2 citations · 7 across the 18 of their papers we have counts for
4 papers · 1 filter
Evidence-Informed LLM Beliefs for Continual Scientific Discovery
Dhruv Agarwal, Reece Adamson, Andrew McCallum +3
Open-ended scientific discovery with large language models (LLMs) increasingly operates as a long-horizon loop of hypothesis search and verification, where a reward signal guides w…
AstaBench: Rigorous Benchmarking of AI Agents with a Scientific Research Suite
Jonathan Bragg, Mike D'Arcy, Nishant Balepur +36
AI agents hold the potential to revolutionize scientific productivity by automating literature reviews, replicating experiments, analyzing data, and even proposing new directions o…
ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning
Bill Yuchen Lin, Ronan Le Bras, Kyle Richardson +4
We investigate the logical reasoning capabilities of large language models (LLMs) and their scalability in complex non-monotonic reasoning. To this end, we introduce ZebraLogic, a…
SUPER: Evaluating Agents on Setting Up and Executing Tasks from Research Repositories
Ben Bogin, Kejuan Yang, Shashank Gupta +5
Given that Large Language Models (LLMs) have made significant progress in writing code, can they now be used to autonomously reproduce results from research repositories? Such a ca…