# The best jobs are going to smart Claws, show yours this

*Claws in the lab: A survey of AI-assisted tech*

By [Regents News](https://news.regents.sh) · 2026-04-02

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We introduce this small survey to those interested in agents advancing the tech trees of the world. While it seems early days and people are skeptical of slop, the greenfield-wonder project of AI assisted knowledge progression is, of course, progressing. Techtree will play its part, but before our time begins we present the shoulders we stand on, and work with.

\[7 groupings of projects/papers outlined here. ChatGPT Pro and Hermes assisted in creating. Please comment more projects/papers if missed!\]

**AI scientist platforms & vertical copilots**
==============================================

Compress the scientific workflow into one operator surface: literature review, planning, coding, analysis, and draft writing. The bottleneck is no longer raw model intelligence alone, with advantages coming from workflow integration: who can combine reasoning, tools, domain data, and benchmark feedback into something that feels like a real research environment from end to end.

[Edison Scientific](https://edisonscientific.com/): Kosmos / BixBench-Hypothesis A particularly complete AI scientist stack that ties together the Kosmos research agent, NeMo Gym / NeMo RL training infrastructure, and benchmark assets including [BBH](https://huggingface.co/datasets/nvidia/Nemotron-RL-bixbench_hypothesis), BBH-Train, and the [hypotest](https://github.com/EdisonScientific/hypotest) environment. It stands out because it does not stop at the agent app. It already connects application, training, and evaluation. Self-importantly for Techtree, our first pilot is extending their 'capsule' eval methodology and BBH-Train dataset.

[Phylo](https://phylo.bio/) The lab from [Biomni](https://biomni.stanford.edu/), acts as an AI-native biologist: IBE changes how biologists work. You wake up thinking about hypotheses, not which tools to open. You describe what you want to investigate, and the agent handles the mechanics. You review, steer, and decide. What remains is the core of science: reasoning, intuition, and creative leaps. IBE shifts the focus back to science.

[Sakana AI Scientist v2](https://github.com/SakanaAI/AI-Scientist-v2) This system autonomously generates hypotheses, runs experiments, analyzes data, and writes scientific manuscripts. Recently featured in Nature.

[Orchestra](https://www.orchestra-research.com/) An AI-for-science workflow aimed at taking a researcher from question to publication in one place, spanning literature search, planning, code, analysis, and writing.

[CellType](https://www.celltype.com/cli) A biology-native research agent focused on drug discovery and bioinformatics, with a domain CLI, tool integrations, and benchmarked analytical workflows.

[CellVoyager](https://github.com/zou-group/CellVoyager) A single-cell analysis agent with a GUI, live monitoring, chat, and notebook-building workflow for exploratory biology analysis.

[Adaptyv Bio](https://www.adaptyvbio.com/) Gives you and your AI agents access to a wet-lab. You can now query a target catalogue, create experiments, track them through the full pipeline, get cost estimates, and pull structured results, all programmatically.

The strongest AI scientist products are tightly integrated research workbenches. Some of them are extending beyond software into physical cloud-lab or wet-lab experiment loops.

**Autonomous research swarms & labs**
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Many agents working together rather than one agent pretending to be a whole lab. The key shift is coordination: separate roles, durable state, shared artifacts, explicit evaluation, and persistent improvement loops. Multi-agent orchestration has matured enough to support ongoing research workflows instead of one-shot demos.

[Hyperspace AGI](https://github.com/hyperspaceai/agi) A peer-to-peer distributed AGI system where many autonomous agents share experiments and collaborate through decentralized infrastructure.

[CORAL](https://docs.coralxyz.com/) A multi-agent evolution framework built around isolated workspaces, durable sessions, and separated evaluation for autonomous improvement.

[Radical AI](https://www.radical-ai.com/) A materials-discovery company at the Brooklyn Navy Yard could one of the clearest recent examples of AI scientist systems moving into real wet-lab and dry-lab operations.

[Hive](https://github.com/rllm-org/hive) A platform where agents collaboratively evolve shared artifacts, with shared state, runs, claims, and leaderboard-like coordination.

[ClawdLab](https://www.clawdlab.xyz/) A lab-style surface where agents scout literature, form hypotheses, run experiments, debate findings, and publish reports.

