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AutoResearchClaw

把研究问题、实验迭代和结果演化组织成自动化科研闭环。

14.3K GitHub stars 2026-09-05 星标同步 2026-07-16 收录时间

#research#experiment#automation

快速判断

01这个 Skill 是干嘛的: 把研究问题、实验设计、自动迭代和结果演化组织成科研闭环。
02它能解决什么问题: 解决研究实验需要反复人工调参、结果难比较和有效发现不能持续积累的问题。
03它适合谁来用: 适合可通过代码和指标自动验证的研究项目与实验型团队。

AutoResearchClaw

把研究问题、实验迭代和结果演化组织成自动化科研闭环。

主要能力

Apply A-Evolve's agentic evolution methodology to improve AI agent performance across runs. Use when the user wants to diagnose agent failures, generate targeted skills from error patterns, evolve system prompts, or accumulate episodic knowledge. Works standalone or inside AutoResearchClaw pipelines. Triggers on: "evolve", "self-improve", "diagnose failures", "generate skills from errors", "what went wrong and how to fix it", or any mention of A-Evolve.

使用方式

仓库级安装会保留完整目录；本次核验的入口是 .claude/skills/a-evolve/SKILL.md。使用前先阅读英文 README 与原始 SKILL.md，并按当前 Agent 的目录规范安装。

适用边界

自动实验不能替代研究设计审查，资源消耗和停止条件要预先设定。

许可与来源

来源：GitHub 公开仓库。核验许可：MIT。本页是贴近原仓库结构的中文导读，具体参数、依赖和更新记录以英文 README 为准。

<h2 align="center"><b>Chat an Idea. Get a Paper. Autonomous, Collaborative & Self-Evolving.</b></h2>

<p align="center"> <b><i><font size="5">Just chat with <a href="#openclaw-integration">OpenClaw</a>: "Research X" → done.</font></i></b> </p>

<p align="center"> 📄 <b>Our paper is on arXiv — come read it!</b> <a href="https://arxiv.org/abs/2605.20025"><i>AutoResearchClaw: Self-Reinforcing Autonomous Research with Human-AI Collaboration</i></a> </p>

<p align="center"> <a href="docs/README_CN.md">🇨🇳 中文</a> · <a href="docs/README_JA.md">🇯🇵 日本語</a> · <a href="docs/README_KO.md">🇰🇷 한국어</a> · <a href="docs/README_FR.md">🇫🇷 Français</a> · <a href="docs/README_DE.md">🇩🇪 Deutsch</a> · <a href="docs/README_ES.md">🇪🇸 Español</a> · <a href="docs/README_PT.md">🇧🇷 Português</a> · <a href="docs/README_RU.md">🇷🇺 Русский</a> · <a href="docs/README_AR.md">🇸🇦 العربية</a> </p>

<p align="center"> <a href="docs/showcase/SHOWCASE.md">🏆 Paper Showcase</a> · <a href="docs/HITL_GUIDE.md">🧑‍✈️ Co-Pilot Guide</a> · <a href="docs/integration-guide.md">📖 Integration Guide</a> · <a href="https://discord.gg/u4ksqW5P">💬 Discord Community</a> </p>

---

<table> <tr> <td width="18%"> <a href="docs/showcase/SHOWCASE.md"><img src="docs/showcase/thumbnails/paper_I_random_matrix-01.png" width="120" alt="Sample Paper"/></a> </td> <td valign="middle"> <b>🏆 Generated Paper Showcase</b><br><br> <b>8 papers across 8 domains</b> — math, statistics, biology, computing, NLP, RL, vision, robustness — generated fully autonomously or with Human-in-the-Loop co-pilot guidance.<br><br> <a href="docs/showcase/SHOWCASE.md"><img src="https://img.shields.io/badge/View_Full_Showcase_→-All_8_Papers-d73a49?style=for-the-badge" alt="View Showcase"></a> </td> </tr> </table>

---

🧪 We're looking for testers! Try the pipeline with your own research idea — from any field — and tell us what you think. Your feedback directly shapes the next version. → Testing Guide | → 中文测试指南 | → 日本語テストガイド

---

🔥 News

[05/19/2026] v0.5.0 — Multi-Domain Experiment Agents + ARC-Bench — Two headline updates. (1) Domain-specialist execution agents: the experiment stage (Stages 10–13) now routes beyond the default ML sandbox to specialist agents per field — high-energy physics (ColliderAgent: Lagrangian → FeynRules → MadGraph5 → Delphes via the Magnus cloud), biology (COBRApy genome-scale metabolic modelling), and statistics (simulation-study agent), with a generic Docker executor covering chemistry/materials. The pipeline auto-selects the right executor from the research domain. (2) ARC-Bench: a 55-topic open-ended autonomous-research benchmark spanning ML (25), HEP (10), quantum (10), biology (7), and statistics (3) — each topic ships a manifest (research question + conditions + metrics + datasets) and a rubric for graded scoring, all under experiments/arc_bench/, and also released on 🤗 Hugging Face. → Domain Integration Guide
[04/01/2026] v0.4.0 — Human-in-the-Loop Co-Pilot System — AutoResearchClaw is no longer purely autonomous. New HITL system adds 6 intervention modes (full-auto, gate-only, checkpoint, step-by-step, co-pilot, custom), per-stage policies, and deep human-AI collaboration. Includes: Idea Workshop for hypothesis co-creation, Baseline Navigator for experiment design review, Paper Co-Writer for collaborative drafting, SmartPause (confidence-driven dynamic intervention), ALHF intervention learning, anti-hallucination claim verification, cost budget guardrails, pipeline branching for parallel hypothesis exploration, and CLI commands (attach/status/approve/reject/guide). → Full HITL Guide
[03/30/2026] Flexible Skill Loading — AutoResearchClaw now supports loading open-source and custom skills from any discipline to further enhance your research experience. 20 pre-loaded skills are included as ready-to-use references, covering scientific writing, experiment design, chemistry, biology, and more — including an A-Evolve agentic evolution skill contributed by the community. Load your own via researchclaw skills install or drop a SKILL.md into .claude/skills/. See Skills Library.
[03/22/2026] v0.3.2 — Cross-Platform Support + Major Stability — AutoResearchClaw now runs on any ACP-compatible agent backend (Claude Code, Codex CLI, Copilot CLI, Gemini CLI, Kimi CLI) and supports messaging platforms (Discord, Telegram, Lark, WeChat) via OpenClaw bridge. New CLI-agent code generation backend delegates Stages 10 & 13 to external CLI agents with budget control and timeout management. Also includes anti-fabrication system (VerifiedRegistry + experiment diagnosis & repair loop), 100+ bug fixes, modular executor refactoring, --resume auto-detection, LLM retry hardening, and community-reported fixes.

