SOURCE-LINKED INTELLIGENCE

Retrieval-Driven Memory Reconsolidation for Long-Term LLM Agents

arXiv · AI, language, vision and robotics · article · Sep 13, 2026 · UTC

Long-term memory is essential for LLM-based agents operating over extended interactions. Existing memory systems primarily update memory when new information arrives, treating retrieval as the endpoint of memory access rather than a driver of memory evolution. Consequently, retrieval feedback is rarely exploited to reorganize memory for future access continuously. Moreover, most existing approaches rely on predefined memory structures together with fixed retrieval pipelines, limiting the agent's ability to organize and evolve its own memory autonomously. Inspired by memory reconsolidation in c

Read original source ↗ Open in workspace

recordType

paper

region

Global