Tag
Proposes MemDefrag, a training-free framework that uses a middle-layer tracing signal to defragment latent memory in LLMs, achieving significantly better knowledge retention than existing methods like MemoryLLM and M+.
LaMem-VLA proposes a latent-memory-native framework that integrates short-term and long-term historical experience directly into Vision-Language-Action reasoning, enabling better performance on long-horizon robotic manipulation tasks.
EvoEmbedding is a dynamic embedding model that maintains a continuously updated latent memory to generate adaptive representations for long-context retrieval, outperforming larger specialist models and improving agentic workflows.
Latent Memory introduces a compressed representation approach for external memory in question answering, reducing token consumption and storage requirements while maintaining competitive performance across text-only and multimodal benchmarks.
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.