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LLM-ACES outperforms traditional methods by leveraging a closed-loop feedback mechanism that iteratively refines both hypotheses and data acquisition, achieving unprecedented accuracy in ODE recovery.
Multi-agent RAG gets a 38% performance boost by letting agents dynamically rewire themselves and evolve their own prompts based on experience.
