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New HaMo framework enhances AI agent learning with experience cards

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced HaMo, a novel framework designed for long-horizon continual learning in AI agents. This collaborative approach combines rapid, non-parametric experience accumulation with slower, parametric adaptation. HaMo distills agent trajectories into reusable "experience cards" for immediate retrieval, while simultaneously training a model to interpret and execute these strategies across evolving contexts. Experiments demonstrate that HaMo surpasses existing harness-only and model-only methods in both within-domain and cross-domain scenarios, enhancing both knowledge retention and adaptation capabilities. AI

IMPACT This framework could improve the ability of AI agents to learn and adapt over long periods, particularly in complex, evolving environments like the web.

RANK_REASON The cluster contains a research paper detailing a new AI framework. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New HaMo framework enhances AI agent learning with experience cards

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The cluster contains a research paper detailing a new AI framework. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Zhen Bi, Zhizhi Peng, Yan Wang, Xueshu Chen, Haofei Zhang, Yaobo Zhang, Zeyu Yang, Qing Shen, Jungang Lou ·

    Harness-Model Collaboration for Long-Horizon Continual Learning

    arXiv:2610.34724v1 Announce Type: new Abstract: Long-horizon agents continuously encounter reusable procedural knowledge, yet existing systems typically rely either on model adaptation or external memory, trading persistent learning for rapid reuse. This limitation is especially …

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