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    <abstract>We present PEAM, a Parametric Embodied Agent Memory framework in Minecraft that transforms agent memory from inference-time retrieval into parameter-resident skills internalized through experience. PEAM pairs a slow deliberative LLM for open-ended reasoning with a fast parametric module for reflexive execution of consolidated skills. The fast module is a multimodal Mixture-of-Experts LoRA architecture with per-category physically isolated adapters, enabling parameter-level continual learning with structural resistance to cross- category forgetting. We treat failure as a first-class training signal: failure–correction trajectory pairs are internalized through a joint behavioral-cloning and contrastive objective, so the agent learns not only what succeeds but also how corrected actions differ from failed ones. To govern consolidation, PEAM introduces a parameterization-worthiness score for deciding which experience should be internalized, and a scale-free self-triggered consolidation mechanism for deciding when to internalize without task-specific hand-tuned thresholds, making the agent self-evolving as the trigger transfers across task distributions without re-tuning. Experiments in Minecraft show that PEAM improves long-horizon task performance, mitigates forgetting on previously consolidated skills, and improves parametric-versus-retrieval efficiency over retrieval-based embodied agents and parametric memory variants.</abstract>
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%0 Conference Proceedings
%T PEAM: Parametric Embodied Agent Memory for Continual Skill Learning via Internalization of Experience in Minecraft
%A Guo, Yuchen
%A Gong, Junli
%A Wang, Weicheng
%A Cai, Hongmin
%A Cheung, Yiu-ming
%A Su, Weifeng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F guo-etal-2026-peam
%X We present PEAM, a Parametric Embodied Agent Memory framework in Minecraft that transforms agent memory from inference-time retrieval into parameter-resident skills internalized through experience. PEAM pairs a slow deliberative LLM for open-ended reasoning with a fast parametric module for reflexive execution of consolidated skills. The fast module is a multimodal Mixture-of-Experts LoRA architecture with per-category physically isolated adapters, enabling parameter-level continual learning with structural resistance to cross- category forgetting. We treat failure as a first-class training signal: failure–correction trajectory pairs are internalized through a joint behavioral-cloning and contrastive objective, so the agent learns not only what succeeds but also how corrected actions differ from failed ones. To govern consolidation, PEAM introduces a parameterization-worthiness score for deciding which experience should be internalized, and a scale-free self-triggered consolidation mechanism for deciding when to internalize without task-specific hand-tuned thresholds, making the agent self-evolving as the trigger transfers across task distributions without re-tuning. Experiments in Minecraft show that PEAM improves long-horizon task performance, mitigates forgetting on previously consolidated skills, and improves parametric-versus-retrieval efficiency over retrieval-based embodied agents and parametric memory variants.
%U https://aclanthology.org/2026.findings-emnlp.1326/
%P 28111-28124

Markdown (Informal)

[PEAM: Parametric Embodied Agent Memory for Continual Skill Learning via Internalization of Experience in Minecraft](https://aclanthology.org/2026.findings-emnlp.1326/) (Guo et al., Findings 2026)

• PEAM: Parametric Embodied Agent Memory for Continual Skill Learning via Internalization of Experience in Minecraft (Guo et al., Findings 2026)

ACL

• Yuchen Guo, Junli Gong, Weicheng Wang, Hongmin Cai, Yiu-ming Cheung, and Weifeng Su. 2026. PEAM: Parametric Embodied Agent Memory for Continual Skill Learning via Internalization of Experience in Minecraft. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 28111–28124, Budapest, Hungary. Association for Computational Linguistics.