graph Neural Networks is NP-Hard

Training Robust Ensembles Requires Rethinking Lipschitz Continuity

Train Small, Infer Large: Memory-Efficient LoRA Training for Large Language Models

Trajectory attention for fine-grained video motion control

Trajectory-Class-Aware Multi-Agent Reinforcement Learning

Trajectory-LLM: A Language-based Data Generator for Trajectory Prediction in Autonomous Driving

Transformer Encoder Satisfiability: Complexity and Impact on Formal Reasoning

Transformer Learns Optimal Variable Selection in Group-Sparse Classification

Transformer Meets Twicing: Harnessing Unattended Residual Information

Transformers are Universal In-context Learners

Transformers Can Learn Temporal Difference Methods for In-Context Reinforcement Learning

Transformers Handle Endogeneity in In-Context Linear Regression

Transformers Learn Low Sensitivity Functions: Investigations and Implications

Transformers Learn to Implement Multi-step Gradient Descent with Chain of Thought

Transformers Provably Learn Two-Mixture of Linear Classification via Gradient Flow

Transformer-Squared: Self-adaptive LLMs

Transformers Provably Solve Parity Efficiently with Chain of Thought

Tree of Attributes Prompt Learning for Vision-Language Models

TRENDy: Temporal Regression of Effective Nonlinear Dynamics

Triples as the Key: Structuring Makes Decomposition and Verification Easier in LLM-based TableQA

Trust or Escalate: LLM Judges with Provable Guarantees for Human Agreement

TSC-Net: Prediction of Pedestrian Trajectories by Trajectory-Scene-Cell Classification

T-Stitch: Accelerating Sampling in Pre-Trained Diffusion Models with Trajectory Stitching

TSVD: Bridging Theory and Practice in Continual Learning with Pre-trained Models

TTVD: Towards a Geometric Framework for Test-Time Adaptation Based on Voronoi Diagram

Tuning-Free Bilevel Optimization: New Algorithms and Convergence Analysis

TVNet: A Novel Time Series Analysis Method Based on Dynamic Convolution and 3D-Variation

TweedieMix: Improving Multi-Concept Fusion for Diffusion-based Image/Video Generation

Two Effects, One Trigger: On the Modality Gap, Object Bias, and Information Imbalance in Contrastive Vision-Language Models

Two Sparse Matrices are Better than One: Sparsifying Neural Networks with Double Sparse Factorization

TypedThinker: Diversify Large Language Model Reasoning with Typed Thinking

UGMathBench: A Diverse and Dynamic Benchmark for Undergraduate-Level Mathematical Reasoning with Large Language Models

Uncertainty-Aware Decoding with Minimum Bayes Risk

Uncertainty Herding: One Active Learning Method for All Label Budgets

Uncertainty modeling for fine-tuned implicit functions

Uncovering Gaps in How Humans and LLMs Interpret Subjective Language

Uncovering Latent Memories in Large Language Models

Uncovering Overfitting in Large Language Model Editing

Understanding and Mitigating Bottlenecks of State Space Models through the Lens of Recency and Over-smoothing

Understanding Factual Recall in Transformers via Associative Memories

Understanding Methods for Scalable MCTS

Understanding the Impacts of GenAI Requires Understanding the Impact of Anthropomorphic AI

Understanding Warmup-Stable-Decay Learning Rates: A River Valley Loss Landscape View

Unearthing Skill-level Insights for Understanding Trade-offs of Foundation Models

U-Nets as Belief Propagation: Efficient Classification, Denoising, and Diffusion in Generative Hierarchical Models

Uni$^2$Det: Unified and Universal Framework for Prompt-Guided Multi-dataset 3D Detection

UniDetox: Universal Detoxification of Large Language Models via Dataset Distillation

UniDrive: Towards Universal Driving Perception Across Camera Configurations

Unified Convergence Analysis for Score-Based Diffusion Models with Deterministic Samplers

Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark

UniGEM: A Unified Approach to Generation and Property Prediction for Molecules

uniINF: Best-of-Both-Worlds Algorithm for Parameter-Free Heavy-Tailed MABs

Union-over-Intersections: Object Detection beyond Winner-Takes-All

UniRestore3D: A Scalable Framework For General Shape Restoration

Uni-Sign: Toward Unified Sign Language Understanding at Scale

Universal generalization guarantees for Wasserstein distributionally robust models

