MIRAGE: Memory Revision and Path-Aware Retrieval for Conflict Resolution in LLM Agent Memory

LLM agent memory systems increasingly rely on non-parametric state to reuse experience across tasks. However, memory maintenance remains under-specified: append-only storage can leave stale evidence, conflicting facts, and disconnected retrieval snippets in the active context. We study memory-state maintenance as an alternative to append-only context construction: deciding what evidence should remain active, what should be suppressed, and what structure is needed for multi-hop reuse when memory updates follow an explicit authority signal. We propose MIRAGE, a memory-maintenance framework that maintains agent memory through ordered revision, provenance marking, and path-aware retrieval. In this paper, we evaluate three concrete MIRAGE paths on MemoryAgentBench FactConsolidation: ordered conflict revision (MIRAGE-R), provenance-marked revision (MIRAGE-P), and graph/path-aware retrieval (MIRAGE-G). On the 6k multi-hop setting, MIRAGE-R improves accuracy on 100 questions from 0.28 to 0.68 while reducing token use by 32.8%; on the 32k multi-hop setting, accuracy improves from 0.21 to 0.65 with a 34.5% token reduction. On the same 6k multi-hop setting, MIRAGE-P maintains comparable accuracy at 0.71 while reducing empty outputs, and MIRAGE-G raises retrieval-based accuracy from 0.17 for lexical top-$k$ retrieval to 0.59 while preserving an 80.1% token reduction. Our findings suggest that MIRAGE provides a practical memory-maintenance layer for long-context agents, especially in settings where persistent state must be revised, audited, and retrieved through multi-hop evidence rather than simply accumulated.