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Life sciences · Preprint
arXiv · August 19, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is an unpublished methodological proposal introducing Harness Continual Learning (HCL), a framework that adapts prompts, memories, tools, and routing rules around a frozen model rather than updating model parameters. Early experiments on three task domains report relative gains exceeding 10% over baselines and demonstrate harness-level forgetting; however, the work lacks peer review, precise experimental detail, and comparison to established continual learning methods.
Preprint. Agent-based systems; not human subjects.. Intervention: Harness Continual Learning (HCL) with guarded harness evolution separating update generation from state commitment.. Compared with: Corresponding baselines (specific methods not named in abstract)..
Relative gains exceeding 10% over corresponding baselines in multiple settings (textual reasoning, multimodal perception, open-world interaction) Component ablations assess contribution of Task Interface, Experience Memory, Capability Map, and Adaptive Router Controlled retention sweeps reveal measurable harness-level forgetting and demonstrate adjustable stability–plasticity trade-off
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Early-stage methodological work proposing a novel continual learning framework with proof-of-concept experiments on multiple tasks but no clinical or patient outcomes, no comparison to established gold-standard methods, and no peer review.
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Quoted from the source exactly as published.
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Continual learning has largely been model-centric, treating model parameters as the state that changes with sequential experience. Modern agents can also adapt through a harness of prompts, memories, tools, skills, and routing rules. Because these contents jointly shape later execution, a harness update can disrupt previously reliable behavior even when the model is frozen. This raises a new question: how can an agent continually improve its state outside the model while retaining behavior acquired earlier? We formulate Harness Continual Learning (HCL), a new continual learning paradigm in which the harness evolves around a frozen foundation model, and define the resulting loss of earlier behavior as harness-level forgetting. We instantiate HCL with four execution-facing components: the Task Interface, Experience Memory, Capability Map, and Adaptive Router. We further introduce guarded harness evolution to separate update generation from state commitment. A Continual Optimizer proposes candidate harnesses from post-execution feedback, and a Continual Evaluator commits the resulting candidate harness only after checking current improvement, historical retention, and validity. Experiments on textual reasoning, multimodal perception, and open-world interaction demonstrate capability accumulation and failure recovery, with relative gains exceeding 10% over corresponding baselines in multiple settings. Component ablations assess the contribution of each harness component, while controlled retention sweeps reveal measurable harness-level forgetting and show that the stability--plasticity trade-off can be explicitly adjusted.
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