The ToolUniverse plugin for Claude Code is here: 2,000 life-science tools and 120 research skills, with a real source on every answer, installed with one prompt. ToolUniverse already powers a range of AI agents. This plugin brings its full toolset and skill library into Claude Code. What it does: → Claude Code automatically reaches for the right tools and skills to run a full life-science analysis end to end: variant interpretation, RNA-seq differential expression, drug and target lookups, rare-disease workups. Every claim comes back with a real source, instead of from memory. → When accuracy is critical, dedicated commands add discipline: • cross-validate a claim across 3 independent sources • literature-sweep, a graded review across 15 indexes (PubMed, EuropePMC, OpenAlex, and more) • compare, side-by-side tables for drugs, targets, diseases, or variants • research, a step-by-step multi-source investigation • translate-id, resolve an ID across every namespace Install in one step. Paste this into Claude Code and it sets itself up: Read raw.githubusercontent.com/mi… and install the ToolUniverse Claude Code plugin for me. Blog post 👇 aiscientist.tools/posts/tool… @marinkazitnik @ScientistTools @KempnerInst @HarvardDBMI

The ToolUniverse plugin for Claude Code is here: 2,000 life-science tools and 120 research skills, with a real source on every answer, installed with one prompt. ToolUniverse already powers a range of AI agents. This plugin brings its full toolset and skill library into Claude Code. What it does: → Claude Code automatically reaches for the right tools and skills to run a full life-science analysis end to end: variant interpretation, RNA-seq differential expression, drug and target lookups, rare-disease workups. Every claim comes back with a real source, instead of from memory. → When accuracy is critical, dedicated commands add discipline: • cross-validate a claim across 3 independent sources • literature-sweep, a graded review across 15 indexes (PubMed, EuropePMC, OpenAlex, and more) • compare, side-by-side tables for drugs, targets, diseases, or variants • research, a step-by-step multi-source investigation • translate-id, resolve an ID across every namespace Install in one step. Paste this into Claude Code and it sets itself up: Read raw.githubusercontent.com/mi… and install the ToolUniverse Claude Code plugin for me. Blog post 👇 aiscientist.tools/posts/tool… @marinkazitnik @ScientistTools @KempnerInst @HarvardDBMI

AI Scientists are starting to actually do science. Not just answer questions. Not just run workflows. Introducing AutoScientists: a decentralized team of AI agents that can generate hypotheses, design experiments, write code, test ideas, analyze failures, and revise strategy as evidence accumulates. Because real research is not a to do list of tasks. It is a living search process. Leads emerge, failures matter, teams form around what works, and priorities shift when evidence changes. Much like how a lab of scientists would work on cutting edge research together. Across GPT training optimization, biomedical ML, and protein fitness prediction, this decentralized structure consistently does better research. Learn more 👇 @GaoShanghua @marinkazitnik @KempnerInst @HarvardDBMI @Harvard

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…

Congratulations @biohub on the release of ESMFold2, ESMC, and ESM Atlas! Excited to share that these models are available on day one to AI agents powered by ToolUniverse @ScientistTools Stay tuned for agentic skills that let AI agents use SAE representations for protein variant interpretation, loss-of-function mechanism analysis, structural annotation, and mechanistic protein reasoning @GaoShanghua @AdaFang_ @_yepeng aiscientist.tools

At @Biohub, our goal is to build models that accelerate scientific discovery and progress toward the cure to disease. We’re releasing all of this under MIT license allowing commercial and non-commercial use. Read more here: biohub.ai/esm/protein/

At @Biohub, our goal is to build models that accelerate scientific discovery and progress toward the cure to disease. We’re releasing all of this under MIT license allowing commercial and non-commercial use. Read more here: biohub.ai/esm/protein/

Join us this Thursday to discuss how AI Scientists can empower scientific discovery with @scale_AI! Together with @GaoShanghua and @marinkazitnik, we will share our recent work on ClawInstitute, how AI Scientists can be built with ToolUniverse, and a sneaky preview of some new work AutoScientists. Much more to come! Link to register 👇

ToolUniverse is going global 🌍 More than 500,000 AI agent analyses powered across 113 countries, including 236K in the last month alone What began as an open platform connecting AI agents to scientific tools, databases, and workflows is becoming an open, global AI foundation science Excited to see amazing researchers across the world using ToolUniverse to build AI scientists, speed up analyses with agents, and explore new forms of scientific reasoning The future of science is bright 🚀 aiscientist.tools/ @ScientistTools

Are we even measuring the right things when we evaluate LLMs? We introduce QWorld, a framework where every question generates its own evaluation world through recursive expansion tree. One question becomes 45 fine-grained criteria. On HealthBench alone: 200k criteria across 530 dimensions. 79% of QWorld's criteria are entirely novel. No expert had ever written them down, yet human judges validate they matter. It surfaces blind spots in every frontier model: sustainability, equity, emergency recognition. Dimensions standard benchmarks don't even have. Built with @YuchangSu456733, @sui67713, @CurtGinder, and @marinkazitnik Paper: arxiv.org/abs/2603.23522 Code: github.com/mims-harvard/qwor… Demo: qworld.openscientist.ai @Harvard @HarvardDBMI @KempnerInst @harvardmed