214 citations · 461 across the 18 of their papers we have counts for
18 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…
Unifying biomedical knowledge in a modern multimodal graph
Lucas Vittor, Ayush Noori, Iñaki Arango +5
Biomedical knowledge graphs (KGs) are widely used in the life sciences, yet many are derived from unstructured documents and therefore lack schema-level constraints, whereas graphs…
Graph AI generates neurological hypotheses validated in molecular, organoid, and clinical systems
Ayush Noori, Joaquín Polonuer, Katharina Meyer +26
Neurological diseases are the leading global cause of disability, yet most lack disease-modifying treatments. We present PROTON, a heterogeneous graph transformer that generates te…
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…
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…
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…
Language agents achieve superhuman synthesis of scientific knowledge
Michael D. Skarlinski, Sam Cox, Jon M. Laurent +6
Language models are known to hallucinate incorrect information, and it is unclear if they are sufficiently accurate and reliable for use in scientific research. We developed a rigo…
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.…
PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
Jakub Lála, Odhran O'Donoghue, Aleksandar Shtedritski +3
Large Language Models (LLMs) generalize well across language tasks, but suffer from hallucinations and uninterpretability, making it difficult to assess their accuracy without grou…
BioPlanner: Automatic Evaluation of LLMs on Protocol Planning in Biology
Odhran O'Donoghue, Aleksandar Shtedritski, John Ginger +4
The ability to automatically generate accurate protocols for scientific experiments would represent a major step towards the automation of science. Large Language Models (LLMs) hav…
14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon
Kevin Maik Jablonka, Qianxiang Ai, Alexander Al-Feghali +50
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To…
Probability Theory without Bayes' Rule
Samuel G. Rodriques
Within the Kolmogorov theory of probability, Bayes' rule allows one to perform statistical inference by relating conditional probabilities to unconditional probabilities. As we sho…
Bounding Polynomial Entanglement Measures for Mixed States
Samuel Rodriques, Nilanjana Datta, Peter J. Love
We generalize the notion of the best separable approximation (BSA) and best W-class approximation (BWA) to arbitrary pure state entanglement measures, defining the best zero- ap…
Multipartite Quantum Entanglement Evolution in Photosynthetic Complexes
Jing Zhu, Sabre Kais, Alán Aspuru-Guzik +3
We investigate the evolution of entanglement in the Fenna-Matthew-Olson (FMO) complex based on simulations using the scaled hierarchy equation of motion (HEOM) approach. We examine…