{"id":8448,"date":"2026-05-25T09:09:00","date_gmt":"2026-05-25T01:09:00","guid":{"rendered":"https:\/\/meta-quantum.today\/?p=8448"},"modified":"2026-05-24T22:05:11","modified_gmt":"2026-05-24T14:05:11","slug":"the-most-advanced-ai-scientist-autoresearchclaw","status":"publish","type":"post","link":"https:\/\/meta-quantum.today\/?p=8448","title":{"rendered":"The Most Advanced AI Scientist: AutoResearchClaw"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">Introduction<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A wry, skeptical field report from a theoretical physicist examining &#8220;AutoResearchClaw&#8221; (also styled AutoResearch Claw), an autonomous AI-scientist system whose paper landed on <strong>May 19, 2026<\/strong>. The framing is deliberately deflationary: the creator opens with Sam Altman&#8217;s 2024 promise that AI would help cure cancer and even replace the human principal investigator, then cuts to the 2026 reality \u2014 a single prompt where you &#8220;drop a research idea&#8221; and the system returns a full academic paper. The pitch is seductive: real literature pulled from sources like OpenArchive, Semantic Scholar, and Google Scholar; sandboxed GPU experiments; statistical analysis; multi-agent peer review; and a conference-ready manuscript. The open-source repo (MIT license, ~12,000 stars) makes it all feel tantalizingly real.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The narrator&#8217;s core stance is that the gap between marketing (&#8220;you think of it, AutoResearchClaw writes it&#8221;) and substance is exactly what the paper&#8217;s own authors quietly admit. This tension between the autonomous-researcher dream and what was actually delivered is the throughline of the whole review. It echoes a long-running idea in AI commentary that automated researchers could one day read every ML paper ever written, learn in parallel from copies of themselves, and accumulate the equivalent of millennia of experience \u2014 a vision this video treats as still firmly aspirational.<\/p>\n\n\n\n<figure class=\"wp-block-video\"><video height=\"720\" style=\"aspect-ratio: 1280 \/ 720;\" width=\"1280\" autoplay controls loop muted src=\"https:\/\/meta-quantum.today\/wp-content\/uploads\/2026\/05\/Beyond_Autonomous_Science.mp4\" playsinline><\/video><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">AutoResearchClaw<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A real, actively developed project \u2014 here&#8217;s the full practical picture, grounded in the official <code>aiming-lab\/AutoResearchClaw<\/code> repo (the video&#8217;s &#8220;v5 \/ 12k stars&#8221; framing is a bit ahead of reality; the public repo is currently v0.3.1, MIT-licensed, Python 3.11+ with ~6.4k stars and 1,634 passing tests).<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What it actually is<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">A Python framework that turns a single research idea into a conference-ready paper through a 23-stage pipeline covering literature discovery (OpenAlex, Semantic Scholar, arXiv), hardware-aware sandbox experiments, statistical analysis, multi-agent peer review, and LaTeX output targeting NeurIPS\/ICML\/ICLR. It can run fully autonomously or in co-pilot mode with human gates, and it self-heals failed experiments and prunes hallucinated citations.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Prerequisites<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Python 3.11+<\/strong> and <code>git<\/code><\/li>\n\n\n\n<li><strong>Docker<\/strong> (optional but recommended for the hardened sandbox \/ GPU experiments) and a <strong>LaTeX<\/strong> install (for <code>.tex<\/code> \u2192 PDF compile). The <code>researchclaw setup<\/code> step checks for both.<\/li>\n\n\n\n<li><strong>An LLM backend<\/strong>, via either:\n<ul class=\"wp-block-list\">\n<li>An OpenAI-compatible API key (OpenAI, OpenRouter, DeepSeek, MiniMax, Novita\u2026), <strong>or<\/strong><\/li>\n\n\n\n<li>An <strong>ACP coding agent<\/strong> (Claude Code, Codex CLI, Gemini CLI, Kimi, OpenCode) \u2014 in this mode your coding agent acts as the LLM backend for all stages with no separate API key needed.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Installation<\/h3>\n\n\n\n<pre class=\"wp-block-code\"><code># 1. Clone &amp; install\ngit clone &lt;https:\/\/github.com\/aiming-lab\/AutoResearchClaw.git&gt;\ncd AutoResearchClaw\npython3 -m venv .venv &amp;&amp; source .venv\/bin\/activate\npip install -e .