# MemConflict: Evaluating Long-Term Memory Systems Under Memory Conflicts

arXiv: https://arxiv.org/abs/2605.20926 · HTML: https://mlsanity.com/paper/2605.20926/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2026.

## Built on by (most cited first, 12 of 12)

- [When Memory Updates but Behavior Does Not: Repairing Implicit Stale Dependencies in Personalized Agent Responses](https://mlsanity.com/paper/2608.01619/) (2026)
- [Engineering Reliable Coding Agents: Evaluating and Operating the System Around the Model](https://mlsanity.com/paper/2608.13867/) (2026)
- [When Personal Memory Has No Single Answer: Evaluating LLM Agents under Irreducible Conflict](https://mlsanity.com/paper/2608.13921/) (2026)
- [REDDIT: Forgetting-Resistant Correction of Timestamp Drift in ASR via Replay-Based Distribution Editing](https://mlsanity.com/paper/2607.05364/) (2026)
- [Hierarchical Graph Memory for LLM Agents with Path-level Localization and Rewrite](https://mlsanity.com/paper/2608.05095/) (2026)
- [Governed Persistent Memory: Source-Bound State Semantics and Fail-Closed Release for Long-Horizon Agents](https://mlsanity.com/paper/2608.12476/) (2026)
- [FinLifeBench: Exhaustive Life-Event History and Financial-State Reconstruction from Longitudinal Banking Dialogue](https://mlsanity.com/paper/2609.01198/) (2026)
- [Probing Stability-Plasticity Tradeoffs in Agent Memory through Cognitive Experimental Paradigms](https://mlsanity.com/paper/2609.30558/) (2026)
- [EMIR2: Evolution-Aware Memory with Intent-Guided Multi-Round Retrieval](https://mlsanity.com/paper/2609.32584/) (2026)
- [PACMI: Provenance-Aware Cascading Memory Invalidation for Long-Term LLM Agents](https://mlsanity.com/paper/2610.05732/) (2026)
- [The Right Memory in the Wrong Context: Verifying Retrieval Admissibility in Long-Term Agent Memory](https://mlsanity.com/paper/2610.07309/) (2026)
- [Stale, Misattributed, or Late: Where Personal Memory Fails Before Generation](https://mlsanity.com/paper/2610.10265/) (2026)

## Abstract (arXiv)

Long-term memory systems enable conversational agents based on large language models (LLMs) to retain, retrieve, and apply user-specific information across multi-session interactions. However, existing evaluations mainly assess outcome-level performance or temporal updating, providing limited insight into how systems retrieve and rank temporally valid, factually correct, and contextually applicable memory evidence under conflicting alternatives. To address this gap, we propose MemConflict, a diagnostic framework that treats memory validity as a query-conditioned fitness-for-use problem. MemConflict formalizes dynamic, static, and conditional conflicts over temporal validity, factual correctness, and contextual applicability. It simulates controlled long-horizon histories from structured user profiles, introduces cross-session conflicts, and injects semantically similar distractors to create competition among memory candidates. The resulting multi-session dialogue benchmark supports black-box evaluation of final answers and white-box analysis of supporting-memory retrieval and ranking. Experiments on six representative long-term memory systems show uneven strengths across conflict types, with answer correctness often diverging from memory retrieval and ranking. Sensitivity analyses reveal that longer histories, distractors, implicit queries, and larger conflict distances degrade performance. Diagnostics show failures from missing supporting memories and ineffective use of retrieved memories. Collectively, MemConflict advances principled long-term memory governance through retrieval-aware, conflict-aware reliability assessment.