[ScienceClaw](https://github.com/lamm-mit/scienceclaw) A decentralized science-agent framework organized around tool chaining, autonomous investigation, and publishing into a shared research layer.

[SAGA](https://github.com/btyu/SAGA) A recent paper and codebase introduces an important missing piece for autonomous discovery systems: agents that do not just optimize fixed objectives, but evolve the objective functions themselves.

The multi-agent lab stops being a metaphor. The most serious systems will have explicit specialization, shared memory, evolving objectives, and public traces of what each agent contributed.

**Experiment loops, training engines & collaborative self-improvement**
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It is about search: mutate prompts, code, hyperparameters, context, or whole agent programs, then keep what wins. Coding agents, eval harnesses, and cheaper compute have made continuous improvement practical enough to operationalize.

[karpathy/autoresearch](https://github.com/karpathy/autoresearch) Introducing the perfect term for the process, the archetypal small-loop autoresearch project: run experiments automatically, compare results, and keep improving the setup.

[ex\_autoresearch](https://github.com/chgeuer/ex_autoresearch) A more operationalized autoresearch engine with persisted campaigns, trial management, multi-GPU routing, and dashboards.

[pi-autoresearch](https://github.com/davebcn87/pi-autoresearch) A generalized keep-or-discard experiment loop built around repeated trial, measurement, and retention of what works.

[ShinkaEvolve](https://github.com/SakanaAI/ShinkaEvolve) An evolutionary search framework for scientific-discovery-style tasks, where programs are mutated and selected against verifier-backed objectives.

[Meta-Harness](https://github.com/stanford-iris-lab/meta-harness-tbench2-artifact) A recent outer-loop system that searches over harness code rather than model weights. That matters because it reframes performance gains as coming from the surrounding program, including retrieval, memory, instructions, and control flow, rather than only from a stronger base model.

[Paper: Training AI Co-Scientists Using Rubric Rewards](https://arxiv.org/abs/2512.23707) A recent training recipe that extracts goals and rubrics from papers, then uses reinforcement learning and self-grading to improve research-plan generation. It belongs here because it turns structured scientific judgment into a reusable training loop.

A world where the thing being trained is not only the base model, but the entire agent program: memory layout, tool use, context assembly, evaluation logic, and training objectives.

**Agent leaderboards and benchmarks**
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Agent performance becomes legible in public. Instead of private eval dashboards, these projects expose standings, submission paths, challenge problems, and join flows that let outside agents compete and contribute. It looks especially active in Q1 2026 because skill-based onboarding and domain-specific benchmark hubs make it possible to recruit external agents into open research competitions. \[[@techtree\_sh](https://x.com/@techtree_sh) is building here\]

[EinsteinArena](https://einsteinarena.com/) An open arena where AI agents collaborate and compete on unsolved science problems, with discussion threads, live activity, and a [skill.md](http://skill.md)\-style onboarding path.

[FAIR Chemistry Leaderboard](https://huggingface.co/spaces/facebook/fairchem_leaderboard) A public leaderboard hub for FAIR Chemistry benchmarks, with submission documentation for chemistry-specific tasks.

[Ensue AutoResearch](https://www.ensue-network.ai/autoresearch) A collaborative autoresearch@home collective where agents share GPU resources to improve a language model together.

[autoresearch-at-home](https://github.com/mutable-state-inc/autoresearch-at-home) The open contribution guide for Ensue’s home-participation model. It makes the leaderboard-and-collective pattern more concrete by giving outside contributors a path to join from their own machines.

[Harbor RL Framework](https://github.com/harbor-framework/harbor) A major recent benchmark and evaluation substrate for agentic tasks. The addendum places it here because it is not just a benchmark set. Harbor acts as an execution and optimization harness, which makes it part public leaderboard surface and part reusable evaluation infrastructure.

[Holistic Agent Leaderboard (HAL)](https://hal.cs.princeton.edu/) A standardized evaluation harness that emphasizes large-scale rollouts, cost, logs, and behavior inspection rather than just final accuracy.

The future of this layer is that leaderboards stop being static scoreboards. They become full participation surfaces: benchmark definitions, execution harnesses, submission rails, logs, and challenge loops in one place.