<details> <summary>Earlier releases</summary>

[03/18/2026] v0.3.1 — OpenCode Beast Mode + Community Contributions — New "Beast Mode" routes complex code generation to OpenCode with automatic complexity scoring and graceful fallback. Added Novita AI provider support, thread-safety hardening, improved LLM output parsing robustness, and 20+ bug fixes from community PRs and internal audit.
[03/17/2026] v0.3.0 — MetaClaw Integration — AutoResearchClaw now supports MetaClaw cross-run learning: pipeline failures → structured lessons → reusable skills, injected into all 23 stages. +18.3% robustness in controlled experiments. Opt-in (metaclaw_bridge.enabled: true), fully backward-compatible. See Integration Guide.
[03/16/2026] v0.2.0 — Three multi-agent subsystems (CodeAgent, BenchmarkAgent, FigureAgent), hardened Docker sandbox with network-policy-aware execution, 4-round paper quality audit (AI-slop detection, 7-dim review scoring, NeurIPS checklist), and 15+ bug fixes from production runs.
[03/15/2026] v0.1.0 — We release AutoResearchClaw: a fully autonomous 23-stage research pipeline that turns a single research idea into a conference-ready paper. No human intervention required.

</details>

---

⚡ One Command. One Paper.

# Fully autonomous — no human intervention
pip install -e . && researchclaw setup && researchclaw init && researchclaw run --topic "Your research idea here" --auto-approve

# Co-Pilot mode — collaborate with AI at key decision points
researchclaw run --topic "Your research idea here" --mode co-pilot

---

🤔 What Is This?

You think it. AutoResearchClaw writes it. You guide the key decisions.

Drop a research topic — get back a full academic paper with real literature from OpenAlex, Semantic Scholar & arXiv, hardware-aware sandbox experiments (GPU/MPS/CPU auto-detected), statistical analysis, multi-agent peer review, and conference-ready LaTeX targeting NeurIPS/ICML/ICLR. Run it fully autonomous, or use Co-Pilot mode to guide the AI at critical decision points — choose research directions, review experiment designs, and co-write the paper. No hallucinated references.

<table> <tr><td>📄</td><td><code>paper_draft.md</code></td><td>Full academic paper (Introduction, Related Work, Method, Experiments, Results, Conclusion)</td></tr> <tr><td>📐</td><td><code>paper.tex</code></td><td>Conference-ready LaTeX (NeurIPS / ICLR / ICML templates)</td></tr> <tr><td>📚</td><td><code>references.bib</code></td><td>Real BibTeX references from OpenAlex, Semantic Scholar and arXiv — auto-pruned to match inline citations</td></tr> <tr><td>🔍</td><td><code>verification_report.json</code></td><td>4-layer citation integrity + relevance verification (arXiv, CrossRef, DataCite, LLM)</td></tr> <tr><td>🧪</td><td><code>experiment runs/</code></td><td>Generated code + sandbox results + structured JSON metrics</td></tr> <tr><td>📊</td><td><code>charts/</code></td><td>Auto-generated condition comparison charts with error bars and confidence intervals</td></tr> <tr><td>📝</td><td><code>reviews.md</code></td><td>Multi-agent peer review with methodology-evidence consistency checks</td></tr> <tr><td>🧬</td><td><code>evolution/</code></td><td>Self-learning lessons extracted from each run</td></tr> <tr><td>📦</td><td><code>deliverables/</code></td><td>All final outputs in one folder — compile-ready for Overleaf</td></tr> </table>

The pipeline runs end-to-end — fully autonomous or with human-in-the-loop collaboration. When experiments fail, it self-heals. When hypotheses don't hold, it pivots. When citations are fake, it kills them. When you want to steer, it pauses and listens.

🌍 Run it anywhere. AutoResearchClaw isn't locked to a single platform. Use it standalone via CLI, plug it into OpenClaw, or wire it up through any ACP-compatible agent — 🤖 Claude Code, 💻 Codex CLI, 🐙 Copilot CLI, ♊ Gemini CLI, 🌙 Kimi CLI, you name it. And because OpenClaw bridges to messaging platforms, you can kick off a full research run from 💬 Discord, ✈️ Telegram, 🐦 Lark (飞书), 💚 WeChat, or wherever your team already hangs out. One topic in, one paper out — no matter where you type it.

---

🚀 Quick Start

# 1. Clone & install
git clone https://github.com/aiming-lab/AutoResearchClaw.git
cd AutoResearchClaw
python3 -m venv .venv && source .venv/bin/activate
pip install -e .

