# PersistBench: When Should Long-Term Memories Be Forgotten by LLMs?

arXiv: https://arxiv.org/abs/2602.01146 · HTML: https://mlsanity.com/paper/2602.01146/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2026.

## Built on by (most cited first, 21 of 21)

- [A Survey on Long-Term Memory Security in LLM Agents: Attacks, Defenses, and Governance Across the Memory Lifecycle](https://mlsanity.com/paper/2604.16548/) (2026)
- [Hidden in Memory: Sleeper Memory Poisoning in LLM Agents](https://mlsanity.com/paper/2605.15338/) (2026)
- [GateMem: Benchmarking Memory Governance in Multi-Principal Shared-Memory Agents](https://mlsanity.com/paper/2606.18829/) (2026)
- [Beyond Similarity: Trustworthy Memory Search for Personal AI Agents](https://mlsanity.com/paper/2606.06054/) (2026)
- [MemSyco-Bench: Benchmarking Sycophancy in Agent Memory](https://mlsanity.com/paper/2607.01071/) (2026)
- [User as Code: Executable Memory for Personalized Agents](https://mlsanity.com/paper/2606.16707/) (2026)
- [Memory-Induced Tool-Drift in LLM Agents](https://mlsanity.com/paper/2605.24941/) (2026)
- [Capable but Careless: Do Computer-Use Agents Follow Contextual Integrity?](https://mlsanity.com/paper/2606.23189/) (2026)
- [Mitigating Over-Personalization in LLMs via Structured Memory](https://mlsanity.com/paper/2608.08300/) (2026)
- [PiSAs: Benchmarking Contextual Integrity in Multi-User Agentic Systems](https://mlsanity.com/paper/2607.05318/) (2026)
- [When Personal Memory Has No Single Answer: Evaluating LLM Agents under Irreducible Conflict](https://mlsanity.com/paper/2608.13921/) (2026)
- [MemTrapBench: Benchmarking Cognitive Traps in LLM Memory Use](https://mlsanity.com/paper/2608.20202/) (2026)
- [From Performance to Purpose: A Sociotechnical Taxonomy for Evaluating Large Language Model Utility](https://mlsanity.com/paper/2602.20513/) (2026)
- [Agents Don't Just Agree, They Remember: Benchmarking Persistent Sycophancy in Self-Improving Personal Agents](https://mlsanity.com/paper/2607.10526/) (2026)
- [TWIST: A Proposed Benchmark for Intervention Quality in Conversational Memory, with a Human-Validated Draft-Alignment](https://mlsanity.com/paper/2609.28575/) (2026)
- [PairPref: When Should Memory Guide the Answer? A Benchmark for Contextual Preference Use](https://mlsanity.com/paper/2609.34526/) (2026)
- [MemLeak: Cross-User Semantic Leakage in Multi-Tenant AI Agent Memory](https://mlsanity.com/paper/2610.04195/) (2026)
- [Memadapter: Counterfactual Adaptation Against Memory-induced Sycophancy](https://mlsanity.com/paper/2610.05162/) (2026)
- [Understanding and Mitigating Inference-Time Overreliance Using Agentic Memory](https://mlsanity.com/paper/2610.07311/) (2026)
- [Defense-in-Depth for LLMs: Evaluating Memory Gates Against Activation-Induced and Memory-Induced Sycophancy](https://mlsanity.com/paper/2610.07403/) (2026)
- [From Acoustic Sensing to Physical Actions: A Comprehensive Survey of Security Risks in Edge-Deployed Audio LLM-Based Agents](https://mlsanity.com/paper/2610.15390/) (2026)

## Abstract (arXiv)

Conversational assistants are increasingly integrating long-term memory with large language models (LLMs). This persistence of memories, e.g., the user is vegetarian, can enhance personalization in future conversations. However, the same persistence can also introduce safety risks that have been largely overlooked. Hence, we introduce PersistBench to measure the extent of these safety risks. We identify two long-term memory-specific risks: cross-domain leakage, where LLMs inappropriately inject context from the long-term memories; and memory-induced sycophancy, where stored long-term memories insidiously reinforce user biases. We evaluate 18 frontier and open-source LLMs on our benchmark. Our results reveal a surprisingly high failure rate across these LLMs - a median failure rate of 53% on cross-domain samples and 97% on sycophancy samples. To address this, our benchmark encourages the development of more robust and safer long-term memory usage in frontier conversational systems.