CoEvo-RSI: When Harnesses Evolve, Which Models Should Follow?

Key points

• 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 it already possesses.1
• We introduce CoEvo-RSI, a RSI framework that advances model--harness co-evolution through agent-driven, bidirectional historical inheritance.1
• Drawing on a shared evolutionary archive and execution feedback, a meta-agent coordinates harness development from historical designs with model checkpoint selection or merging and subsequent adaptation under the selected harness, aligning inherited capabilities with evolving execution requirements to reduce brain--body mismatch.1
• Using only supervised fine-tuning (SFT) on its own rejection-sampled trajectories, CoEvo-RSI makes Qwen3.5-9B competitive with larger models across diverse vertical applications, including multi-agent failure attribution, role-aware text-to-SQL, and smart-home instruction following.1

Sources (2)

• [1]CoEvo-RSI: When Harnesses Evolve, Which Models Should Follow?
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 07:38 AM

  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 it already possesses.

• [2]CoEvo-RSI: When Harnesses Evolve, Which Models Should Follow?
  Hugging Face Daily Papers · Oct 18, 12:00 AM · same content

Extractive summary: sentences quoted from the sources.