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Source note3 min read

Harness Continual Learning: Continual Adaptation Beyond Model Parameters

Source

arXiv

arxiv.org/abs/2608.19013v1

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Source

  • Source name: arXiv
  • Source kind: paper
  • Authors / organization: Borui Kang, Jinrui Gu, Junhan Lv, Wenbin Li, Lei Wang, Yang Gao
  • Venue: arXiv: cs.LG
  • Published: 2026-08-19
  • Collected: 2026-08-20
  • URL: http://arxiv.org/abs/2608.19013v1
  • PDF: https://arxiv.org/pdf/2608.19013v1
  • DOI: UNKNOWN
  • Registry group: academic_apis_and_preprints
  • Evidence role: canonical_academic_index
  • Language: en
  • Country / region: UNKNOWN

Plain-Language Brief

The paper 'Harness Continual Learning: Continual Adaptation Beyond Model Parameters' introduces a new approach to continual learning, focusing on the evolution of an agent's harness (which includes prompts, memories, tools, and routing rules) around a frozen foundation model. The goal is for the agent to continually improve its state while retaining previously acquired behavior. The authors propose four components for this paradigm: Task Interface, Experience Memory, Capability Map, and Adaptive Router. They also present guarded harness evolution and a Continual Optimizer and Evaluator to manage the proposed system.

What the Document Says

  • The paper presents a new continual learning paradigm called Harness Continual Learning (HCL).
  • In this approach, an agent's harness evolves around a frozen foundation model.
  • The harness includes prompts, memories, tools, and routing rules that jointly shape later execution.
  • HCL aims to allow an agent to continually improve its state while retaining behavior acquired earlier.
  • The paper introduces four components: Task Interface, Experience Memory, Capability Map, and Adaptive Router.
  • Guarded harness evolution is presented to separate update generation from state commitment.
  • A Continual Optimizer proposes candidate harnesses from post-execution feedback.
  • A Continual Evaluator commits the resulting candidate harness after checking current improvement, historical retention, and validity.
  • Experiments show capability accumulation and failure recovery in textual reasoning, multimodal perception, and open-world interaction.
  • Relative gains exceed 10% over corresponding baselines in multiple settings.
  • Component ablations assess the contribution of each harness component.
  • Controlled retention sweeps reveal measurable harness-level forgetting and allow for explicit adjustment of the stability--plasticity trade-off.

Key Concepts Mentioned

  • Harness Continual Learning
  • continual learning
  • model parameters
  • prompts
  • memories
  • tools
  • skills
  • routing rules
  • Task Interface
  • Experience Memory
  • Capability Map
  • Adaptive Router
  • guarded harness evolution
  • Continual Optimizer
  • Continual Evaluator
  • textual reasoning
  • multimodal perception
  • open-world interaction
  • stability--plasticity trade-off

Claims Made by the Document

  • HCL is a new continual learning paradigm.
  • HCL aims to allow an agent to continually improve its state while retaining behavior acquired earlier.
  • Experiments demonstrate capability accumulation and failure recovery.

Evidence Used

  • The paper describes the formulation of Harness Continual Learning (HCL).
  • The paper presents four execution-facing components for HCL.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.