# 2. Setup (interactive — installs OpenCode beast mode, checks Docker/LaTeX)
researchclaw setup

# 3. Configure
researchclaw init          # Interactive: choose LLM provider, creates config.arc.yaml
# Or manually: cp config.researchclaw.example.yaml config.arc.yaml

# 4. Run
export OPENAI_API_KEY="sk-..."
researchclaw run --config config.arc.yaml --topic "Your research idea" --auto-approve

Output → artifacts/rc-YYYYMMDD-HHMMSS-<hash>/deliverables/ — compile-ready LaTeX, BibTeX, experiment code, charts.

<details> <summary>📝 Minimum required config</summary>

project:
  name: "my-research"

research:
  topic: "Your research topic here"

llm:
  base_url: "https://api.openai.com/v1"
  api_key_env: "OPENAI_API_KEY"
  primary_model: "gpt-4o"
  fallback_models: ["gpt-4o-mini"]

experiment:
  mode: "sandbox"
  sandbox:
    python_path: ".venv/bin/python"

</details>

---

🧠 What Makes It Different

Capability	How It Works
🧑‍✈️ Co-Pilot Mode	6 intervention modes — from fully autonomous to step-by-step. Guide the AI at critical decisions (hypotheses, baselines, paper writing) or let it run free. SmartPause auto-detects when human input would help.
🔄 PIVOT / REFINE Loop	Stage 15 autonomously decides: PROCEED, REFINE (tweak params), or PIVOT (new direction). Artifacts auto-versioned.
🤖 Multi-Agent Debate	Hypothesis generation, result analysis, and peer review each use structured multi-perspective debate.
🧬 Self-Learning	Lessons extracted per run (decision rationale, runtime warnings, metric anomalies) with 30-day time-decay. Future runs learn from past mistakes.
📚 Knowledge Base	Every run builds structured KB across 6 categories (decisions, experiments, findings, literature, questions, reviews).
🛡️ Sentinel Watchdog	Background quality monitor: NaN/Inf detection, paper-evidence consistency, citation relevance scoring, anti-fabrication guard.
🔍 Claim Verification	Inline fact-checking: extracts claims from AI-generated text and cross-references against collected literature. Flags ungrounded citations and fabricated numbers.
🌿 Branch Exploration	Fork the pipeline to explore multiple research directions simultaneously, compare results side-by-side, and merge the best path forward.

---

🦞 OpenClaw Integration

AutoResearchClaw is an OpenClaw-compatible service. Install it in OpenClaw and launch autonomous research with a single message — or use it standalone via CLI, Claude Code, or any AI coding assistant.

</tr> </table>

🚀 Use with OpenClaw (Recommended)

If you already use OpenClaw as your AI assistant:

1️⃣  Share the GitHub repo URL with OpenClaw
2️⃣  OpenClaw auto-reads RESEARCHCLAW_AGENTS.md → understands the pipeline
3️⃣  Say: "Research [your topic]"
4️⃣  Done — OpenClaw clones, installs, configures, runs, and returns results

That's it. OpenClaw handles git clone, pip install, config setup, and pipeline execution automatically. You just chat.

<details> <summary>💡 What happens under the hood</summary>

OpenClaw reads RESEARCHCLAW_AGENTS.md → learns the research orchestrator role
OpenClaw reads README.md → understands installation and pipeline structure
OpenClaw copies config.researchclaw.example.yaml → config.yaml
Asks for your LLM API key (or uses your environment variable)
Runs pip install -e . + researchclaw run --topic "..." --auto-approve
Returns the paper, LaTeX, experiments, and citations

</details>

🔌 OpenClaw Bridge (Advanced)

For deeper integration, AutoResearchClaw includes a bridge adapter system with 6 optional capabilities:

# config.arc.yaml
openclaw_bridge:
  use_cron: true              # ⏰ Scheduled research runs
  use_message: true           # 💬 Progress notifications (Discord/Slack/Telegram)
  use_memory: true            # 🧠 Cross-session knowledge persistence
  use_sessions_spawn: true    # 🔀 Spawn parallel sub-sessions for concurrent stages
  use_web_fetch: true         # 🌐 Live web search during literature review
  use_browser: false          # 🖥️ Browser-based paper collection

Each flag activates a typed adapter protocol. When OpenClaw provides these capabilities, the adapters consume them without code changes. See docs/integration-guide.md for full details.

ACP (Agent Client Protocol)

AutoResearchClaw can use any ACP-compatible coding agent as its LLM backend — no API keys required. The agent communicates via acpx, maintaining a single persistent session across all 23 pipeline stages.

Agent	Command	Notes
Claude Code	`claude`	Anthropic
Codex CLI	`codex`	OpenAI
Copilot CLI	`gh`	GitHub
Gemini CLI	`gemini`	Google
OpenCode	`opencode`	SST
Kimi CLI	`kimi`	Moonshot

# config.yaml — ACP example
llm:
  provider: "acp"
  acp:
    agent: "claude"   # Any ACP-compatible agent CLI command
    cwd: "."          # Working directory for the agent
  # No base_url or api_key needed — the agent handles its own auth.