\n\n# 2. Setup \u2014 interactive; installs OpenCode \"beast mode\", checks Docker\/LaTeX\nresearchclaw setup\n\n# 3. Configure \u2014 interactive; pick your LLM provider, writes config.arc.yaml\nresearchclaw init\n# (manual alternative: cp config.researchclaw.example.yaml config.arc.yaml)\n<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">The README also collapses this into a one-liner: <code>pip install -e . &amp;&amp; researchclaw setup &amp;&amp; researchclaw init &amp;&amp; researchclaw run --topic \"...\" --auto-approve<\/code>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Configuration<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><code>researchclaw init<\/code> generates <code>config.arc.yaml<\/code>. The <strong>minimum viable config<\/strong> is small:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>project:\n  name: \"my-research\"\nresearch:\n  topic: \"Your research topic here\"\nllm:\n  base_url: \"&lt;https:\/\/api.openai.com\/v1&gt;\"\n  api_key_env: \"OPENAI_API_KEY\"\n  primary_model: \"gpt-4o\"\n  fallback_models: &#91;\"gpt-4o-mini\"]\nexperiment:\n  mode: \"sandbox\"\n  sandbox:\n    python_path: \".venv\/bin\/python\"\n<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">The knobs worth knowing from the full reference:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Section<\/th><th>Key options<\/th><th>Notes<\/th><\/tr><\/thead><tbody><tr><td><code>project.mode<\/code><\/td><td><code>docs-first<\/code> \/ <code>semi-auto<\/code> \/ <code>full-auto<\/code><\/td><td>overall autonomy level<\/td><\/tr><tr><td><code>experiment.mode<\/code><\/td><td><code>simulated<\/code> \/ <code>sandbox<\/code> \/ <code>docker<\/code> \/ <code>ssh_remote<\/code><\/td><td>how\/where code runs; <code>docker<\/code> enables GPU + network policy, <code>ssh_remote<\/code> targets a GPU box<\/td><\/tr><tr><td><code>experiment.max_iterations<\/code><\/td><td>default <code>10<\/code><\/td><td>self-healing refinement rounds<\/td><\/tr><tr><td><code>export.target_conference<\/code><\/td><td><code>neurips_2025<\/code> \/ <code>iclr_2026<\/code> \/ <code>icml_2026<\/code><\/td><td>LaTeX template<\/td><\/tr><tr><td><code>security.hitl_required_stages<\/code><\/td><td><code>[5, 9, 20]<\/code><\/td><td>the three human-approval gates (skipped by <code>--auto-approve<\/code>)<\/td><\/tr><tr><td><code>llm.s2_api_key<\/code><\/td><td>optional<\/td><td>Semantic Scholar key for higher rate limits<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>ACP mode<\/strong> (no API key \u2014 let Claude Code\/Codex\/Gemini drive):<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>llm:\n  provider: \"acp\"\n  acp:\n    agent: \"claude\"   # or codex \/ gemini \/ kimi \/ opencode\n    cwd: \".\"\n<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">Two optional bridges add capability without code changes: <code>metaclaw_bridge<\/code> (cross-run learning \u2014 converts failures into reusable skills injected into all 23 stages, reporting +18.3% robustness) and <code>openclaw_bridge<\/code> (scheduled runs, Discord\/Slack notifications, parallel sub-sessions, live web fetch).<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Ways to run it<\/h3>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-8f761849 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:100%\">\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Method<\/th><th>How<\/th><\/tr><\/thead><tbody><tr><td><strong>Standalone CLI<\/strong><\/td><td><code>researchclaw run --config config.arc.yaml --topic \"...\" --auto-approve<\/code><\/td><\/tr><tr><td><strong>OpenClaw (easiest)<\/strong><\/td><td>Share the repo URL with OpenClaw \u2192 it reads <code>RESEARCHCLAW_AGENTS.md<\/code>, then say &#8220;Research [topic]&#8221; \u2192 it clones, installs, configures, runs, and returns results<\/td><\/tr><tr><td><strong>Claude Code<\/strong><\/td><td>Reads <code>RESEARCHCLAW_CLAUDE.md<\/code>; just say <em>&#8220;Run research on [topic]&#8221;<\/em><\/td><\/tr><tr><td><strong>Python API<\/strong><\/td><td><code>from researchclaw.pipeline import Runner; Runner(config).run()<\/code><\/td><\/tr><\/tbody><\/table><\/figure>\n<\/div>\n<\/div>\n\n\n\n<h3 class=\"wp-block-heading\">Simple showcase<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">A minimal end-to-end run on a small, well-scoped methodology question:<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>source .venv\/bin\/activate\nexport OPENAI_API_KEY=\"sk-...\"\n\nresearchclaw run \\\\\n  --config config.arc.yaml \\\\\n  --topic \"Do different cross-validation strategies meaningfully change model selection on small tabular datasets?