Disentangling Long-Term Memory via Latent Neuro-Symbolic Reasoning

The paper introduces LGM, a neuro‑symbolic framework that disentangles long‑term memory by mapping historical interactions into a continuous latent graph. Instead of static memory graphs, LGM uses a sparse autoencoder to create query‑aware latent nodes and edges, then applies a graph encoder conditioned on the query to perform non‑linear message passing. Experiments on long‑term personalization benchmarks show that LGM outperforms existing methods in capturing both explicit and implicit user preferences and generating personalized responses.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• rag
• agents
• diffusion
• benchmarks