Universal Sharpness Dynamics in Neural Network Training: Fixed Point Analysis, Edge of Stability, and Route to Chaos

UniWav: Towards Unified Pre-training for Speech Representation Learning and Generation

Unlearn and Burn: Adversarial Machine Unlearning Requests Destroy Model Accuracy

Unlearning-based Neural Interpretations

Unlearning or Obfuscating? Jogging the Memory of Unlearned LLMs via Benign Relearning

Unleashing the Potential of Vision-Language Pre-Training for 3D Zero-Shot Lesion Segmentation via Mask-Attribute Alignment

Unlocking Efficient, Scalable, and Continual Knowledge Editing with Basis-Level Representation Fine-Tuning

Unlocking Global Optimality in Bilevel Optimization: A Pilot Study

Unlocking Guidance for Discrete State-Space Diffusion and Flow Models

Unlocking the Potential of Model Calibration in Federated Learning

Unsupervised Disentanglement of Content and Style via Variance-Invariance Constraints

Unsupervised Zero-Shot Reinforcement Learning via Dual-Value Forward-Backward Representation

Unveiling the Magic of Code Reasoning through Hypothesis Decomposition and Amendment

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs

URLOST: Unsupervised Representation Learning without Stationarity or Topology

U-shaped and Inverted-U Scaling behind Emergent Abilities of Large Language Models

Utilitarian Algorithm Configuration for Infinite Parameter Spaces

UTILITY: Utilizing Explainable Reinforcement Learning to Improve Reinforcement Learning

VAE-Var: Variational Autoencoder-Enhanced Variational Methods for Data Assimilation in Meteorology

Valid Conformal Prediction for Dynamic GNNs

Variance-Reducing Couplings for Random Features

Variational Bayesian Pseudo-Coreset

Varying Shades of Wrong: Aligning LLMs with Wrong Answers Only

VCR: Pixel-Level Complex Reasoning by Restoring Occluded Text

Vec2Face: Scaling Face Dataset Generation with Loosely Constrained Vectors

Vector-ICL: In-context Learning with Continuous Vector Representations

Verifying Properties of Binary Neural Networks Using Sparse Polynomial Optimization

Vertical Federated Learning with Missing Features During Training and Inference

VibeCheck: Discover and Quantify Qualitative Differences in Large Language Models

ViBiDSampler: Enhancing Video Interpolation Using Bidirectional Diffusion Sampler

VICtoR: Learning Hierarchical Vision-Instruction Correlation Rewards for Long-horizon Manipulation

Video Action Differencing

VideoGLUE: Video General Understanding Evaluation of Foundation Models

VideoGrain: Modulating Space-Time Attention for Multi-Grained Video Editing

Video In-context Learning: Autoregressive Transformers are Zero-Shot Video Imitators

VideoPhy: Evaluating Physical Commonsense for Video Generation

Video-STaR: Self-Training Enables Video Instruction Tuning with Any Supervision

VideoWebArena: Evaluating Long Context Multimodal Agents with Video Understanding Web Tasks

ViDiT-Q: Efficient and Accurate Quantization of Diffusion Transformers for Image and Video Generation

ViSAGe: Video-to-Spatial Audio Generation

Vision and Language Synergy for Rehearsal Free Continual Learning

Vision CNNs trained to estimate spatial latents learned similar ventral-stream-aligned representations

Vision-RWKV: Efficient and Scalable Visual Perception with RWKV-Like Architectures

VisualAgentBench: Towards Large Multimodal Models as Visual Foundation Agents

Visual Agents as Fast and Slow Thinkers

Visual Haystacks: A Vision-Centric Needle-In-A-Haystack Benchmark

Visually Consistent Hierarchical Image Classification

Visually Guided Decoding: Gradient-Free Hard Prompt Inversion with Language Models

Visual-O1: Understanding Ambiguous Instructions via Multi-modal Multi-turn Chain-of-thoughts Reasoning

VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning

VLM2Vec: Training Vision-Language Models for Massive Multimodal Embedding Tasks

VLMaterial: Procedural Material Generation with Large Vision-Language Models

VOILA: Evaluation of MLLMs For Perceptual Understanding and Analogical Reasoning

VTDexManip: A Dataset and Benchmark for Visual-tactile Pretraining and Dexterous Manipulation with Reinforcement Learning

