Socials

This discussion booth explores the emerging intersection of machine learning and smell technology, also known as digital olfaction. The space is designed for researchers, students, builders, and curious attendees interested in how AI can be used to detect, classify, generate, and interpret scent-related data. Topics may include electronic noses, olfactory sensing, multimodal AI, applications in healthcare, food quality, environmental monitoring, fragrance, and human-computer interaction. The goal is to create an open and interdisciplinary conversation around both the technical challenges and the creative opportunities in this fast-growing field. Whether you work directly on ML models for chemical signal analysis or are simply interested in the future of smell interfaces and sensory intelligence, this booth offers a place to exchange ideas, share projects, discuss collaborations, and connect with others working at the frontier of AI and olfaction.

Real estate is one of the most financially significant and emotionally complex decisions people make — yet digital experiences have historically optimized for information retrieval (i.e., home search) rather than intelligent and personalized guidance. In this talk, I’ll share how we are evolving from high-IQ systems that answer questions to agentic systems that demonstrate AQ (Agentic Quotient) and EQ (Emotional Intelligence). By combining deep, panoptic understanding of homes and users with advanced reasoning, planning, and tool use, we are building AI copilots that don’t just respond passively — they guide, anticipate, and act proactively. I’ll discuss the key technical challenges, and how we operationalize agentic AI in production, architect for trust in high-stakes decisions, and design experiences that transform the home journey from fragmented search into confident, personalized progression.

As AI reshapes industries, compensation is changing faster than researchers can track. New labs, startups, and top companies are competing for talent with vastly different pay structures, currencies, and cultural norms. Yet most researchers are never formally taught how to understand their worth or navigate these systems. The result is an uneven landscape where brilliant minds often make life-changing decisions without the information they need.

The session begins with a concise, data-driven talk on current AI compensation and negotiation trends, grounded in real stories and case studies. From there, a fireside chat and open Q&A invite candid, experience-driven insights from researchers who have navigated these conversations firsthand.

Key Takeaways for attendees: * How to evaluate compensation (salary, equity, bonuses) across industry roles such as Research/Applied/Data Scientists, Research/Machine Learning/Software Engineers, and more * How to compare global opportunities and account for regional differences in pay structures * How to identify leverage points and negotiate effectively at different career stages and levels * How to respond to pushback and recognize red vs. green flags in job offers * How to negotiate the deadline, and avoid having an offer rescinded * How to advocate for yourself without counteroffers and amidst having fears

Physical AI — robots, autonomous vehicles, and embodied agents — is approaching a genuine inflection point. Foundation models for real-world interaction are becoming viable, hardware costs are dropping, and developer interest is surging. Yet most teams building in this space are still stitching together their development stack from incompatible pieces, and it is slowing them down. The core bottlenecks are well understood but rarely addressed together. Real-world robotic behavior cannot be learned from synthetic data alone — collecting, annotating, and validating diverse physical-world data at scale is a full operational discipline. Training multimodal vision-language-action models demands infrastructure purpose-built for the task. And evaluating whether a model actually works in the physical world requires benchmarking approaches that go far beyond standard leaderboards. This social will bring together researchers and practitioners to examine all three problems in parallel. Short talks from speakers with hands-on experience in physical AI development will cover the state of real-world data pipelines, what purpose-built infrastructure for physical AI actually looks like, and how the community is approaching evaluation for embodied systems. An open discussion will follow, focused on where the biggest unsolved problems lie and how the research community can contribute.

Real estate is one of the most financially significant and emotionally complex decisions people make — yet digital experiences have historically optimized for information retrieval (i.e., home search) rather than intelligent and personalized guidance. In this talk, I’ll share how we are evolving from high-IQ systems that answer questions to agentic systems that demonstrate AQ (Agentic Quotient) and EQ (Emotional Intelligence). By combining deep, panoptic understanding of homes and users with advanced reasoning, planning, and tool use, we are building AI copilots that don’t just respond passively — they guide, anticipate, and act proactively. I’ll discuss the key technical challenges, and how we operationalize agentic AI in production, architect for trust in high-stakes decisions, and design experiences that transform the home journey from fragmented search into confident, personalized progression.

