Borui Kang presents Harness Continual Learning (HCL), where an agent's harness (prompts, memories, tools, skills, routing rules) evolves around a frozen foundation model, unlike updating model parameters. The paper defines "harness-level forgetting" as losing reliable behavior due to harness updates and proposes a guarded evolution mechanism. A Continual Optimizer generates candidate harnesses from feedback, and a Continual Evaluator commits changes only after verifying improvement, retention, and validity. Experiments in textual reasoning, multimodal perception, and open-world interaction show capability accumulation and failure recovery, with over 10% relative gains versus baselines.
- Four execution-facing harness components: Task Interface, Experience Memory, Capability Map, and Adaptive Router.
- Controlled retention sweeps show the stability''-plasticity trade-off can be explicitly adjusted at the harness level.
- The work reframes continual learning away from parameter updates toward externalized, inspectable agent state.
- Four execution-facing harness components: Task Interface, Experience Memory, Capability Map, and Adaptive Router.
- Controlled retention sweeps show the stability''-plasticity trade-off can be explicitly adjusted at the harness level.
- The work reframes continual learning away from parameter updates toward externalized, inspectable agent state.