\" \\\\\n  --auto-approve\n<\/code><\/pre>\n\n\n\n<p class=\"wp-block-paragraph\">What happens under the hood is the 8-phase, 23-stage flow \u2014 scoping \u2192 literature pull (real APIs) \u2192 hypothesis via multi-agent debate \u2192 hardware-aware code generation \u2192 sandboxed execution with self-healing \u2192 a PROCEED\/REFINE\/PIVOT decision at stage 15 \u2192 drafting \u2192 peer review \u2192 quality gate \u2192 LaTeX export \u2192 citation verification.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">When it finishes, results land in <code>artifacts\/rc-YYYYMMDD-HHMMSS-&lt;hash&gt;\/deliverables\/<\/code>, containing:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><code>paper_draft.md<\/code> and <code>paper.tex<\/code> \u2014 full paper + compile-ready LaTeX<\/li>\n\n\n\n<li><code>references.bib<\/code> \u2014 real BibTeX, auto-pruned to inline citations<\/li>\n\n\n\n<li><code>verification_report.json<\/code> \u2014 a 4-layer citation integrity check (arXiv ID \u2192 CrossRef\/DataCite DOI \u2192 Semantic Scholar title match \u2192 LLM relevance scoring)<\/li>\n\n\n\n<li><code>experiment runs\/<\/code> + <code>charts\/<\/code> \u2014 generated code, JSON metrics, comparison charts with error bars<\/li>\n\n\n\n<li><code>reviews.md<\/code> \u2014 multi-agent peer review with methodology-evidence consistency checks<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">To stay in the loop instead of full-auto, drop <code>--auto-approve<\/code> and it will pause at stages 5, 9, and 20 for your sign-off \u2014 which, per both the paper and the video, is where the quality actually comes from.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Practical caveats<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">It&#8217;s genuinely v0.x: the surrounding ecosystem notes it&#8217;s powerful but painful to set up \u2014 many early issues were about setup failures, config confusion, and a Stage 10 (code generation) crash, which is why a community wrapper skill (<code>OthmanAdi\/researchclaw-skill<\/code>) exists to automate config and error diagnosis. Docker + a real GPU matter a lot for the experiment stages; the <code>simulated<\/code> mode is fine for kicking the tires without burning tokens or compute. And keep the video&#8217;s punchline in mind \u2014 treat outputs as a research <em>amplifier<\/em>, not a finished publication.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">New Features and Concept<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">AutoResearchClaw is positioned as <strong>version 5<\/strong> of an evolving multi-agent autonomous research pipeline, and the upgrade is built around <strong>five mechanisms<\/strong>:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Multi-agent debate<\/strong> for both hypothesis generation and result analysis \u2014 a cast of <em>investigator<\/em>, <em>innovator<\/em>, <em>pragmatist<\/em>, and <em>contrarian<\/em> agents that argue and then reach consensus or a majority vote.<\/li>\n\n\n\n<li><strong>A self-healing executor<\/strong> with a &#8220;pivot\/refine&#8221; decision loop designed to convert execution failures into usable information rather than dead ends.<\/li>\n\n\n\n<li><strong>Verifiable result reporting<\/strong> \u2014 explicit guardrails against fabricated numbers and hallucinated citations.<\/li>\n\n\n\n<li><strong>Human-in-the-loop (HITL) collaboration<\/strong> \u2014 seven intervention modes ranging from full autonomy to step-by-step human oversight.<\/li>\n\n\n\n<li><strong>Converting past mistakes into future safeguards<\/strong> \u2014 institutional memory across research cycles.<\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\">Conceptually, the most important admission is in the paper&#8217;s first sentence: automatic scientific discovery requires <em>more<\/em> than generating papers from ideas. The authors acknowledge that real research is iterative \u2014 hypotheses get challenged, experiments fail and inform the next attempt, and lessons accumulate across cycles. That reframing \u2014 from &#8220;autonomous paper generator&#8221; to &#8220;iterative research partner&#8221; \u2014 is the actual conceptual shift here. This sits squarely in the broader autonomous-agent paradigm, where the dream has long been to give individuals a virtual researcher, assistant, writer, or worker at their disposal, while reality keeps demanding more structure and oversight.