Story · arXiv (via papers.cool)
Harness Continual Learning: Continual Adaptation Beyond Model Parameters (arXiv (via papers.cool))
paper · Story page
The paper treats prompts, memories, tools, skills, and routing rules as state evolving around a frozen model, and names the failure mode: a harness update can break behavior acquired earlier. Updates commit only after an evaluator checks improvement, retention, and validity.
In plain words
- Researchers proposed updating an artificial intelligence system's instructions, memories, tools, and decision rules without retraining its response generator.
- A proposed update is tested before it becomes the system's new working setup.
- The test checks whether the update improves current work, preserves earlier abilities, and remains valid.
- This process could help builders improve these systems over time without accidentally breaking behavior that previously worked.
Appeared in
- v0 keeps OAuth tokens out of generated code, plus SkillGate and Temporal's harness
Aug 21, 2026 · in the sections
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