ATHENA Masters 212 Biomedical Tools via Two-Stage Self-Learning
marinkazitnik · x · 2026-07-07
ATHENA is capable of identifying missing information and invoking the correct tools at each reasoning step, covering 212 biomedical tools to ultimately form an inspectable, complete reasoning chain. Because manual annotation at this scale is infeasible, training was divided into two stages: the first stage involved agents autonomously generating tool-calling trajectories for supervised fine-tuning; the second stage utilized reinforcement learning with scientific feedback rewards to further optimize the policy, enabling large-scale training without human annotation.
More from Research
- 3D ResNet Paper Crosses 3,000 Citations Eight Years After CVPR 2018 — HirokatuKataoka · 2026-09-11
- Jeff Heaton's Intro to the Math of Neural Networks eBook Is Free to Download — blaizedsouza · 2026-09-11
- Mathematician Daniel Litt Launches Problem Repo to Track Human vs AI Progress: 15 Problems, 1 Solved — littmath · 2026-09-11
- Open ECDSA.fail challenge uses AI agents to shrink Shor's-algorithm quantum circuits for Bitcoin keys — StefanoGogioso · 2026-09-11
- Alex Townsend posts 200 open problems in numerical linear algebra for humans and AI agents — IgorCarron · 2026-09-11
- Navier-Stokes, Riemann, P vs NP: what this week's math buzzwords mean for you — koltregaskes · 2026-09-11