AI Research
Mara Chain: Rethinking Failure as a Stepping Stone for AI System Auto-Evolution
Harnessing failure loops to enable self-improving, autonomous AI system architectures.
Optimizing deployed AI systems increasingly amounts to editing prompts, skills, harnesses, and code rather than model weights. Existing approaches commonly optimize these artifacts through propose-evaluate-select procedures, where candidate configurations are evaluated and only t...
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ArXiv CS.LG
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ArXiv CS.LG
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Mara Chain shifts the focus of AI development from static model training to a dynamic, iterative process of artifact evolution. By viewing system failure as a structured feedback loop, it enables autonomous optimization that bypasses the need for costly weight adjustments.
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