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">The Pipeline and How It Works<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The workflow flows through distinct phases: a <strong>discovery phase<\/strong> (problem decomposition and domain detection), <strong>literature discovery<\/strong> (paper collection, screening, knowledge extraction), <strong>knowledge synthesis<\/strong> (hypothesis formation via the multi-agent debate), then the <strong>experimentation phase<\/strong> \u2014 code generation, resource planning, and sandboxed Docker execution. When code fails, a failure-diagnosis loop iteratively repairs it; when it succeeds, the system analyzes results and decides whether to <em>proceed, refine, or pivot<\/em> to a different topic. After looping (the narrator notes up to ~10 refinement cycles), the system writes the paper.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">YouTube about AutoResearchClaw<\/h2>\n\n\n\n<figure class=\"wp-block-embed is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio\"><div class=\"wp-block-embed__wrapper\">\n<iframe loading=\"lazy\" title=\"Stanford, Berkeley, MIT, UNC: AI Scientist AutoResearchClaw\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/MRcGeQcMP2s?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe>\n<\/div><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Benchmarks and the Comparison Game<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The video is sharpest in critiquing the evaluation. The authors define a feature-comparison table where AutoResearchClaw conveniently earns all green checkmarks (self-healing, citation verification, human-in-the-loop gating) \u2014 features the <em>competing<\/em> systems were never built around. They also introduce their own <strong>ARC benchmark<\/strong>: a 25-topic machine-learning suite (tabular ML, dimensionality reduction, NLP, AutoML, GP kernels, topic modeling, feature selection, causal discovery) plus a 20-topic scientific domain split (10 high-energy physics, 7 systems biology, 3 statistics).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Comparisons run against two systems that, in AI time, are ancient: an <strong>AI-driven code-exploration system (AIDE)<\/strong> from February 2025 and <strong>Sakana AI&#8217;s &#8220;The AI Scientist v2&#8221;<\/strong> from April 2025 \u2014 both over a year old. All systems use the same <strong>GPT-5.3 Codex<\/strong> backbone. Two terms matter:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Co-pilot<\/strong> = a human intervenes at critical or high-risk decision points.<\/li>\n\n\n\n<li><strong>Full auto<\/strong> = zero human interaction.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">The results are tellingly modest. Code <em>development<\/em> scores are nearly identical across systems (~0.93\u20130.96). Code <em>execution<\/em> improves with multi-agents. The interesting metric is <strong>result analysis<\/strong> \u2014 whether the AI can judge if its own output makes sense. Full-auto AutoResearchClaw reaches only 0.44 (vs. the older AIDE&#8217;s 0.33), and only the <strong>team + human co-pilot<\/strong> configuration breaks above 0.5, at 0.52. For the domain-specialized tasks, AutoResearchClaw&#8217;s agents are equipped with field-specific skills (Feynman rules, MadGraph, and a collider-agent architecture for high-energy physics; flux-balance analysis for biology; Monte Carlo and semi-parametric inference for statistics) and run inside <strong>Claude Code<\/strong> rather than Codex. Unsurprisingly, the only system with these skills wins (e.g., 48% in high-energy physics while others fail) \u2014 which the narrator flags as a stacked comparison.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">The Real Finding: Where Humans Matter<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The case study (topic D10 \u2014 how cross-validation strategies differ for model selection on small datasets) crystallizes the lesson: <strong>full auto completely fails, but co-pilot succeeds.<\/strong> The reason isn&#8217;t that humans solve it directly \u2014 it&#8217;s that human guidance targets the experimental bottleneck and shows a way out, whereas full auto has none.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The authors&#8217; insights are genuinely useful:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Verification is necessary but not sufficient.<\/strong> A full-auto run can pass a numeric gate while the &#8220;verified&#8221; value is zero \u2014 and zero might be a legitimate measurement <em>or<\/em> a silent failure of the whole system. The gate can&#8217;t tell which.