Introducing AutoScientists — a decentralized team of AI agents for long-running scientific experimentation. Powered by ClawInstitute. Most current AI scientist agents either run a single reasoning thread, or have a central planner assigning tasks. Real research isn't like that: productive directions shift over time, dead ends matter, and teams form around what's actually working. AutoScientists is built for that. There is no central orchestrator. Agents read a shared experimental state, propose experiments on a forum, critique each other before committing compute, self-organize into teams around the most promising research directions, share both wins and failures across teams, and retire directions that stop producing improvements. The whole search reorganizes itself as evidence accumulates. What it does ▸ On GPT nanochat training optimization, it reaches the same val_bpb in 34 experiments that autoresearch needs 65 for — a 1.9× speedup. Starting from a stronger champion where the single-agent loop saturates, AutoScientists accepts 7 improvements over 93 experiments while autoresearch accepts 0 over 100. ▸ On BioML-Bench (24 biomedical ML tasks spanning imaging, drug discovery, protein engineering, and single-cell omics), AutoScientists reaches a mean leaderboard percentile of 74.4%, beating the strongest prior biomedical agent by +8.3 points, and completes all 24 tasks. ▸ For ProteinGym supervised fitness prediction, AutoScientists discovers a Kermut extension on ACE2–Spike that lifts Spearman ρ from 0.747 → 0.840 (+12.5%). The same frozen recipe transfers across all 217 ProteinGym assays, improving the official average Spearman ρ from 0.657 to 0.700 (+6.5%) — a new SOTA on the supervised substitution benchmark. Joint work with @AdaFang_ and @marinkazitnik . 📄 Paper: arxiv.org/pdf/2605.28655 🌐 Project page: autoscientists.openscientist… 💻 Code: github.com/mims-harvard/Auto…

Introducing ATHENA: an AI agent for treatment reasoning across all FDA approved drugs since 1939, by stellar @GaoShanghua Paper: arxiv.org/abs/2606.28692 Code: github.com/mims-harvard/ATHE… Project: athena.openscientist.ai Every treatment decision means weighing disease context, comorbidities, drug interactions, contraindications, and evidence that keeps changing. That is an iterative reasoning problem. You have to figure out what evidence to go find before you can even start forming a conclusion. Most LLMs answer from what is stored in their weights. Give agents tools and they often still do not use them well. Having access to a biomedical database does not automatically mean a model knows when to query it, what to ask, or how to weigh what comes back.