# Chat an Idea.
Get a Paper.

AutoResearchClaw is a fully autonomous 23-stage pipeline that transforms a research topic into a conference-ready paper — with real experiments, GPU-accelerated code, and verified citations.

Get Started Explore Pipeline
# one command, one paper
python -m researchclaw run --topic "your research idea"
23
Autonomous Stages
1117
Tests Passing
3
Literature APIs
GPU
Docker Sandbox

## From Idea to Paper in 23 Steps

Eight autonomous phases transform a research topic into a publication-ready manuscript.

🎯

### A: Research Scoping

Topic initialization, problem decomposition, and scope definition.

📚

### B: Literature Discovery

Multi-source paper search via OpenAlex, Semantic Scholar, and arXiv with quality screening.

🧠

### C: Knowledge Synthesis

Gap analysis, trend synthesis, and novel hypothesis generation.

⚙️

### D: Experiment Design

Methodology design, code generation, and resource planning with hardware awareness.

🚀

### E: Experiment Execution

GPU-accelerated Docker sandbox execution with iterative refinement.

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### F: Analysis & Decision

Result analysis with pivot/refine/proceed decisions.

✍️

### G: Paper Writing

Structured drafting, multi-agent peer review, and iterative revision.

✔️

### H: Finalization

Quality gate, knowledge archival, LaTeX export, and citation verification.

View Full Pipeline →

## Key Features

Built for serious research, engineered for reliability.

🔍

### Real Literature Search

Multi-source search across OpenAlex, Semantic Scholar, and arXiv with circuit breakers, rate limiting, and intelligent caching.

🐋

### Docker Sandbox + GPU

Experiments run in isolated Docker containers with NVIDIA GPU passthrough, network sandboxing, and automatic dependency management.

🤖

### Multi-Agent Peer Review

Simulated conference-style peer review with multiple reviewer personas providing structured feedback for revision.

🔄

### Iterative Refinement

Automatic pivot/refine/proceed decisions with rollback to any previous stage based on experiment outcomes.

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### Conference-Ready LaTeX

Publication-quality LaTeX output with proper citations, experiment charts, and structured abstracts.

✅

### Citation Verification

All citations verified against CrossRef, OpenAlex, and arXiv APIs to ensure bibliography accuracy.

All Features →

## Showcase Papers

Papers generated entirely by the pipeline, from topic to camera-ready PDF.

📄

### Curriculum Learning with Adaptive Difficulty Scheduling for Image Classification

Computer Vision Coming Soon

Investigates adaptive curriculum strategies on CIFAR-10/100 benchmarks, demonstrating improved convergence speed and final accuracy compared to standard training.

📄

### Test-Time Adaptation via Batch Normalization Statistics for Distribution Shift

Domain Adaptation Coming Soon

Explores test-time adaptation methods using batch normalization statistics to handle distribution shift on CIFAR-10-C corruption benchmarks.

📄

### Entropy-Guided Exploration Bonuses for Sparse-Reward Continuous Control

Reinforcement Learning Coming Soon

Proposes entropy-guided intrinsic reward bonuses to improve exploration in sparse-reward MuJoCo locomotion environments.

View All Papers →

## System Architecture

End-to-end pipeline architecture from topic input to published paper.

## Ready to Generate Your First Paper?

Clone the repo, configure your LLM API key, and run your first autonomous research paper.

Quick Start Guide View on GitHub