GRAVITY: Architecture-Agnostic Structured Anchoring for Long-Horizon Conversational Memory

Abstract:Long-horizon memory systems increasingly improve how evidence is stored and retrieved, yet the generator must still reason over fragments whose cross-session relationships are implicit. We study generation-time memory organization as a distinct design dimension and introduce GRAVITY (Generation-time Relational Anchoring Via Injected Topological MemorY), a host-independent auxiliary memory layer. GRAVITY consolidates raw dialogue into entity profiles, temporal event traces, and cross-session topic summaries, then retrieves and injects query-relevant records through the prompt interface. Across five heterogeneous memory systems on LongMemEval and LoCoMo, it improves every host--benchmark baseline under two distinct LLM configurations. Controlled analyses separate gains from organizing already available evidence and from consolidating information across the full history. Under a matched LightMem pipeline, the entity--event--topic representation reaches 83.9% on LoCoMo, 3.6% above the strongest of six alternative auxiliary representations. These results show that generation-time structure is a portable complement to existing memory retrieval, while its interaction with host evidence depends on the benchmark and host.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2605.01688 [cs.CL]

(or arXiv:2605.01688v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.01688 Focus to learn more arXiv-issued DOI via DataCite

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