Walk the Talk? Measuring the Faithfulness of Large Language Model Explanations

Ward: Provable RAG Dataset Inference via LLM Watermarks

Wasserstein Distances, Neuronal Entanglement, and Sparsity

Warm Diffusion: Recipe for Blur-Noise Mixture Diffusion Models

Watch Less, Do More: Implicit Skill Discovery for Video-Conditioned Policy

Wavelet-based Positional Representation for Long Context

Wayward Concepts In Large Multimodal Models

Weakly-Supervised Affordance Grounding Guided by Part-Level Semantic Priors

Weak to Strong Generalization for Large Language Models with Multi-capabilities

Weak-to-Strong Generalization Through the Data-Centric Lens

WeatherGFM: Learning a Weather Generalist Foundation Model via In-context Learning

WebRL: Training LLM Web Agents via Self-Evolving Online Curriculum Reinforcement Learning

Weighted Multi-Prompt Learning with Description-free Large Language Model Distillation

Weighted Point Set Embedding for Multimodal Contrastive Learning Toward Optimal Similarity Metric

What Are Good Positional Encodings for Directed Graphs?

What Does It Mean to Be a Transformer? Insights from a Theoretical Hessian Analysis

What Do You See in Common? Learning Hierarchical Prototypes over Tree-of-Life to Discover Evolutionary Traits

What is Wrong with Perplexity for Long-context Language Modeling?

What Makes Large Language Models Reason in (Multi-Turn) Code Generation?

What Matters in Learning from Large-Scale Datasets for Robot Manipulation

What Matters When Repurposing Diffusion Models for General Dense Perception Tasks?

What Secrets Do Your Manifolds Hold? Understanding the Local Geometry of Generative Models

What's New in My Data? Novelty Exploration via Contrastive Generation

What's the Move? Hybrid Imitation Learning via Salient Points

What to align in multimodal contrastive learning?

When does compositional structure yield compositional generalization? A kernel theory.

When GNNs meet symmetry in ILPs: an orbit-based feature augmentation approach

When Graph Neural Networks Meet Dynamic Mode Decomposition

When is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers

When narrower is better: the narrow width limit of Bayesian parallel branching neural networks

When Prompt Engineering Meets Software Engineering: CNL-P as Natural and Robust "APIs'' for Human-AI Interaction

Which Tasks Should Be Compressed Together? A Causal Discovery Approach for Efficient Multi-Task Representation Compression

Why In-Context Learning Models are Good Few-Shot Learners?

Why RoPE Struggles to Maintain Long-Term Decay in Long Sequences?

Wide Neural Networks Trained with Weight Decay Provably Exhibit Neural Collapse

WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild

WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct

Words in Motion: Extracting Interpretable Control Vectors for Motion Transformers

World Model on Million-Length Video And Language With Blockwise RingAttention

W-PCA Based Gradient-Free Proxy for Efficient Search of Lightweight Language Models

XAIguiFormer: explainable artificial intelligence guided transformer for brain disorder identification

X-ALMA: Plug & Play Modules and Adaptive Rejection for Quality Translation at Scale

X-Fi: A Modality-Invariant Foundation Model for Multimodal Human Sensing

xFinder: Large Language Models as Automated Evaluators for Reliable Evaluation

You Only Prune Once: Designing Calibration-Free Model Compression With Policy Learning

Your Mixture-of-Experts LLM Is Secretly an Embedding Model for Free

YouTube-SL-25: A Large-Scale, Open-Domain Multilingual Sign Language Parallel Corpus

Zero-cost Proxy for Adversarial Robustness Evaluation

ZeroDiff: Solidified Visual-semantic Correlation in Zero-Shot Learning

Zero-shot forecasting of chaotic systems

Zero-Shot Natural Language Explanations

Zeroth-Order Fine-Tuning of LLMs with Transferable Static Sparsity

ZETA: Leveraging $Z$-order Curves for Efficient Top-$k$ Attention

ZooProbe: A Data Engine for Evaluating, Exploring, and Evolving Large-scale Training Data for Multimodal LLMs

Federated Class-Incremental Learning: A Hybrid Approach Using Latent Exemplars and Data-Free Techniques to Address Local and Global Forgetting