Join us for an evening hosted by a team building video AI systems for real-world deployment, bringing together researchers, engineers, and builders working at the frontier of video AI. This is a relaxed, conversation-first gathering connecting people across robotics, self-driving, and embodied AI.

We’ll focus on a new shift in how video is used in real-world AI: moving beyond static labeling toward systems that can search, reason over, and validate events directly from raw footage. Discussions will explore approaches that combine vision-language models, motion understanding, and multi-stage validation to turn large-scale video into something you can actually act on.

No formal talks, just high-signal conversations with people actively building and deploying these systems.

The goal is to go beyond surface-level ideas and get into what it actually takes to make Physical AI work reliably in the real world. If you are working on autonomy, VLMs, or production AI systems, this is a chance to exchange ideas and shape what comes next.

Step away from the booths and join us for a relaxed afternoon tea - a chance to connect with fellow builders, researchers, and teams over coffee, tea, and great conversation. We’ll also be hosting informal office hours, where you can chat directly with our technical and research teams, ask questions, share ideas, and get hands-on guidance.

World models have recently gained popularity thanks to impressive results and the availability of data. However, no consensus has been reached on how they should help improve decision-making. This social aims to foster discussions around the role of world models in decision-making.

World models are used in many ways. Some approaches use world models as synthetic data generators. Others leverage them at test time to reason or evaluate policies. Video models, in addition to an inverse dynamics model, can be used to infer actions. Discussing those choices appears to be essential to assess their role in decision-making.

When data is scarce, some methods only learn a world model to improve representation learning and rely on model-free methods for decision-making to avoid hallucinations. We believe it is fruitful to discuss the scenarios for which world models can be trusted.

The exchanges can focus on discussing in which situations world models have an edge over algorithms that do not directly learn the transition dynamics. It is also not yet clear whether world models are more relevant at certain levels of hierarchy.

Finally, discussing why world models can enable better generalization can provide an answer to the question asked in this social.

The Women in Machine Learning (WiML) initiative, founded in 2006, was created to connect and support the relatively small but growing community of researchers in ML who identify as women or nonbinary. Over the years, WiML events at conferences such as NeurIPS, ICML, ICLR, and other conferences have highlighted cutting-edge research, fostered mentorship, and created space for meaningful technical exchange. For ICLR, we propose a WiML Social that keeps WiML’s core mission while emphasising interaction and networking. The event will feature a panel discussion, facilitated roundtables, and structured networking activities designed to spark in-depth conversations and future collaborations. Building on the success of the highly interactive WiML formats at ICLR in the past, we will include small-group discussions, allowing participants to engage directly on open research questions and career paths. The goals remain the same: to celebrate the work of researchers who identify as women or nonbinary, to create opportunities for junior and senior participants to connect, and to strengthen community ties within the broader ICLR ecosystem.

As AI agents increasingly operate in shared environments — negotiating, transacting, and making joint decisions — questions of coordination and cooperation become inseparable from questions of system design. How do we incentivize cooperation among autonomous agents when no single party controls the system? What coordination protocols, commitment devices, and oversight mechanisms work in distributed settings? How do we prevent collusion and ensure robustness as agent networks scale? This social is hosted by Cooperative AI Foundation and The Institute for Decentralized AI as a gathering for researchers working on multi-agent systems, mechanism design, AI safety, distributed systems, and related areas who are interested in how cooperative and decentralized approaches to AI intersect and inform each other. Drinks will be provided.

Let's be honest, nobody teaches researchers how to negotiate in grad school. They spend years mastering ML, publishing papers, and building models, and then suddenly they're staring at an offer letter with no idea if it's good or how to push back.