<\/li>\n\n\n\n<li><strong>Co-pilot improves quality not through <em>more<\/em> intervention but through <em>correctly placed<\/em> intervention<\/strong> \u2014 at the moments where the AI cracks. Micromanaging every step both bores the human and adds little value.<\/li>\n\n\n\n<li>The proposed division of labor: humans make the high-stakes judgment calls (hypothesis co-creation, experiment-design review, shaping conclusions, scientific-faithfulness checks), while the AI handles low-risk execution \u2014 setup, Python, coding.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">This human-at-the-gates pattern mirrors a broader trend in research-automation work, where decision gates are deliberately embedded at key stages so researchers can steer rather than rubber-stamp the pipeline.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Conclusion and Key Takeaways<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The video&#8217;s punchline is the authors&#8217; own closing statement: AutoResearchClaw is positioned as a <strong>research amplifier<\/strong> that accelerates exploration while keeping verifiability central \u2014 explicitly <em>not<\/em> a replacement for human scientific judgment. The 2024 prophecy of AI ousting the principal investigator has, by mid-2026, quietly collapsed into &#8220;AI helps you code and visualize faster.&#8221;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Key points:<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>AutoResearchClaw v5 = a five-mechanism multi-agent pipeline (debate, self-healing execution, verifiable reporting, human-in-the-loop, mistake-to-safeguard memory).<\/li>\n\n\n\n<li>Headline gains are real but incremental; the standout improvement is <strong>result interpretation<\/strong>, and even that only clears 0.5 <em>with<\/em> a human co-pilot.<\/li>\n\n\n\n<li><strong>Verification gates can be fooled by valid-looking-but-meaningless outputs<\/strong> (e.g., zeros) \u2014 automation alone can&#8217;t catch this.<\/li>\n\n\n\n<li>The winning recipe is <strong>strategic human input at the right decision points<\/strong>, not full autonomy and not micromanagement.<\/li>\n\n\n\n<li><strong>Bonus paper (Cornell, May 18, 2026):<\/strong> &#8220;How far are we from true auto research with AI?&#8221; reached a parallel conclusion \u2014 <strong>none of the 117 AI-generated papers<\/strong> examined cleared the acceptance bar of a top-tier venue, suggesting we remain meaningfully short of true autonomous research.<\/li>\n\n\n\n<li>Net message: treat &#8220;AI will cure cancer&#8221; marketing with healthy skepticism; the current frontier is augmentation, not replacement.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Related References<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Official repo \u2014 <a href=\"https:\/\/github.com\/aiming-lab\/AutoResearchClaw\" target=\"_blank\" rel=\"noopener\" title=\"\">aiming-lab\/AutoResearchClaw<\/a> (README, <code>config.researchclaw.example.yaml<\/code>, <code>docs\/integration-guide.md<\/code>, Tester Guide)<\/li>\n\n\n\n<li>Paper \u2014 <em>AutoResearchClaw: Self-Reinforcing Autonomous Research with Human-AI Collaboration<\/em> (<a href=\"https:\/\/papers.cool\/arxiv\/2605.20025\" target=\"_blank\" rel=\"noopener\" title=\"\">papers.cool\/arxiv\/2605.20025<\/a>)<\/li>\n\n\n\n<li><a href=\"https:\/\/github.com\/aiming-lab\/MetaClaw\" target=\"_blank\" rel=\"noopener\" title=\"\">MetaClaw<\/a> (cross-run learning) \u00b7 <a href=\"https:\/\/github.com\/openclaw\/openclaw\" target=\"_blank\" rel=\"noopener\" title=\"\">OpenClaw<\/a> (chat-driven runner) \u00b7 <a href=\"https:\/\/github.com\/OthmanAdi\/researchclaw-skill\" target=\"_blank\" rel=\"noopener\" title=\"\">researchclaw-skill<\/a> (setup wrapper)<\/li>\n\n\n\n<li>Lineage it credits: Sakana AI&#8217;s <a href=\"https:\/\/github.com\/SakanaAI\/AI-Scientist\" target=\"_blank\" rel=\"noopener\" title=\"\">AI-Scientist<\/a>, Karpathy&#8217;s AutoResearch, Analemma&#8217;s FARS<\/li>\n\n\n\n<li><strong>AutoResearch Claw v5 paper<\/strong> \u2014 &#8220;<a href=\"https:\/\/huggingface.co\/papers\/2605.20025\" target=\"_blank\" rel=\"noopener\" title=\"Self-Reinforced Autonomous Research with Human\u2013AI Collaboration\">Self-Reinforced Autonomous Research with Human\u2013AI Collaboration<\/a>&#8221; (published May 19, 2026; multi-institution: UNC Chapel Hill, UC Santa Cruz, CMU, NUS, UC Berkeley, Rutgers, NEC Labs America, Meta, Stanford, Google, University of Washington) + the open-source GitHub repository (MIT license).