# Just run — the agent uses its own credentials
researchclaw run --config config.yaml --topic "Your research idea" --auto-approve

🛠️ Other Ways to Run

Method	How
Standalone CLI	`researchclaw run --topic "..." --auto-approve` (autonomous) or `--mode co-pilot` (collaborative)
Python API	`from researchclaw.pipeline import Runner; Runner(config).run()`
Claude Code	Reads `RESEARCHCLAW_CLAUDE.md` — just say "Run research on [topic]"
Copilot CLI	`researchclaw run --topic "..."` with `llm.acp.agent: "gh"`
OpenCode	Reads `.claude/skills/` — same natural language interface
Any AI CLI	Provide `RESEARCHCLAW_AGENTS.md` as context → agent auto-bootstraps

---

🔬 Pipeline: 23 Stages, 8 Phases

Phase A: Research Scoping          Phase E: Experiment Execution
  1. TOPIC_INIT                      12. EXPERIMENT_RUN
  2. PROBLEM_DECOMPOSE               13. ITERATIVE_REFINE  ← self-healing

Phase B: Literature Discovery      Phase F: Analysis & Decision
  3. SEARCH_STRATEGY                 14. RESULT_ANALYSIS    ← multi-agent
  4. LITERATURE_COLLECT  ← real API  15. RESEARCH_DECISION  ← PIVOT/REFINE
  5. LITERATURE_SCREEN   [gate]
  6. KNOWLEDGE_EXTRACT               Phase G: Paper Writing
                                     16. PAPER_OUTLINE
Phase C: Knowledge Synthesis         17. PAPER_DRAFT
  7. SYNTHESIS                       18. PEER_REVIEW        ← evidence check
  8. HYPOTHESIS_GEN    ← debate      19. PAPER_REVISION

Phase D: Experiment Design         Phase H: Finalization
  9. EXPERIMENT_DESIGN   [gate]      20. QUALITY_GATE      [gate]
 10. CODE_GENERATION                 21. KNOWLEDGE_ARCHIVE
 11. RESOURCE_PLANNING               22. EXPORT_PUBLISH     ← LaTeX
                                     23. CITATION_VERIFY    ← relevance check

Gate stages (5, 9, 20) pause for human approval or auto-approve with --auto-approve. On rejection, the pipeline rolls back.

Co-Pilot mode (--mode co-pilot): Deep human-AI collaboration at Stages 7-8 (Idea Workshop), Stage 9 (Baseline Navigator), and Stages 16-17 (Paper Co-Writer). Other stages auto-execute with SmartPause monitoring.

Decision loops: Stage 15 can trigger REFINE (→ Stage 13) or PIVOT (→ Stage 8), with automatic artifact versioning.

<details> <summary>📋 What Each Phase Does</summary>

Phase	What Happens
A: Scoping	LLM decomposes the topic into a structured problem tree with research questions
A+: Hardware	Auto-detects GPU (NVIDIA CUDA / Apple MPS / CPU-only), warns if local hardware is limited, adapts code generation accordingly
B: Literature	Multi-source search (OpenAlex → Semantic Scholar → arXiv) for real papers, screens by relevance, extracts knowledge cards
C: Synthesis	Clusters findings, identifies research gaps, generates testable hypotheses via multi-agent debate
D: Design	Designs experiment plan, generates hardware-aware runnable Python (GPU tier → package selection), estimates resource needs
E: Execution	Runs experiments in sandbox, detects NaN/Inf and runtime bugs, self-heals code via targeted LLM repair
F: Analysis	Multi-agent analysis of results; autonomous PROCEED / REFINE / PIVOT decision with rationale
G: Writing	Outlines → section-by-section drafting (5,000-6,500 words) → peer reviews (with methodology-evidence consistency) → revises with length guard
H: Finalization	Quality gate, knowledge archival, LaTeX export with conference template, citation integrity + relevance verification

</details>

---

✨ Key Features

Feature	Description
📚 Multi-Source Literature	Real papers from OpenAlex, Semantic Scholar & arXiv — query expansion, deduplication, circuit breaker with graceful degradation
🔍 4-Layer Citation Verification	arXiv ID check → CrossRef/DataCite DOI → Semantic Scholar title match → LLM relevance scoring. Hallucinated refs auto-removed.
🖥️ Hardware-Aware Execution	Auto-detects GPU (NVIDIA CUDA / Apple MPS / CPU-only) and adapts code generation, imports, and experiment scale accordingly
🦾 OpenCode Beast Mode	Complex experiments auto-routed to OpenCode — generates multi-file projects with custom architectures, training loops, and ablation studies. Install via `researchclaw setup`.
🧪 Sandbox Experiments	AST-validated code, immutable harness, NaN/Inf fast-fail, self-healing repair, iterative refinement (up to 10 rounds), partial result capture
📝 Conference-Grade Writing	NeurIPS/ICML/ICLR templates, section-by-section drafting (5,000-6,500 words), anti-fabrication guard, revision length guard, anti-disclaimer enforcement
📐 Template Switching	`neurips_2025`, `iclr_2026`, `icml_2026` — Markdown → LaTeX with math, tables, figures, cross-refs, `\cite{}`
🛡️ Anti-Fabrication	VerifiedRegistry enforces ground-truth experiment data in papers. Auto-diagnoses failed experiments and repairs them before writing. Unverified numbers sanitized.
🚦 Quality Gates	3 human-in-the-loop gates (Stages 5, 9, 20) with rollback. Skip with `--auto-approve`.
🧑‍✈️ HITL Co-Pilot	6 intervention modes with per-stage policies. Idea Workshop, Baseline Navigator, Paper Co-Writer for deep collaboration. SmartPause, cost guardrails, escalation policies, and intervention learning for production safety. CLI/WebSocket/MCP adapters.
💰 Cost Guardrails	Budget monitoring with configurable threshold alerts (50%/80%/100%). Pipeline auto-pauses when cost exceeds budget.
🔐 Reproducibility	SHA256 checksums for all stage artifacts. Immutable manifests for verification. Multi-level undo with versioned snapshots.

