Banger paper on harness continual learning.

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If you already are allowing your agents to rewrite their own prompts, skills, or memory files, this one is worth your time.

(bookmark it)

Continual learning has always tracked what changes in the weights. Modern agents accumulate experience in the harness instead, across prompts, memories, tools, skills, and routing rules.

What this means is that if you update any harness component, previously reliable behavior can break with the model completely untouched. The paper names that harness-level forgetting and provides a way to measure it.

Guarded harness evolution separates proposing an update from committing it. A Continual Optimizer drafts a candidate harness from post-execution feedback, and a Continual Evaluator commits only after checking current improvement, historical retention, and validity.

Relative gains exceed 10% across textual reasoning, multimodal perception, and open-world interaction.

Paper: https://t.co/58HvPcpANi

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