<\/li>\n\n\n\n<li><strong>Cornell bonus paper<\/strong> \u2014 &#8220;<a href=\"https:\/\/arxiv.org\/html\/2605.19156v1\" target=\"_blank\" rel=\"noopener\" title=\"\">How far are we from true auto research with AI?<\/a>&#8221; (May 18, 2026), introducing SAR (agentic reviewer with human inspection) and PR (artifacts-aware peer review).<\/li>\n\n\n\n<li><strong>Sakana AI \u2014 &#8220;The AI Scientist v2&#8221;<\/strong> (April 2025), one of the baseline comparison systems. (<a href=\"https:\/\/arxiv.org\/pdf\/2408.06292\">Sakana AI Scientist on arXiv\/GitHub<\/a>)<\/li>\n\n\n\n<li><a href=\"https:\/\/huggingface.co\/papers\/2502.13138\" target=\"_blank\" rel=\"noopener\" title=\"\"><strong>AIDE \u2014 AI-Driven Exploration in the Space of Code<\/strong> (February 2025), the second baseline system.<\/a><\/li>\n\n\n\n<li>Related Glasp reading: <a href=\"https:\/\/glasp.co\/hatch\/glasp\/p\/VYVvssTBth8pPp6PYTAb\">The Complete Beginners Guide to Autonomous Agents<\/a> and <a href=\"https:\/\/glasp.co\/5e7qwbnfs1jxqwcz\/p\/f970b3206841b627cb8b\">From AGI to Superintelligence: The Intelligence Explosion (highlights)<\/a>.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>AutoResearchClaw turns a single research idea into a conference-ready paper through a 23-stage autonomous pipeline\u2014pulling real literature, running self-healing sandbox experiments, and verifying citations. This ready-to-use config runs Claude Code via ACP (no API key needed), with DeepSeek and OpenRouter as drop-in alternatives. Human gates stay active where quality matters most: literature screening, experiment design, and the final quality check.<\/p>\n","protected":false},"author":1,"featured_media":8451,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[15,18,13,7],"tags":[],"class_list":["post-8448","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai","category-education","category-quantum-and-u","category-quantum-mindset-programme"],"aioseo_notices":[],"featured_image_src":"https:\/\/meta-quantum.today\/wp-content\/uploads\/2026\/05\/Beyond_Autonomous_Science-00-scaled.jpg","featured_image_src_square":"https:\/\/meta-quantum.today\/wp-content\/uploads\/2026\/05\/Beyond_Autonomous_Science-00-scaled.jpg","author_info":{"display_name":"coffee","author_link":"https:\/\/meta-quantum.today\/?author=1"},"rbea_author_info":{"display_name":"coffee","author_link":"https:\/\/meta-quantum.today\/?author=1"},"rbea_excerpt_info":"AutoResearchClaw turns a single research idea into a conference-ready paper through a 23-stage autonomous pipeline\u2014pulling real literature, running self-healing sandbox experiments, and verifying citations. This ready-to-use config runs Claude Code via ACP (no API key needed), with DeepSeek and OpenRouter as drop-in alternatives. Human gates stay active where quality matters most: literature screening, experiment design, and the final quality check.","category_list":"<a href=\"https:\/\/meta-quantum.today\/?cat=15\" rel=\"category\">AI<\/a>, <a href=\"https:\/\/meta-quantum.today\/?cat=18\" rel=\"category\">Education<\/a>, <a href=\"https:\/\/meta-quantum.today\/?cat=13\" rel=\"category\">Quantum and U<\/a>, <a href=\"https:\/\/meta-quantum.today\/?cat=7\" rel=\"category\">Quantum Mindset Programme<\/a>","comments_num":"0 comments","_links":{"self":[{"href":"https:\/\/meta-quantum.today\/index.php?rest_route=\/wp\/v2\/posts\/8448","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/meta-quantum.today\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/meta-quantum.today\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/meta-quantum.today\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/meta-quantum.today\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=8448"}],"version-history":[{"count":4,"href":"https:\/\/meta-quantum.today\/index.php?rest_route=\/wp\/v2\/posts\/8448\/revisions"}],"predecessor-version":[{"id":8454,"href":"https:\/\/meta-quantum.today\/index.php?rest_route=\/wp\/v2\/posts\/8448\/revisions\/8454"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/meta-quantum.today\/index.php?rest_route=\/wp\/v2\/media\/8451"}],"wp:attachment":[{"href":"https:\/\/meta-quantum.today\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8448"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/meta-quantum.today\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=8448"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/meta-quantum.today\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=8448"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}