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Advaith Sridhar introduces Discovered Materials, a startup building AI scientists to discover new semiconductor materials, releasing hundreds of discoveries and a benchmark.
The Proto team from BrianHie's lab will participate in the re:AGENT hackathon (August 15-16 in San Francisco), a weekend for building AI scientists, datasets, and pipelines for biological design. Applications close July 30.
This paper introduces the Hypothesis Evolution Protocol (HEP) for LLM agents, which makes hypothesis generation, testing, and belief updates explicit and auditable. Experiments on materials-science tasks show that HEP-equipped agents generalize across research questions and become more effective with stronger base LLMs.
Introduces SDABench, a benchmark evaluating LLMs on six scientific analysis capabilities across five domains, finding models struggle with tasks requiring assumption selection and mechanistic reasoning.
CausaLab is a scalable environment for evaluating LLM agents on interactive causal discovery, assessing both predictive accuracy and faithful recovery of underlying causal mechanisms. Experiments reveal a gap between prediction and mechanism recovery, highlighting limits in current LLM agents as experimental causal reasoners.
This paper introduces AiraXiv, an AI-driven open-access platform designed for both human and AI scientists, featuring interactive UI and MCP-based interactions to support continuous, feedback-driven paper iteration and scalable research infrastructure.
A study of 25,000 AI scientist trials finds the agents ignore evidence 68% of the time and rarely revise hypotheses, showing popular scaffolding fixes don’t instill true scientific reasoning.