latent-memory

ElasticMem introduces a learnable latent memory mechanism for LLM agents that adaptively allocates variable budgets to retrieved memories, improving performance on memory-intensive QA and embodied agent tasks while reducing token costs.

This paper introduces JAMEL, a framework that jointly trains agentic memory and exploration policies using novelty signals, enabling efficient exploration in open-ended environments with reduced computational costs.

This paper formulates context distillation as a latent memory management problem, proposing a framework that stores distilled contexts as independent LoRA adapters with retrieval, routing, and self-gating to improve robustness and efficiency.