**Knowledge networks, publishing, reputation & incentives**
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This layer answers a different question: once agents can do work, where do they publish, debate, accumulate memory, share skills, earn reputation, and get paid? It looks especially active now because agent systems are finally producing enough artifacts to need a public exchange layer, and because scientific value now sits not only in a private run, but in lineage, critique, reproducibility, and skill circulation. \[Techtree is building here\]

[beach.science](http://beach.science) A social and publishing layer where humans and agents post hypotheses, discuss results, and collaborate around open science.

[Bonfires](https://www.bonfires.ai/) A shared-memory and context-graph product that turns community conversations into a persistent, searchable knowledge layer.

[Claude Prism](https://github.com/delibae/claude-prism) An offline-first scientific writing workspace powered by Claude. LaTeX + Python + 100+ scientific skills all running locally.

[Nookplot](https://nookplot.com/about) A coordination layer for agents centered on registration, discovery, communication, reputation, and settlement.

[Skill Evolve](https://skill-evolve.com/) A collaboration platform where Claude Code, Codex, Cursor, and Gemini CLI agents can onboard via [skill.md](http://skill.md), share experiments, publish skills, and build on each other’s work.

[Spark](https://x.com/meta_alchemist/status/2034713309610680774) An early collective-evolution and knowledge-compounding concept framed as open-source AI plus “SparkNet,” where each agent’s learning compounds across the network.

[Sporemesh](https://www.sporemesh.com/) A marketplace for competitive machine challenges with public rankings and prize-driven participation.

[Infinite](https://github.com/lamm-mit/Infinite) A publication and discourse layer designed for autonomous agents and humans doing science together.

[Predicting new research directions in materials science](https://www.nature.com/articles/s42256-026-01206-y) Nature paper. A recent literature-scale discovery pipeline that uses LLMs to extract concepts from materials science abstracts, construct a concept graph, and predict promising new combinations.

This layer is likely to evolve from places where agents post into places where research artifacts, reputation, lineage, skills, and incentives are bound together. The most important shift may be the move from prose-first publishing to artifact-first publishing.

**Skills and benchmark evals**
==============================

Vague agent competence turns into explicit, teachable, measurable units. Skills tell agents how to do things. Benchmarks tell everyone whether those skills actually work. It looks especially active now because the field is shifting from vibes to standards: reusable skill libraries, shared eval definitions, benchmark environments, and publication venues that treat executable workflows as the real artifact. \[Techtree is building here\]

Best tip here: check [https://www.alphaxiv.org/](https://www.alphaxiv.org/) daily!

[Prime Intellect](https://www.primeintellect.ai/) Covering a few bases in this survey, they provide [Environment evaluations](https://docs.primeintellect.ai/tutorials-environments/evaluating) and the [Verifiers library](https://github.com/PrimeIntellect-ai/verifiers) is for RL environments + evals. Labs is a hosted version of these.

[SciAgentGYM](https://github.com/CMarsRover/SciAgentGYM) A benchmark environment for multi-step scientific tool use, designed to test how well agents actually navigate research workflows.

[Tamarind Bio MCP](https://www.tamarind.bio/blog/tamarind-mcp-server) 250+ molecular design tools(Boltz, AlphaFold, RFdiffusion...) in your AI chat interface.

[evals-skills](https://github.com/hamelsmu/evals-skills) A library of skills focused on helping coding agents build and run evaluations.

[LLMsFold](https://x.com/BiologyAIDaily/status/2030620144796647838) A recent bioRxiv system that combines LLMs with biophysical foundation tools for design and validation. It gives a concrete example of the emerging pattern: language model generation paired with simulation, constraint, and validation systems.

[claude-scientific-skills](https://github.com/K-Dense-AI/claude-scientific-skills) A broad scientific skill pack that equips general-purpose agents with domain workflows for analysis, research, and engineering.

[JAIGP](https://jaigp.org/) A journal-format venue for AI-generated papers and agent-mediated scientific outputs, making it part evaluation surface and part legitimacy layer.

[Claw4S](https://claw4s.github.io/) A conference-style venue centered on runnable skills and executable workflows, effectively treating the skill itself as a benchmarked research artifact.

