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AutoResearchClaw Framework

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🏆 Paper Showcase · 📖 Integration Guide · 💬 Discord Community

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🏆 Generated Paper Showcase

8 papers across 8 domains — math, statistics, biology, computing, NLP, RL, vision, robustness — generated fully autonomously with zero human intervention.

--- > **🧪 We're looking for testers!** Try the pipeline with your own research idea — from any field — and [tell us what you think](docs/TESTER_GUIDE.md). Your feedback directly shapes the next version. **[→ Testing Guide](docs/TESTER_GUIDE.md)** | **[→ 中文测试指南](docs/TESTER_GUIDE_CN.md)** | **[→ 日本語テストガイド](docs/TESTER_GUIDE_JA.md)** --- ## 🔥 News - **[03/22/2026]** [v0.3.2](https://github.com/aiming-lab/AutoResearchClaw/releases/tag/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. - **[03/18/2026]** [v0.3.1](https://github.com/aiming-lab/AutoResearchClaw/releases/tag/v0.3.1) — **OpenCode Beast Mode + Community Contributions** — New "Beast Mode" routes complex code generation to [OpenCode](https://github.com/anomalyco/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](https://github.com/aiming-lab/AutoResearchClaw/releases/tag/v0.3.0) — **MetaClaw Integration** — AutoResearchClaw now supports [MetaClaw](https://github.com/aiming-lab/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](#-metaclaw-integration). - **[03/16/2026]** [v0.2.0](https://github.com/aiming-lab/AutoResearchClaw/releases/tag/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](https://github.com/aiming-lab/AutoResearchClaw/releases/tag/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. --- ## ⚡ One Command. One Paper. ```bash pip install -e . && researchclaw setup && researchclaw init && researchclaw run --topic "Your research idea here" --auto-approve ``` --- ## 🤔 What Is This? **You think it. AutoResearchClaw writes it.** 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. No babysitting. No copy-pasting. No hallucinated references.

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

The pipeline runs **end-to-end without human intervention**. When experiments fail, it self-heals. When hypotheses don't hold, it pivots. When citations are fake, it kills them. 🌍 **Run it anywhere.** AutoResearchClaw isn't locked to a single platform. Use it standalone via CLI, plug it into [OpenClaw](https://github.com/openclaw/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 ```bash # 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-/deliverables/` — compile-ready LaTeX, BibTeX, experiment code, charts.

📝 Minimum required config

```yaml 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" ```

--- ## 🧠 What Makes It Different | Capability | How It Works | |-----------|-------------| | **🔄 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. | --- ## 🦞 OpenClaw Integration **AutoResearchClaw is an [OpenClaw](https://github.com/openclaw/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.

### 🚀 Use with OpenClaw (Recommended) If you already use [OpenClaw](https://github.com/openclaw/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.

💡 What happens under the hood

1. OpenClaw reads `RESEARCHCLAW_AGENTS.md` → learns the research orchestrator role 2. OpenClaw reads `README.md` → understands installation and pipeline structure 3. OpenClaw copies `config.researchclaw.example.yaml` → `config.yaml` 4. Asks for your LLM API key (or uses your environment variable) 5. Runs `pip install -e .` + `researchclaw run --topic "..." --auto-approve` 6. Returns the paper, LaTeX, experiments, and citations

### 🔌 OpenClaw Bridge (Advanced) For deeper integration, AutoResearchClaw includes a **bridge adapter system** with 6 optional capabilities: ```yaml # 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`](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](https://github.com/openclaw/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 | ```yaml # 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. ``` ```bash # 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 setup` → `researchclaw init` → `researchclaw run --topic "..." --auto-approve` | | **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. > **Decision loops**: Stage 15 can trigger REFINE (→ Stage 13) or PIVOT (→ Stage 8), with automatic artifact versioning.

📋 What Each Phase Does

| 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 |

--- ## ✨ 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](https://github.com/anomalyco/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`. | --- ## 🧠 MetaClaw Integration **AutoResearchClaw + [MetaClaw](https://github.com/aiming-lab/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 ```bash # 1. Install MetaClaw (if not already) pip install metaclaw # 2. Enable in your config ``` ```yaml # 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 ``` ```bash # 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 1,823 existing tests pass** with the integration code present. --- ## ⚙️ Configuration Reference

Click to expand full configuration reference

```yaml # === 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 # === 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) # === 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 ```

--- ## 🙏 Acknowledgments Inspired by: - 🔬 [AI Scientist](https://github.com/SakanaAI/AI-Scientist) (Sakana AI) — Automated research pioneer - 🧠 [AutoResearch](https://github.com/karpathy/autoresearch) (Andrej Karpathy) — End-to-end research automation - 🌐 [FARS](https://analemma.ai/blog/introducing-fars/) (Analemma) — Fully Automated Research System --- ## 📄 License MIT — see [LICENSE](LICENSE) for details. --- ## 📌 Citation If you find AutoResearchClaw useful, please cite: ```bibtex @misc{liu2026autoresearchclaw, author = {Liu, Jiaqi and Xia, Peng and Han, Siwei and Qiu, Shi and Zhang, Letian and Chen, Guiming and Tu, Haoqin and Yang, Xinyu and and Zhou, Jiawei and Zhu, Hongtu and Li, Yun and Zhou, Yuyin and Zheng, Zeyu and Xie, Cihang and Ding, Mingyu and Yao, Huaxiu}, title = {AutoResearchClaw: Fully Autonomous Research from Idea to Paper}, year = {2026}, organization = {GitHub}, url = {https://github.com/aiming-lab/AutoResearchClaw}, } ```

_{Built with 🦞 by the AutoResearchClaw team}