"Agent Laboratory: Using LLM Agents as Research Assistants"

Think it, Agent Laboratory builds it, a research assistant that never sleeps

Agent Laboratory enables researchers to automate tedious research tasks while maintaining control over ideation, reducing research costs by 84% compared to existing methods.

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https://arxiv.org/abs/2501.04227

🔧 Methods in this Paper:

→ Agent Laboratory introduces a three-phase workflow: literature review, experimentation, and report writing, each powered by specialized LLM agents.

→ The system features both autonomous and co-pilot modes, allowing researchers to provide feedback at each stage.

→ A PhD agent handles literature review using arXiv API, while ML Engineer agents manage code generation and experimentation through mle-solver.

→ The paper-solver module generates research reports in standard academic format, with automated quality assessment.

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💡 Key Insights:

→ Human involvement significantly improves research quality compared to fully autonomous operation

→ O1-preview backend generates the best overall research outcomes

→ The mle-solver outperforms existing ML automation tools on standardized benchmarks

→ Co-pilot mode achieves higher scores than autonomous mode but faces challenges in aligning with researcher intent

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📊 Results:

→ 84% reduction in research costs compared to previous methods

→ Only $2.33 per paper with gpt-4o backend

→ O1-preview achieved highest usefulness (4.4/5) and report quality (3.4/5)

→ Mle-solver earned more gold and silver medals than MLAB, OpenHands, and AIDE on MLE-Bench

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