That's exactly why we're hosting this social.

Rora has been in the trenches with researchers since 2017, we've worked with 1,000+ people navigating offers from top AI labs, startups, and big tech, and have helped negotiate over $1 billion in total compensation. We've seen it all, and we want to share what actually works. Come hang out, grab a drink, and ask the questions you've been too afraid to ask your recruiter.

Whether you're mid-process, just starting to explore, or already have an offer in hand, there's something here for you. No slides, no fluff — just real talk from people who've been doing this for a while.

Large language models are increasingly used to translate natural language into SQL. But how reliable are they in real-world settings?

In this session, we present a focused evaluation framework for measuring NL-to-SQL performance, including execution correctness, robustness, and query efficiency under varying levels of database context. We’ll discuss how structured QA and validation approaches can help move LLM systems from benchmark success to production reliability.

We invite researchers and practitioners working on NL-to-SQL systems, LLM evaluation, and database applications to participate in the discussion and share perspectives from real-world deployments.

This affinity group is for NLP and AI researchers with a prior background or interest in the humanities. As AI research becomes increasingly interdisciplinary - drawing from theories and methods from outside of computer science - it is important to engage with what the humanities have to offer in terms of theories and methods to study language, culture, narrative, subjectivity, mind, emotion, etc. Disciplines like literary and cultural studies, media studies, philosophy, and history have a great deal to offer in terms of studying, building and improving language systems. Topics of interest include (but are not limited to): co intelligence and co-creative systems, narrative understanding, cultural analytics, literary NLP, AI literacy, AI ethics, culture and cognition, etc.

This social is an informal community gathering organised by Queer in AI to foster connection, visibility, and mutual support among LGBTQ+ researchers and allies in AI/ML. The event provides a welcoming space for queer scientists, students, and practitioners to meet, share experiences, and build professional and personal networks within the broader research community.

Benchmark contamination is no longer a theoretical concern. As frontier models are trained on open-web data, public test sets are routinely absorbed into pre-training corpora — and beyond passive contamination, labs are known to actively optimize against known benchmarks and selectively report favorable results. When a model claims state-of-the-art on a public leaderboard, it is increasingly unclear whether that reflects genuine generalization or familiarity with the test. Private-only benchmarks — never released publicly, evaluated under controlled conditions, and continuously refreshed — offer a structural solution. If a model cannot train against a benchmark it has never seen, contamination becomes impossible by design. Built across capability families rather than isolated skills, such benchmarks can also surface cross-domain failure modes that narrow public evaluations miss entirely. This social will examine what private, holistic evaluation infrastructure could look like in practice, with short talks from practitioners followed by open discussion on what it would take for the community to coalesce around shared private evaluation standards.

As AI agents increasingly operate in shared environments — negotiating, transacting, and making joint decisions — questions of coordination and cooperation become inseparable from questions of system design. How do we incentivize cooperation among autonomous agents when no single party controls the system? What coordination protocols, commitment devices, and oversight mechanisms work in distributed settings? How do we prevent collusion and ensure robustness as agent networks scale?

This social is hosted by Cooperative AI Foundation and The Institute for Decentralized AI as a gathering for researchers working on multi-agent systems, mechanism design, AI safety, distributed systems, and related areas who are interested in how cooperative and decentralized approaches to AI intersect and inform each other. Drinks will be provided.

More simply put: LLMs for Metascience. This social is focused on facilitating conversations from industry, academia, and government about how we are all using AI to understand the current landscape of AI research. What’s coming at us the fastest? What are the problems we’ve effectively “solved” ? Where can existing methods be applied to support progress in overlooked areas?

This is meant to be a relaxed, discussion-driven space. We’re excited for a cross-sector exchange where people can share tools, workflows, rough ideas, and open questions, as well as the challenges of using AI systems to reason about science itself. Input from those who conduct, fund, and review research will be especially valuable in shaping a more complete picture of the different components in the AI research landscape.

