Poster Fri, Apr 24, 2026 • 6:30 AM – 9:00 AM PDT Pavilion 3 P3-#420

AstaBench: Rigorous Benchmarking of AI Agents with a Scientific Research Suite

Jonathan Bragg ⋅ Mike D'Arcy ⋅ Nishant Balepur ⋅ Dan Bareket ⋅ Bhavana Dalvi Mishra ⋅ Sergey Feldman ⋅ Dany Haddad ⋅ Jena Hwang ⋅ Peter Jansen ⋅ Varsha Kishore ⋅ Bodhisattwa Prasad Majumder ⋅ Aakanksha Naik ⋅ Sigal Rahamimov ⋅ Kyle Richardson ⋅ Amanpreet Singh ⋅ Harshit Surana ⋅ Aryeh Tiktinsky ⋅ Rosni Vasu ⋅ Guy Wiener ⋅ Chloe Anastasiades ⋅ Stefanus Candra ⋅ Jason Dunkelberger ⋅ Daniel Emery ⋅ Rob Evans ⋅ Malachi Hamada ⋅ Regan Huff ⋅ Rodney Kinney ⋅ Matt Latzke ⋅ Jaron Lochner ⋅ Ruben Lozano-Aguilera ⋅ Ngoc-Uyen Nguyen ⋅ Smita Rao ⋅ Amber Tanaka ⋅ Brooke Vlahos ⋅ Peter Clark ⋅ Doug Downey ⋅ Yoav Goldberg ⋅ Ashish Sabharwal ⋅ Daniel Weld

Abstract

AI agents hold the potential to revolutionize scientific productivity by automating literature reviews, replicating experiments, analyzing data, and even proposing new directions of inquiry; indeed, there are now many such agents, ranging from general-purpose "deep research" systems to specialized science-specific agents, such as AI Scientist and AIGS. Rigorous evaluation of these agents is critical for progress. Yet existing benchmarks fall short on several fronts: they often (1) lack reproducible agent tools necessary for a controlled comparison of core agentic capabilities; (2) do not account for confounding variables such as model cost and tool access; (3) do not provide standardized interfaces for quick agent prototyping and evaluation; (4) fail to provide holistic, product-informed measures of real-world use cases such as science research; and (5) lack comprehensive baseline agents necessary to identify true advances. In response, we define principles and tooling for more rigorously benchmarking agents. Using these, we present AstaBench, a suite that provides a holistic measure of agentic ability to perform scientific research, comprising 2400+ problems spanning the entire scientific discovery process and multiple scientific domains, and including many problems inspired by actual user requests to deployed Asta agents. Our suite comes with the first scientific research environment with production-grade search tools that enable controlled, reproducible evaluation, better accounting for confounders. Alongside, we provide a comprehensive suite of nine science-optimized classes of Asta agents and numerous baselines. Our extensive evaluation of 57 agents across 22 agent classes reveals several interesting findings, most importantly that despite meaningful progress on certain individual aspects, AI remains far from solving the challenge of science research assistance.