---

🧑‍✈️ Human-in-the-Loop Co-Pilot

AutoResearchClaw v0.4.0 introduces a complete Human-in-the-Loop (HITL) system that transforms the pipeline from purely autonomous to a human-AI collaborative research engine. Choose your level of involvement:

Intervention Modes

Mode	Command	What It Does
Full Auto	`--auto-approve`	Original behavior — no human intervention
Gate Only	`--mode gate-only`	Pause at 3 gate stages (5, 9, 20) for approval
Checkpoint	`--mode checkpoint`	Pause at each phase boundary (8 checkpoints)
Co-Pilot	`--mode co-pilot`	Deep collaboration at critical stages, auto elsewhere
Step-by-Step	`--mode step-by-step`	Pause after every stage — learn the pipeline
Express	`--mode express`	Quick review — only 3 most critical gates
Custom	`--mode custom`	Define per-stage policies via `stage_policies` config

Co-Pilot Workflow(updated Apr 13, added experiment to prove the best)

You: researchclaw run --topic "Quantum noise as neural network regularization" --mode co-pilot

Pipeline runs Stages 1-7 automatically...

  ┌─────────────────────────────────────────────────────────────┐
  │  HITL | Stage 08: HYPOTHESIS_GEN                            │
  │  Post-stage review                                          │
  │                                                             │
  │  Hypotheses mentioned: 3                                    │
  │  Novelty score: 0.72 (moderate)                             │
  │                                                             │
  │  [a] Approve  [r] Reject  [e] Edit  [c] Collaborate         │
  │  [i] Inject guidance  [v] View output  [q] Abort            │
  └─────────────────────────────────────────────────────────────┘

You: c  (start collaborative chat)
You: Hypothesis 3 is interesting but needs Dropout/Label Smoothing as baselines
AI:  Updated — added Dropout, Label Smoothing, MixUp, CutMix as baselines...
You: approve

Pipeline continues with your refined hypothesis...

CLI Commands

# Start with HITL mode
researchclaw run --topic "..." --mode co-pilot

# Attach to a paused pipeline (from another terminal)
researchclaw attach artifacts/rc-2026-xxx

# Check pipeline and HITL status
researchclaw status artifacts/rc-2026-xxx

# Approve/reject from another terminal or script
researchclaw approve artifacts/rc-2026-xxx --message "LGTM"
researchclaw reject artifacts/rc-2026-xxx --reason "Missing key baseline"

# Inject guidance for a stage (even before it runs)
researchclaw guide artifacts/rc-2026-xxx --stage 9 --message "Use ResNet-50 as primary baseline"

Key Capabilities

Feature	Description
Idea Workshop	Brainstorm, evaluate, and refine hypotheses collaboratively (Stage 7-8)
Baseline Navigator	AI suggests baselines + human adds/removes + reproducibility checklist (Stage 9)
Paper Co-Writer	Section-by-section drafting with human editing and AI polishing (Stage 16-19)
SmartPause	Confidence-driven dynamic pausing — auto-detects when human input would help
Claim Verification	Inline fact-checking against collected literature — flags ungrounded claims
Cost Guardrails	Budget monitoring with 50%/80%/100% threshold alerts
Intervention Learning	ALHF — learns from your review patterns to optimize future pause decisions
Branch Exploration	Fork pipeline to explore multiple hypotheses, compare, merge the best
Escalation Policy	Tiered notification (terminal → Slack → email → auto-halt) when unattended
3 Adapters	CLI (terminal), WebSocket (web dashboard), MCP (external agents)

Configuration

# config.arc.yaml
hitl:
  enabled: true
  mode: co-pilot                     # full-auto | gate-only | checkpoint | co-pilot | custom
  cost_budget_usd: 50.0              # Pause when cost exceeds budget (0 = no limit)

  notifications:
    on_pause: true
    on_quality_drop: true
    channels: ["terminal"]            # terminal | slack | webhook

  timeouts:
    default_human_timeout_sec: 86400  # 24h default wait
    auto_proceed_on_timeout: false

  collaboration:
    max_chat_turns: 50
    save_chat_history: true

  # Per-stage custom policies (optional, for 'custom' mode)
  stage_policies:
    8: { require_approval: true, enable_collaboration: true }
    9: { require_approval: true, allow_edit_output: true }

Backward Compatibility

Default: OFF. Without hitl.enabled: true or --mode, the pipeline behaves exactly as before.
--auto-approve still works. It overrides HITL mode.
All 2,699 existing tests pass with HITL code present.

---

🧠 MetaClaw Integration

AutoResearchClaw + MetaClaw = A pipeline that learns from every run.

MetaClaw adds cross-run knowledge transfer to AutoResearchClaw. When enabled, the pipeline automatically captures lessons from failures and warnings, converts them into reusable skills, and injects those skills into all 23 pipeline stages on subsequent runs — so the same mistakes are never repeated.

How It Works

Run N executes → failures/warnings captured as Lessons
                      ↓
          MetaClaw Lesson → Skill conversion
                      ↓
          arc-* Skill files stored in ~/.metaclaw/skills/
                      ↓
Run N+1 → build_overlay() injects skills into every LLM prompt
                      ↓
          LLM avoids known pitfalls → higher quality, fewer retries

Quick Setup

# 1. Install MetaClaw (if not already)
pip install metaclaw

# 2. Enable in your config

# config.arc.yaml
metaclaw_bridge:
  enabled: true
  proxy_url: "http://localhost:30000"        # MetaClaw proxy (optional)
  skills_dir: "~/.metaclaw/skills"          # Where skills are stored
  fallback_url: "https://api.openai.com/v1" # Direct LLM fallback
  fallback_api_key: ""                      # API key for fallback URL
  lesson_to_skill:
    enabled: true
    min_severity: "warning"                 # Convert warnings + errors
    max_skills_per_run: 3

# 3. Run as usual — MetaClaw works transparently
researchclaw run --config config.arc.yaml --topic "Your idea" --auto-approve

After each run, check ~/.metaclaw/skills/arc-*/SKILL.md to see the skills your pipeline has learned.

