CoEvo-RSI: When Harnesses Evolve, Which Models Should Follow?
Model--harness co-evolution has emerged as a prominent paradigm for recursive self-improving (RSI) agents, jointly evolving their execution scaffolds and intrinsic capabilities. We study the persistent yet often overlooked brain--body mismatch problem in this paradigm, where an evolving harness (body) demands behaviors its model (brain) has not learned or restricts capabilities
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Model--harness co-evolution has emerged as a prominent paradigm for recursive self-improving (RSI) agents, jointly evolving their execution scaffolds and intrinsic capabilities. We study the persistent yet often overlooked brain--body mismatch problem in this paradigm, where an evolving harness (body) demands behaviors its model (brain) has not learned or restricts capabilities
This research addresses a critical challenge in recursive self-improving (RSI) agents: the 'brain-body mismatch' problem. For ML engineers, understanding co-evolutionary frameworks like CoEvo-RSI is crucial for developing robust and continuously improving AI agents, especially in complex, dynamic environments where models and their execution scaffolds must adapt in tandem.