14 citations · 33 across the 6 of their papers we have counts for
6 papers
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…
MarkushGlyph and OCSRGlyph: Improved Chemical Structure Recognition
Alex Andonian, Samuel G Rodriques, Andrew D White +1
Chemical structures appear in patents and the scientific literature as images. For programmatic usage, such as indexing in databases or constructing machine learning model training…
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…
Training a Scientific Reasoning Model for Chemistry
Siddharth M. Narayanan, James D. Braza, Ryan-Rhys Griffiths +6
Reasoning models are large language models that emit a long chain-of-thought before answering, providing both higher accuracy and explicit reasoning for their response. A major que…
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…
BixBench: a Comprehensive Benchmark for LLM-based Agents in Computational Biology
Ludovico Mitchener, Jon M Laurent, Alex Andonian +6
Large Language Models (LLMs) and LLM-based agents show great promise in accelerating scientific research. Existing benchmarks for measuring this potential and guiding future develo…