Experiment Results

In controlled A/B experiments (same topic, same LLM, same configuration):

Metric	Baseline	With MetaClaw	Improvement
Stage retry rate	10.5%	7.9%	-24.8%
Refine cycle count	2.0	1.2	-40.0%
Pipeline stage completion	18/19	19/19	+5.3%
Overall robustness score (composite)	0.714	0.845	+18.3%

Composite robustness score is a weighted average of stage completion rate (40%), retry reduction (30%), and refine cycle efficiency (30%).

Backward Compatibility

Default: OFF. If metaclaw_bridge is absent or enabled: false, the pipeline behaves exactly as before.
No new dependencies. MetaClaw is optional — the core pipeline works without it.
All 2,699 existing tests pass with the integration code present.

---

🧩 Skills Library

AutoResearchClaw now supports loading open-source and custom skills to further enhance your research experience. We also ship with 20 pre-loaded built-in skills (scientific writing, literature search, chemistry, biology, and more) as ready-to-use references, offering a high degree of flexibility out of the box. Disable any skill by adding enabled: false to its frontmatter.

Sample built-in skills:

Category	Skill	Description
Writing	`scientific-writing`	IMRAD structure, citation formatting, reporting guidelines
Domain	`chemistry-rdkit`	Molecular analysis, SMILES, fingerprints, drug discovery
Experiment	`literature-search`	Systematic review, PRISMA methodology

See all 20 skills with researchclaw skills list.

Load Your Own Skills

# Option 1: Install a skill (persists across projects)
researchclaw skills install /path/to/my-skill/

# Option 2: Drop a SKILL.md into the project
mkdir -p .claude/skills/my-custom-skill
# Then create a SKILL.md with YAML frontmatter (name, description, trigger-keywords, applicable-stages)

# Option 3: Configure shared skill directories in config.arc.yaml
# skills:
#   custom_dirs:
#     - /path/to/team-shared-skills

Using Skills

Skills are loaded and injected into LLM prompts automatically — no manual activation needed. Use the CLI to inspect:

researchclaw skills list               # Show all loaded skills with sources
researchclaw skills validate ./my-skill # Check SKILL.md format

Browse community skills: K-Dense-AI/claude-scientific-skills (150+ scientific skills across multiple disciplines).

---

⚙️ Configuration Reference

<details> <summary>Click to expand full configuration reference</summary>

# === Project ===
project:
  name: "my-research"              # Project identifier
  mode: "docs-first"               # docs-first | semi-auto | full-auto

# === Research ===
research:
  topic: "..."                     # Research topic (required)
  domains: ["ml", "nlp"]           # Research domains for literature search
  daily_paper_count: 8             # Target papers per search query
  quality_threshold: 4.0           # Minimum quality score for papers

# === Runtime ===
runtime:
  timezone: "America/New_York"     # For timestamps
  max_parallel_tasks: 3            # Concurrent experiment limit
  approval_timeout_hours: 12       # Gate stage timeout
  retry_limit: 2                   # Retry count on stage failure

# === LLM ===
llm:
  provider: "openai-compatible"    # openai | openrouter | deepseek | minimax | acp | openai-compatible
  base_url: "https://..."          # API endpoint (required for openai-compatible)
  api_key_env: "OPENAI_API_KEY"    # Env var for API key (required for openai-compatible)
  api_key: ""                      # Or hardcode key here
  primary_model: "gpt-4o"          # Primary model
  fallback_models: ["gpt-4o-mini"] # Fallback chain
  s2_api_key: ""                   # Semantic Scholar API key (optional, higher rate limits)
  acp:                             # Only used when provider: "acp"
    agent: "claude"                # ACP agent CLI command (claude, codex, gemini, etc.)
    cwd: "."                       # Working directory for the agent

# === Literature search ===
literature_search:
  sources: ["openalex", "semantic_scholar", "arxiv"]  # Stage 4 backend order
  max_results_per_query: 40          # Results per query before deduplication
  inter_query_delay_sec: 1.5         # Delay between expanded queries
  openalex_email: "researchclaw@users.noreply.github.com"
  openalex_api_key_env: "OPENALEX_API_KEY"
  openalex_api_key: ""              # Optional; env var is preferred
  s2_api_key_env: "S2_API_KEY"
  s2_api_key: ""                    # Optional; falls back to llm.s2_api_key

