13 citations · 26 across the 9 of their papers we have counts for
5 papers · 1 filter
Amortising Bayesian Experimental Design for Sequential Information Gathering in LLMs
Jakob Hartmann, James Harvey, Jhonathan Navott +5
Large language models (LLMs) exhibit strong reasoning and world-knowledge capabilities, yet often struggle to gather information effectively across the multi-turn interactions requ…
Measuring Progress in Reasoning Toward Mathematical Discovery with Automatic Verification
Erik Y. Wang, Sumeet R. Motwani, James V. Roggeveen +9
Can AI make progress on important, unsolved mathematical problems? Large language models are now capable of sophisticated mathematical and scientific reasoning, but whether they ca…
MetaGFN: Exploring Distant Modes with Adapted Metadynamics for Continuous GFlowNets
Dominic Phillips, Flaviu Cipcigan
Generative Flow Networks (GFlowNets) are a class of generative models that sample objects in proportion to a specified reward function through a learned policy. They can be trained…
Machine Guided Discovery of Novel Carbon Capture Solvents
James L. McDonagh, Benjamin H. Wunsch, Stamatia Zavitsanou +5
The increasing importance of carbon capture technologies for deployment in remediating CO2 emissions, and thus the necessity to improve capture materials to allow scalability and e…
Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics
Payel Das, Tom Sercu, Kahini Wadhawan +12
De novo therapeutic design is challenged by a vast chemical repertoire and multiple constraints, e.g., high broad-spectrum potency and low toxicity. We propose CLaSS (Controlled La…