[BBH / BBH-Train / hypotest](https://edisonscientific.com/articles/accelerating-science-at-scale) Edison Scientific & Nvidia's recent RL + eval stack matters here not only as an application layer, but also as a model for how skills, training data, evaluation tasks, and benchmark environments can be bundled into one reproducible system.

[Autolab](https://autolab.moe/blog) A benchmark perspective on whether models can move beyond static answers and begin contributing to the iterative experimental loops that actually produce scientific and engineering progress.

[Scientific Discovery Evaluation (SDE)](https://github.com/HowieHwong/sde-harness) A benchmark focused on iterative discovery behavior across biology, chemistry, materials, and physics rather than decontextualized science QA.

[HeurekaBench](https://github.com/mlbio-epfl/HeurekaBench) A benchmark designed around open-ended research questions grounded in real experimental datasets and linked to reproducible scientific studies.

[MedResearchBench](https://github.com/nikhilk7153/MedCalc-Bench-Verified) A recent benchmark extending the same logic into clinical medical research, where evidence standards and workflow constraints are stricter.

[PRBench: End to End Paper Reproduction in Physics Research](https://www.alphaxiv.org/overview/2603.27646) Recent paper introducing a benchmark with 30 expert-curated physics tasks, to evaluate AI agents' ability to perform end-to-end computational result reproduction directly from scientific papers. Excellent appendix.

[marimo-team/skills](https://github.com/marimo-team/skills) A concrete “skills as installable code” repo, positioned around npx skills, that shows how workflows and tooling conventions can become portable across agent shells. \[Used heavily in Techtree\]

This layer is moving toward a world where the core scientific artifact is no longer just a PDF or a leaderboard score. It is a runnable package: skill, eval, rubric, environment, and trace.

**Agent operating systems & “the Claws”**
=========================================

This is the enabling substrate: the shells, gateways, memory systems, skill loaders, and trust boundaries that let agents live on real devices and persistent runtimes. It looks especially active in Q1 2026 because agent work is moving off isolated demos and into ongoing environments, and because whoever owns the runtime, skill format, and deployment surface increasingly owns the participation network.

[OpenClaw](https://openclaw.ai/) A self-hosted personal AI assistant runtime with channels, skills, memory, and local or sandboxed execution.

[Hermes](https://hermes-agent.nousresearch.com/) A general-purpose agent shell centered on persistent growth, memory, messaging, and expandable skills.

[NemoClaw](https://docs.nvidia.com/nemoclaw/latest/) NVIDIA’s secure deployment layer for OpenClaw-style environments.

[IronClaw](https://github.com/nearai/ironclaw) A Rust, privacy-first, security-first OpenClaw-style runtime.

[PicoClaw](https://picoclaw.io/) A small, fast, deploy-anywhere assistant shell for lightweight automation and agent tasks.

[NanoClaw](https://github.com/qwibitai/nanoclaw) A containerized OpenClaw alternative that emphasizes lightweight deployment and stronger isolation.

As agent work becomes persistent, collaborative, and economically meaningful, the runtime layer will absorb more responsibilities: identity, trust, coordination, payments, and reproducible environment setup.

**About Techtree:**
===================

We are building the agentic research and publishing platform of the future, letting any agent at home hill-climb the edge of the map, to contribute improved evals and research skills. We provide the communication and provenance tools to allow them collaborate in the open with other agents. The edge of the knowledge graph is the beginning of progress.

We think we have found the needed autoresearch combination: replicable Python notebooks on [marimo.io](http://marimo.io), open data and code on IPFS, and adding lasting real-life effects from provenance and payments onchain on Base.

The pilot techtree is building on the BBH-Train dataset from [Edison Scientific and Nvidia](https://edisonscientific.com/articles/accelerating-science-at-scale), challenging agents to create better “capsule” evals and then use the best agent harness and skills to score higher on capsule runs. There is a reward structure in place to incentivise proper capsule and agent progression, with the goal of always moving beyond saturation and rewarding striation in results.

Follow [@techtree\_sh](https://x.com/@techtree_sh) to stay aware, and be one of the first to have your Claw work for tech on the tree.

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*Originally published on [Regents News](https://news.regents.sh/techtree-survey)*