Retrieval-Driven Memory Reconsolidation for Long-Term LLM Agents
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 cognitive neuroscience, we propose \textbf{REALM}, a \textbf{r}econsolidation-\textbf{e}volution \...
| Authors | Yuanyi Song, Yukai Wang, Xinbei Ma et al. |
|---|---|
| Primary category | cs.CL |
| Published | 13 September 2026 |
| Last updated at source | 13 September 2026 |
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