AutoResearchClaw releases

v0.4.0 — Human-in-the-Loop Co-Pilot System

AutoResearchClaw is no longer purely autonomous. The new HITL Co-Pilot system transforms the pipeline into a human-AI collaborative research engine.

Highlights

• 6+ Intervention Modes: full-auto, gate-only, checkpoint, step-by-step, co-pilot, custom, express
• Idea Workshop: Brainstorm and refine hypotheses collaboratively (Stages 7-8)
• Baseline Navigator: Review and customize experiment designs (Stage 9)
• Paper Co-Writer: Section-by-section collaborative drafting (Stages 16-19)
• SmartPause: Confidence-driven dynamic intervention
• ALHF Intervention Learning: Learns from your review patterns
• Claim Verification: Inline fact-checking against collected literature
• Cost Guardrails: Budget monitoring with threshold alerts
• Pipeline Branching: Fork to explore multiple research directions
• CLI Commands: attach, status, approve, reject, guide
• 3 Adapters: CLI, WebSocket, MCP

New Files

• researchclaw/hitl/ — 34 modules (7,500+ lines)
• tests/test_hitl_*.py — 9 test files (242 tests)
• docs/HITL_GUIDE.md — 620-line guide
• 3 new builtin skills

Testing

• 2,753 tests passed, 0 failures

Full Changelog: https://github.com/aiming-lab/AutoResearchClaw/compare/v0.3.2...v0.4.0

What's New

Cross-Platform Support

• ACP-compatible agent backends: Claude Code, Codex CLI, Copilot CLI, Gemini CLI, Kimi CLI
• OpenClaw bridge: messaging platform integration (Discord, Telegram, Lark, WeChat)
• CLI-agent code generation backend: delegates Stages 10 & 13 to external CLI agents with budget control and timeout management

Anti-Fabrication System

• VerifiedRegistry: ground-truth whitelist from experiment results with tolerance matching
• Experiment diagnosis & repair loop: 13 deficiency categories, auto-repair with best-result selection
• Always-on sanitization: unverified numbers replaced in paper tables

Stability & Quality

• 100+ bug fixes across 8 deep audit rounds
• Modular executor refactoring (10K → 400-line facade)
• --resume auto-detection for interrupted runs
• LLM retry hardening with exponential backoff
• Community-reported fixes (macOS M3, math/theoretical topics)

New Subsystems

• Assessor (paper quality scoring + venue recommendation)
• Calendar (conference deadline tracking)
• Collaboration (multi-user research coordination)
• Copilot (interactive steering modes)
• Dashboard (real-time metrics broadcasting)
• Knowledge Graph (entity extraction + visualization)
• Memory (cross-run experiment/ideation/writing memory)
• MCP (Model Context Protocol server)
• Overleaf (live sync with conflict resolution)
• Project Manager (multi-project scheduling)
• Remote Servers (SSH/SLURM/cloud execution)
• Skills Library (12 built-in domain/tooling skills)
• Trends (daily arXiv digest + opportunity finder)
• Voice (speech-to-text commands)
• Wizard (guided project setup)

Testing

• 1,935 tests passing

Full Changelog: https://github.com/aiming-lab/AutoResearchClaw/compare/v0.3.1...v0.3.2

Highlights

This release introduces three multi-agent subsystems, a hardened Docker sandbox, and 4 rounds of paper quality auditing — significantly improving the end-to-end quality of generated research papers.

New Multi-Agent Subsystems

CodeAgent (4-phase architecture)

• LLM generates multi-file experiment code (main.py + setup.py + requirements.txt)
• Static analysis & deep validation (AST-based class/method checks)
• LLM-guided code review with structured JSON feedback
• Iterative repair loop (up to 3 rounds) with automatic UnboundLocalError fix

BenchmarkAgent (4 sub-agents: Surveyor → Selector → Acquirer → Validator)

• Domain-aware dataset and baseline selection from 13-domain knowledge base
• Automatic benchmark acquisition with Docker compatibility validation
• Integrated at Stage 9 (experiment_design), output injected into Stage 10

FigureAgent (5 sub-agents: Planner → CodeGen → Renderer → Critic → Integrator)

• Academic-quality chart generation with SciencePlots, 300 DPI, colorblind-safe palette
• 6 built-in chart templates + LLM fallback for custom visualizations
• Tri-modal critic review (data accuracy, aesthetics, academic convention)

Docker Sandbox Enhancements

• Network-policy-aware code generation: none | setup_only | pip_only | full
• Dynamic dependency installation via requirements.txt
• Pre-cached datasets: CIFAR-10/100, MNIST, FashionMNIST, STL-10, SVHN
• Extended ML stack: torch, torchvision, timm, einops, transformers, etc.

Paper Quality Hardening (4-round audit)

• Post-compilation quality checks, weasel/duplicate word lint
• 7-dimension AI-Scientist-style review scoring
• AI-slop detection (50+ phrases), statistical rigor validator
• Cross-discipline support for 7 research domains (ML/physics/chem/econ/math/eng/bio)
• NeurIPS checklist integration

Bug Fixes (15+)

• Fix baselines dict-to-list crash in BenchmarkAgent
• Fix Gymnasium environment versions (v4 → v5)
• Fix experiment condition drift in iterative refinement (anchor to exp_plan.yaml)
• Fix compute budget constraint for experiment design
• Fix metric direction mismatch, citation verification batching
• Fix LaTeX output sanitization, figure plan format handling
• Add RL stability guidance (gradient clipping, NaN guard)
• And more — see full commit message for details

Compatibility

All changes are backward-compatible with v0.1.0 configuration files.

Full Changelog: https://github.com/aiming-lab/AutoResearchClaw/compare/v0.1.0...v0.2.0

AutoResearchClaw v0.1.0

Fully autonomous research pipeline: one message in, full conference paper out. 🦞

Highlights

• 23-stage pipeline: Research Scoping → Literature Discovery → Knowledge Synthesis → Hypothesis Generation → Experiment Design → Self-Healing Execution → Analysis & Decision → Paper Writing → Citation Verification
• Multi-agent debate: 3 agents (Innovator, Pragmatist, Contrarian) argue over hypotheses; adversarial analysis panel reviews results
• Self-healing executor: autonomous crash diagnosis, code repair, and Pivot/Refine decisions
• Cross-run evolution: time-decayed lesson store that improves future runs
• Citation verification: 4-layer pipeline (arXiv, DOI, Semantic Scholar, LLM relevance check)
• OpenClaw integration: trigger full runs from a chat message

Results (6 end-to-end runs)

• 100% pipeline completion (124/124 steps)
• 94.3% citation integrity
• Mean quality 6.2/10 on conference review scale

Requirements

• Python 3.9+
• OpenAI-compatible LLM API