Memory Consolidation Flattens the Temporal Shape of User Facts
Not on the map. Why?
Abstract (arXiv)
Long-term memory systems turn conversations into short stored notes. A note can keep a user fact while losing evidence about whether the fact still holds. For example, "I am driving a Peugeot" can become "The user drives a Peugeot," which drops the cue that the activity is ongoing. We call this aspectual flattening and measure it with LAPSE, a benchmark of matched user statements that differ only in temporal form. We find that memory writers flatten aspect selectively. Three writer models flattened the progressive statement but kept its simple-present match in 244 of 381 pairs, never the reverse. The asymmetry holds in all 11 model configurations tested and in the installed pipelines mem0, Graphiti, and Letta. The lost cue matters to later readers. In exploratory tests, changing only the stored verb shifted all three readers' estimates that a fact still holds. When readers could ask the user before acting, two of three acted without asking more often on flattened notes. Our planned memory-use task could not detect this, because readers there acted on almost every stored fact, even expired ones. Memory writing can thus remove evidence that later models use to decide whether to act.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 22 of its 31 references that are in the map. First posted on arXiv 29 Sep 2026.
Builds on
22 of 31 references.
References · 22
- 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
- 2023MemGPT: Towards LLMs as Operating Systemscited by 1,389
- 2025A-MEM: Agentic Memory for LLM Agentscited by 1,063
- 2024Evaluating Very Long-Term Conversational Memory of LLM Agentscited by 830
- 2025Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memorycited by 779
- 2024LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memorycited by 707
- 2024WildChat: 1M ChatGPT Interaction Logs in the Wildcited by 629
- 2025Zep: A Temporal Knowledge Graph Architecture for Agent Memorycited by 400
- 2024Towards Lifelong Dialogue Agents via Timeline-based Memory Managementcited by 51
- 2026STALE: Can LLM Agents Know When Their Memories Are No Longer Valid?cited by 50
- 2026From Recall to Forgetting: Benchmarking Long-Term Memory for Personalized Agentscited by 36
- 2026Beyond Dialogue Time: Temporal Semantic Memory for Personalized LLM Agentscited by 14
10 more
- 2026Temporal Validity in Retrieval Memory: Eliminating Stale-Fact Errors for AI Agents over Evolving Knowledgecited by 14
- 2026When Memory Updates but Behavior Does Not: Repairing Implicit Stale Dependencies in Personalized Agent Responsescited by 7
- 2026Same Ranking, Different Winner: How Scoring Targets Shape LLM Memory Benchmarkscited by 5
- 2025Chronocept: Instilling a Sense of Time in Machinescited by 3
- 2026Manufactured Confidence: How Memory Consolidation Turns Hearsay into Confident Factscited by 3
- 2026Fortunate Recall: Ontology-Driven Memory Lifecycle Management for Persistent Coherence in LLMscited by 3
- 2024Temporal Validity Change Predictioncited by 2
- 2026Fixed RAG Compression Collapses Measured Reader Scalingcited by 1
- 2026When Personal Memory Has No Single Answer: Evaluating LLM Agents under Irreducible Conflictcited by 1
- 2026MemoryLACE: Memory Lifecycle-Aware Consolidation and Evidence Retrievalcited by 1
Built on by
Citing papers per year: 2026: 1.
1 paper.