# === Experiment ===
experiment:
  mode: "sandbox"                  # simulated | sandbox | docker | ssh_remote
  time_budget_sec: 300             # Max execution time per run (default: 300s)
  max_iterations: 10               # Max optimization iterations
  metric_key: "val_loss"           # Primary metric name
  metric_direction: "minimize"     # minimize | maximize
  sandbox:
    python_path: ".venv/bin/python"
    gpu_required: false
    allowed_imports: [math, random, json, csv, numpy, torch, sklearn]
    max_memory_mb: 4096
  docker:
    image: "researchclaw/experiment:latest"
    network_policy: "setup_only"   # none | setup_only | pip_only | full
    gpu_enabled: true
    memory_limit_mb: 8192
    auto_install_deps: true        # Auto-detect imports → requirements.txt
  ssh_remote:
    host: ""                       # GPU server hostname
    gpu_ids: []                    # Available GPU IDs
    remote_workdir: "/tmp/researchclaw_experiments"
  opencode:                          # OpenCode Beast Mode (auto-installed via `researchclaw setup`)
    enabled: true                    # Master switch (default: true)
    auto: true                       # Auto-trigger without confirmation (default: true)
    complexity_threshold: 0.2        # 0.0-1.0 — higher = only trigger on complex experiments
    model: ""                        # Override model (empty = use llm.primary_model)
    timeout_sec: 600                 # Max seconds for OpenCode generation
    max_retries: 1                   # Retry count on failure
    workspace_cleanup: true          # Remove temp workspace after collection
  code_agent:                        # CodeAgent v2 — multi-phase code generation
    enabled: true                    # Use CodeAgent instead of legacy single-prompt codegen
    architecture_planning: true      # Generate deep implementation blueprint before coding
    sequential_generation: true      # Generate files one-by-one following dependency DAG
    hard_validation: true            # AST-based validation gates (blocks identical ablations, hardcoded metrics)
    hard_validation_max_repairs: 2   # Max repair attempts when validation fails
    exec_fix_max_iterations: 3       # Execution-in-the-loop fix attempts
    exec_fix_timeout_sec: 60         # Timeout per exec-fix attempt
  benchmark_agent:                   # BenchmarkAgent — automated dataset & baseline selection
    enabled: true                    # Enable 4-agent benchmark pipeline (Surveyor→Selector→Acquirer→Validator)
    enable_hf_search: true           # Search HuggingFace Datasets
    enable_web_search: true          # Search Google Scholar for benchmarks
    tier_limit: 2                    # Dataset tier filtering (1=small/cached, 2=medium, 3=large)
    min_benchmarks: 1                # Minimum datasets required
    min_baselines: 2                 # Minimum baseline methods required
  figure_agent:                      # FigureAgent — academic figure generation
    enabled: true                    # Enable 5-agent figure pipeline (Planner→CodeGen→Renderer→Critic→Integrator)
    min_figures: 3                   # Minimum figures to generate
    max_figures: 8                   # Maximum figures
    max_iterations: 3                # Critic-driven refinement iterations
    dpi: 300                         # Output resolution
    strict_mode: false               # Fail pipeline if figure generation fails
  repair:                            # Anti-fabrication experiment repair
    enabled: true                    # Auto-diagnose and repair failed experiments
    max_cycles: 3                    # Repair retry loops
    min_completion_rate: 0.5         # >=50% conditions must complete to proceed
    min_conditions: 2                # At least 2 conditions for valid experiment
    use_opencode: true               # Route repairs through OpenCode Beast Mode

# === Web Search (Optional) ===
web_search:
  enabled: true                      # Enable web-augmented literature search
  tavily_api_key_env: "TAVILY_API_KEY"  # Tavily API key env var (optional)
  enable_scholar: true               # Google Scholar search
  enable_pdf_extraction: true        # Extract text from PDFs
  max_web_results: 10                # Max web results per query

# === Export ===
export:
  target_conference: "neurips_2025"  # neurips_2025 | iclr_2026 | icml_2026
  authors: "Anonymous"
  bib_file: "references"

# === Prompts ===
prompts:
  custom_file: ""                  # Path to custom prompts YAML (empty = defaults)

# === HITL Co-Pilot (NEW in v0.4.0) ===
hitl:
  enabled: false                     # Set to true to enable HITL
  mode: co-pilot                     # full-auto | gate-only | checkpoint | step-by-step | co-pilot | custom
  cost_budget_usd: 0.0              # Cost limit in USD (0 = no limit)
  notifications:
    on_pause: true                   # Notify when pipeline pauses
    on_quality_drop: true            # Notify on quality issues
    channels: ["terminal"]           # terminal | slack | webhook
  timeouts:
    default_human_timeout_sec: 86400 # Wait up to 24h for human input
    auto_proceed_on_timeout: false   # If true, auto-approve on timeout
  collaboration:
    max_chat_turns: 50               # Max turns per collaboration session
    save_chat_history: true          # Persist chat logs
  stage_policies: {}                 # Per-stage overrides (for 'custom' mode)

# === Security ===
security:
  hitl_required_stages: [5, 9, 20] # Stages requiring human approval
  allow_publish_without_approval: false
  redact_sensitive_logs: true

# === Knowledge Base ===
knowledge_base:
  backend: "markdown"              # markdown | obsidian
  root: "docs/kb"

# === Notifications ===
notifications:
  channel: "console"               # console | discord | slack
  target: ""

# === MetaClaw Bridge (Optional) ===
metaclaw_bridge:
  enabled: false                   # Set to true to enable cross-run learning
  proxy_url: "http://localhost:30000"  # MetaClaw proxy URL
  skills_dir: "~/.metaclaw/skills" # Where arc-* skills are stored
  fallback_url: ""                 # Direct LLM fallback when proxy is down
  fallback_api_key: ""             # API key for fallback endpoint
  lesson_to_skill:
    enabled: true                  # Auto-convert lessons to skills
    min_severity: "warning"        # Minimum severity to convert
    max_skills_per_run: 3          # Max new skills per pipeline run
  prm:                             # Process Reward Model quality gate (optional)
    enabled: false                 # Use LLM-as-judge to score stage outputs
    model: "gpt-5.4"              # PRM judge model
    votes: 3                       # Majority vote count
    gate_stages: [5, 9, 15, 20]   # Stages to apply PRM gates

# === OpenClaw Bridge ===
openclaw_bridge:
  use_cron: false                  # Scheduled research runs
  use_message: false               # Progress notifications
  use_memory: false                # Cross-session knowledge persistence
  use_sessions_spawn: false        # Spawn parallel sub-sessions
  use_web_fetch: false             # Live web search
  use_browser: false               # Browser-based paper collection

</details>

---

🔭 HEP-ph Physics Mode (collider_agent)

When project.profile=hep_ph and experiment.mode=collider_agent, the pipeline routes Stage 12 through ColliderAgent (Lagrangian → FeynRules → MadGraph5 → figures via Magnus cloud) instead of the default Python ML sandbox.

