44 citations · 107 across the 9 of their papers we have counts for
6 papers · 1 filter
BixBench3: Benchmarking AI agents on research-study-scale computational biology tasks
Zane Koch, Asmamaw T. Wassie, Javier Valdes-Aleman +5
Artificial intelligence (AI) promises to accelerate biological research by automating computational analyses. Yet the ability of AI agents to execute on computational biology at th…
LABBench2: An Improved Benchmark for AI Systems Performing Biology Research
Jon M Laurent, Albert Bou, Michael Pieler +9
Optimism for accelerating scientific discovery with AI continues to grow. Current applications of AI in scientific research range from training dedicated foundation models on scien…
Kosmos: An AI Scientist for Autonomous Discovery
Ludovico Mitchener, Angela Yiu, Benjamin Chang +34
Data-driven scientific discovery requires iterative cycles of literature search, hypothesis generation, and data analysis. Substantial progress has been made towards AI agents that…
Robin: A multi-agent system for automating scientific discovery
Ali Essam Ghareeb, Benjamin Chang, Ludovico Mitchener +7
Scientific discovery is driven by the iterative process of background research, hypothesis generation, experimentation, and data analysis. Despite recent advancements in applying a…
Aviary: training language agents on challenging scientific tasks
Siddharth Narayanan, James D. Braza, Ryan-Rhys Griffiths +8
Solving complex real-world tasks requires cycles of actions and observations. This is particularly true in science, where tasks require many cycles of analysis, tool use, and exper…
LAB-Bench: Measuring Capabilities of Language Models for Biology Research
Jon M. Laurent, Joseph D. Janizek, Michael Ruzo +6
There is widespread optimism that frontier Large Language Models (LLMs) and LLM-augmented systems have the potential to rapidly accelerate scientific discovery across disciplines.…