# Features

Everything you need for autonomous research paper generation, built for reliability and quality.

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### Multi-Source Literature Search

Searches OpenAlex (primary, 10K/day), Semantic Scholar, and arXiv in parallel. Intelligent source fallback ensures results even when individual APIs are rate-limited.

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### Rate Limit Defense

Five-layer defense: adaptive rate limiter, three-state circuit breaker, multi-source fallback, intelligent caching with per-source TTL, and request optimization via S2 batch API.

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### Docker Sandbox with GPU

Experiments run in isolated Docker containers based on nvidia/cuda:12.4.1 with PyTorch, GPU passthrough, network sandboxing, and pre-cached datasets (CIFAR-10, FashionMNIST).

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### Hardware-Aware Design

Automatically detects available GPU memory and adjusts experiment parameters (batch size, model size, training epochs) to fit within hardware constraints.

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### Multi-Agent Peer Review

Simulated conference-style peer review with multiple LLM reviewer personas providing structured feedback on technical soundness, methodology, and clarity.

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### Pivot / Refine / Proceed

After analyzing experiment results, the pipeline autonomously decides whether to proceed with paper writing, refine the experiment, or pivot to a new hypothesis (max 2 pivots).

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### Experiment Charts

Automatically generates publication-quality comparison charts, filtering out timing/meta metrics. Supports bar charts, learning curves, and ablation visualizations.

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### Conference-Ready LaTeX

Outputs publication-quality LaTeX with proper bibliography, figure placement, and conference formatting (NeurIPS, ICLR, ICML templates).

✅

### Citation Verification

Every cited paper is verified against real academic databases (CrossRef, OpenAlex, arXiv, Semantic Scholar) in optimized order to minimize API pressure.

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### Result Caching

Per-source cache TTL: arXiv results cached 24h (daily metadata updates), S2/OpenAlex 3 days, citation verification results cached permanently.

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### Seminal Paper Library

Built-in seed library of foundational ML papers (normalization, ResNets, transformers, etc.) injected during literature search to ensure key references are cited.

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### Code Security Validation

Generated experiment code is validated for security (no network access, no subprocess calls, no file system writes outside workspace) before Docker execution.

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### Contradiction Detection

Automatically detects contradictions in experiment results: null findings, negative results, and cases where control outperforms proposed method.

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### Quality Assessment

Built-in quality scoring across novelty, soundness, significance, clarity, and reproducibility. Papers below threshold trigger rewriting.

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### Knowledge Archive

Research findings are archived in a persistent knowledge base (Markdown-backed) for cross-project knowledge transfer and future reference.

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### LLM Fine-Tuning

Optional QLoRA/LoRA fine-tuning support for adapting language models to specific research domains and writing styles.

## How We Compare

AutoResearchClaw vs. other autonomous research tools

Feature | AutoResearchClaw | PaperClaw | Sibyl | Idea2Paper
--- | --- | --- | --- | ---
Literature search | 3 APIs + cache | 2 APIs | arXiv only | Offline KG
Rate limit handling | Circuit breaker + fallback | Exponential backoff | None | N/A
Code execution | Docker + GPU | No | No | No
Peer review | Multi-agent | No | Single agent | No
Citation verification | 4 API sources | No | No | No
Pipeline stages | 23 | ~8 | ~5 | ~6