arXiv:2608. 11341v1 Announce Type: new Abstract: Apollo did not reach the Moon merely because its engineers could solve difficult equations.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
OpenDiscoveryTrace is a public dataset of 558 complete AI scientific agent trajectories that records the reasoning process—thoughts, tool calls, observations, errors, revision triggers, and confidence—across 124 scientific tasks in drug discovery, materials science, genomics, and literature analysis. The dataset includes seven models (three frontier models and four open‑weight models) and 60 live‑retrieval variants, providing a balanced view of performance and error patterns. Pilot analysis shows that process traces reveal behavioral differences invisible to output‑only evaluation, such as differing error rates and types among frontier models.
arXiv:2609.15973v1 Announce Type: new Abstract: Foundation models have progressed from learning and reasoning over existing knowledge, to increasingly learning through action, tool use, and outcome f...
arXiv:2608. 06961v1 Announce Type: new Abstract: Early-stage molecular design is an iterative process, not just a task of generating molecules.
arXiv:2608. 02642v1 Announce Type: cross Abstract: Accelerating scientific discovery is among the most consequential applications of AI, and computational biomolecular simulation stands out as a particularly promising target within this broader effort.
arXiv:2606. 19245v1 Announce Type: new Abstract: Artificial intelligence (AI) agents promise to accelerate drug discovery by compressing interpretation and decision-making loops, but practical deployment requires trusted evaluation on realistic program decisions.
The paper extends Co‑Scientist, a Gemini‑based multi‑agent system, and validates it in real‑world scientific settings. In materials science it designed a safe precursor route for MXenes and achieved single‑attempt growth of monolayer MoS₂, MoSe₂, and WS₂. In biology it predicted swarming phenotypes of engineered E. coli, and in computer science it discovered a superior inference‑time scaling architecture for HealthBench. A double‑blind study with 30 experts showed that Co‑Scientist’s reliability modules reduce hallucination and plagiarism while improving research safety.