World models have emerged as a unifying thread across video generation, model-based reinforcement learning, and robotic planning — yet the communities working on these problems often don't overlap at conferences. This Social brings together researchers working on learned simulators, video prediction, world models for decision-making, and sim-to-real transfer for an informal, discussion-first session.

Scientific ML is more than applying ML to science — it's about letting domain structure shape the model itself. In this social, we bring together researchers working across domains (neuroscience, PDEs, physics simulations, and beyond) to discuss: What is the urgent 'X' in your X-informed AI? How do you identify the right inductive bias? How do we build a community that supports this? Come share how your domain shapes your models.

AI for Science is rapidly emerging as a key area where machine learning can accelerate discovery in domains such as materials science, biology, physics, and mathematics. Progress in this space increasingly depends on collaboration between machine learning researchers and domain scientists, as well as open ecosystems of data, models, and tools. This socials aims to create an informal space at ICLR for researchers interested in AI for Science and open collaboration to connect, exchange ideas, and build new collaborations.

The socials will bring together participants from several ICLR workshops related to AI for Science, including AI4Mat, FM4Science, AI&PDE, and Sci4DL, and foster interaction across these communities. The session will focus on practical challenges and opportunities in building open scientific ecosystems, including open datasets and benchmarks, open-source tools and foundation models for science, cross-disciplinary collaboration, and community-driven initiatives.

The format will emphasize interaction and networking with brief opening remarks, structured speed networking, themed small-group discussions, and a short open panel conversation where participants can share insights and identify opportunities for collaboration. The social will also provide a welcoming environment for students and early-career researchers to engage with both academic and industry researchers working on AI-driven scientific discovery

Large language models are increasingly used to translate natural language into SQL. But how reliable are they in real-world settings? In this session, We present a focused evaluation framework for measuring NL-to-SQL performance, including execution correctness, robustness, and query efficiency under varying levels of database context. We’ll discuss how structured QA and validation approaches can help move LLM systems from benchmark success to production reliability. We invite researchers and practitioners working on NL-to-SQL systems, LLM evaluation, and database applications to participate in the discussion and share perspectives from real-world deployments.

This discussion booth explores the emerging intersection of machine learning and smell technology, also known as digital olfaction. The space is designed for researchers, students, builders, and curious attendees interested in how AI can be used to detect, classify, generate, and interpret scent-related data. Topics may include electronic noses, olfactory sensing, multimodal AI, applications in healthcare, food quality, environmental monitoring, fragrance, and human-computer interaction. The goal is to create an open and interdisciplinary conversation around both the technical challenges and the creative opportunities in this fast-growing field. Whether you work directly on ML models for chemical signal analysis or are simply interested in the future of smell interfaces and sensory intelligence, this booth offers a place to exchange ideas, share projects, discuss collaborations, and connect with others working at the frontier of AI and olfaction.

As AI reshapes industries, compensation is changing faster than researchers can track. New labs, startups, and top companies are competing for talent with vastly different pay structures, currencies, and cultural norms. Yet most researchers are never formally taught how to understand their worth or navigate these systems. The result is an uneven landscape where brilliant minds often make life-changing decisions without the information they need.

The session begins with a concise, data-driven talk on current AI compensation and negotiation trends, grounded in real stories and case studies. From there, a fireside chat and open Q&A invite candid, experience-driven insights from researchers who have navigated these conversations firsthand.

Key Takeaways for attendees: * How to evaluate compensation (salary, equity, bonuses) across industry roles such as Research/Applied/Data Scientists, Research/Machine Learning/Software Engineers, and more * How to compare global opportunities and account for regional differences in pay structures * How to identify leverage points and negotiate effectively at different career stages and levels * How to respond to pushback and recognize red vs. green flags in job offers * How to negotiate the deadline, and avoid having an offer rescinded * How to advocate for yourself without counteroffers and amidst having fears