SOURCE-LINKED INTELLIGENCE

MemoryLACE: Memory Lifecycle-Aware Consolidation and Evidence Retrieval

arXiv · AI, language, vision and robotics · article · Sep 2, 2026 · UTC

Long-term LLM agents must preserve information across interactions while distinguishing repeated evidence, historical states, updates, and unresolved contradictions. Existing textual memory systems retrieve semantically relevant memories efficiently but often leave these relationships implicit, whereas richer structured approaches model them through global graphs, hierarchical abstractions, or reflection at greater complexity. We introduce MemoryLACE (MemLACE), a lightweight memory framework that explicitly models the lifecycle of textual evidence through sparse merge, supersession, and contra

Read original source ↗ Open in workspace

recordType

paper

region

Global