arxiv arXiv cs.AI · 1h ago · research

Harness Continual Learning adapts agent state outside frozen model parameters

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The authors introduce Harness Continual Learning (HCL), a paradigm where an agent improves by evolving its execution-facing harness rather than updating model parameters. This approach addresses the challenge of retaining previously acquired behavior while adapting to new experiences through components like prompts, memories, and routing rules.

HCL utilizes four components: Task Interface, Experience Memory, Capability Map, and Adaptive Router.
Guarded harness evolution separates update generation from state commitment using a Continual Optimizer and Evaluator.
Experiments show relative gains exceeding 10% over baselines in textual reasoning, multimodal perception, and open-world interaction.
Controlled retention sweeps reveal measurable harness-level forgetting and allow explicit adjustment of the stability-plasticity trade-off.

This method enables capability accumulation and failure recovery without disrupting behavior acquired earlier by keeping the foundation model frozen.

Importance 2/3 New harness with differentiators arXiv cs.AI Research paper Training methods

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