PEAM: Parametric Embodied Agent Memory through Contrastive Internalization of Experience in Minecraft
arXiv:2605.27762v1 Announce Type: new 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
The continuous advancements in LLM architectures and reinforcement learning provide the necessary technical foundation for developing more sophisticated agent memory systems.
This development represents a significant step towards more autonomous and capable AI agents that can learn and adapt continuously without catastrophic forgetting, enhancing their real-world applicability.
Agent memory is transitioning from primarily retrieval-based to more parameter-resident, internalized skills, leading to faster, more robust, and continually learning AI systems.
- · AI agents developers
- · Open-world simulation platforms
- · Robotics
- · Gaming
- · Simple retrieval-based AI architectures
- · Legacy AI middleware
AI agents will exhibit improved long-term performance and adaptability in complex environments like Minecraft.
The ability to internalize skills continuously without forgetting will accelerate the development of general-purpose AI and personal AI assistants.
More capable and autonomous AI agents could increasingly take over complex, multi-step tasks currently requiring human oversight.
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Read at arXiv cs.AI