| title | PEAM: Parametric Embodied Agent Memory through Contrastive Internalization of Experience in Minecraft |
|---|---|
| authors | Yuchen Guo, Junli Gong, Hongmin Cai, Yiu-ming Cheung, Weifeng Su |
| date | 2026-05-26 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.27762 |
| upvotes | 5 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| primer | Memory for AI agents |
| 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 without catastrophic 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. |
cite
@misc{guo2026peam,
title={PEAM: Parametric Embodied Agent Memory through Contrastive Internalization of Experience in Minecraft},
author={Yuchen Guo and Junli Gong and Hongmin Cai and Yiu-ming Cheung and Weifeng Su},
year={2026},
eprint={2605.27762},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.27762}
}