Mid-stage HITL

Stage 10 (CODE_GENERATION) becomes a HITL gate. The pipeline pauses with collider_plan.md open in $EDITOR so you can review or edit the physics prompt before ColliderAgent runs. Reject sends control back to Stage 9 (EXPERIMENT_DESIGN); the hypothesis from Stage 8 stays intact.

Incremental experiments (`--incremental-experiment`)

To add new mass points or analyses to a completed run without redoing the heavy simulation, re-launch with --incremental-experiment and either --from-stage CODE_GENERATION (also edit the prompt) or --from-stage EXPERIMENT_RUN (reuse existing prompt):

python -m researchclaw run --profile hep_ph --output artifacts/<run_id> \
    --from-stage CODE_GENERATION --incremental-experiment

The Stage 12 sandbox will:

Snapshot the existing stage-12/ tree to stage-12_v{N}/.
Save the previous collider_plan.md as collider_plan.prev.md.
Build workspace_manifest.json listing reusable artifacts.
Send ColliderAgent a three-part prompt:

CONTINUATION CONTEXT + PRIOR PLAN + your new delta.

Merge ColliderAgent's new results.json with the snapshot's prior one

(metrics: new wins on collisions, old-only kept; artifact lists: concat + dedupe). The merge is recorded in incremental_merge.json.

Stage 13 then promotes the merged state to experiment_final/ as before.

Note: re-entering at Stage 13 alone is a no-op in collider mode and will NOT run any new physics — Stage 13 is a shutil.copy2 passthrough. PIVOT (Stage 15 decision) intentionally remains destructive because changing the hypothesis makes prior events invalid.

---

🙏 Acknowledgments

Inspired by:

🔬 AI Scientist (Sakana AI) — Automated research pioneer
🧠 AutoResearch (Andrej Karpathy) — End-to-end research automation
🌐 FARS (Analemma) — Fully Automated Research System

---

📄 License

MIT — see LICENSE for details.

---

📌 Citation

If you find AutoResearchClaw useful, please cite:

@misc{liu2026autoresearchclawselfreinforcingautonomousresearch,
      title={AutoResearchClaw: Self-Reinforcing Autonomous Research with Human-AI Collaboration},
      author={Jiaqi Liu and Shi Qiu and Mairui Li and Bingzhou Li and Haonian Ji and Siwei Han and Xinyu Ye and Peng Xia and Zihan Dong and Congyu Zhang and Letian Zhang and Guiming Chen and Haoqin Tu and Xinyu Yang and Lu Feng and Xujiang Zhao and Haifeng Chen and Jiawei Zhou and Xiao Wang and Weitong Zhang and Hongtu Zhu and Yun Li and Jieru Mei and Hongliang Fei and Jiaheng Zhang and Linjie Li and Linjun Zhang and Yuyin Zhou and Sheng Wang and Caiming Xiong and James Zou and Zeyu Zheng and Cihang Xie and Mingyu Ding and Huaxiu Yao},
      year={2026},
      eprint={2605.20025},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2605.20025},
}

<p align="center"> <sub>Built with 🦞 by the AutoResearchClaw team</sub> </p>

安装

选择适合你当前 Agent 的方式

01 · 发给当前 Agent自动识别兼容方式与安装位置

请帮我安装「AutoResearchClaw」。它是一个 Agent Skill 或仓库级 Skill 包。
来源：https://github.com/aiming-lab/AutoResearchClaw

请先阅读仓库的 README、SKILL.md 与安装说明，再按我当前使用的 Agent 的原生规范安装；如果仓库兼容 Agent Skills CLI，优先执行「npx skills add https://github.com/aiming-lab/AutoResearchClaw」。不要写死为某一个 Agent 的专用目录，不覆盖已有同名文件。完成后告诉我实际安装位置、启用方式和验证结果。

在 GitHub 查看原文

本站按仓库级 package 收录，安装时克隆完整 AutoResearchClaw 仓库；已核验的 skill 入口为 .claude/skills/a-evolve/SKILL.md。

运行前阅读 README、依赖说明和 MIT 许可；涉及账号、外部写入或高风险领域时保留人工确认。

安装即视为你已阅读并遵守该 skill 的开源协议。内容版权归原作者所有。

AutoResearchClaw

主要能力

使用方式

适用边界

许可与来源

🔥 News

⚡ One Command. One Paper.

🤔 What Is This?

🚀 Quick Start

🧠 What Makes It Different

🦞 OpenClaw Integration

🚀 Use with OpenClaw (Recommended)

🔌 OpenClaw Bridge (Advanced)

ACP (Agent Client Protocol)

🛠️ Other Ways to Run

🔬 Pipeline: 23 Stages, 8 Phases

✨ Key Features

🧑‍✈️ Human-in-the-Loop Co-Pilot

Intervention Modes

Co-Pilot Workflow(updated Apr 13, added experiment to prove the best)

CLI Commands

Key Capabilities

Configuration

Backward Compatibility

🧠 MetaClaw Integration

How It Works

Quick Setup

Experiment Results

Backward Compatibility

🧩 Skills Library

Load Your Own Skills

Using Skills

⚙️ Configuration Reference

🔭 HEP-ph Physics Mode (collider_agent)

Mid-stage HITL

Incremental experiments (--incremental-experiment)

🙏 Acknowledgments

📄 License

📌 Citation

Incremental experiments (`--incremental-experiment`)