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

• URL: http://arxiv.org/abs/2602.01146v1
• Date: Sun, 01 Feb 2026 10:44:58 GMT
• Title: PersistBench: When Should Long-Term Memories Be Forgotten by LLMs?
• Abstract summary: We introduce PersistBench to measure the extent of long-term memory-specific safety risks.<n>We identify two long-term memory-specific risks: cross-domain leakage and memory-induced sycophancy.<n>Our benchmark encourages the development of more robust and safer long-term memory usage in frontier conversational systems.
• Score: 4.657424818207681
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Insecure Coding Preferences in Long-Term Memory: Security Risks for LLM-based Code Generation [32.05457233792888]
  This study is the first systematic empirical study on the impact of insecure coding preferences stored in long-term memory on the security of LLM-based code generation.<n>We evaluate four LLMs across five programming languages (Python, C, C++, Go, and JavaScript)
  arXiv  Detail & Related papers  (2026-07-20T07:15:35Z)
• MemRefine: LLM-Guided Compression for Long-Term Agent Memory [67.62350557461352]
  Large language model (LLM) agents are increasingly expected to operate over long-term interactions.<n>As interactions accumulate, the memory store grows without bound and fills with redundant entries that inflate storage cost and degrade retrieval by crowding out the most useful evidence.<n>This is especially limiting on resource-constrained platforms with hard memory budgets.<n>We propose storage-budgeted memory management, the task of keeping an already constructed memory store within a fixed budget while preserving information useful for future interactions.
  arXiv  Detail & Related papers  (2026-06-11T10:46:17Z)
• Personalize-then-Store: Benchmarking and Learning Personalized Memory for Long-horizon Agents [20.22872890297194]
  Existing large language model (LLM) based memory systems apply universal, static policies that overlook a fundamental reality: the contexts that are worth storing in memory are different across users.<n>We introduce PerMemBench, the first benchmark for evaluating personalized memory systems.<n>We propose session level storage gating, a lightweight framework that selectively bypasses memory operations for transient sessions.
  arXiv  Detail & Related papers  (2026-05-25T07:48:33Z)
• Causal Intervention-Based Memory Selection for Long-Horizon LLM Agents [0.33842793760651557]
  We propose a causal memory-selection technique that estimates how candidate memories affect the model's answer under controlled interventions.<n>Results show that CMI achieves a stronger balance between answer quality and robustness to misleading memory, suggesting that reliable long-term memory requires selecting context based on causal usefulness rather than relevance alone.
  arXiv  Detail & Related papers  (2026-05-17T20:21:55Z)
• From Recall to Forgetting: Benchmarking Long-Term Memory for Personalized Agents [38.52713500119118]
  We introduce Memora, a long-term memory benchmark spanning weeks to months long user conversations.<n>The benchmark evaluates three memory-grounded tasks: remembering, reasoning, and recommending.<n>Forgetting-Aware Memory Accuracy (FAMA) is a metric that penalizes reliance on obsolete or invalidated memory.
  arXiv  Detail & Related papers  (2026-04-21T21:31:01Z)
• MemEvoBench: Benchmarking Memory MisEvolution in LLM Agents [78.95081012334116]
  Large Language Models (LLMs) with persistent memory enhances interaction continuity and personalization but introduces new safety risks.<n> contaminated or biased memory accumulation can trigger abnormal agent behaviors.<n>MemeEvoBench is the first benchmark evaluating memory safety in LLM agents against adversarial memory injection, noisy tool outputs, and biased feedback.
  arXiv  Detail & Related papers  (2026-04-17T07:29:52Z)
• AdaMem: Adaptive User-Centric Memory for Long-Horizon Dialogue Agents [49.63422082885992]
  We propose AdaMem, an adaptive user-centric memory framework for long-horizon dialogue agents.<n>AdaMem organizes dialogue history into working, episodic, persona, and graph memories.<n>We evaluate AdaMem on the LoCoMo and PERSONAMEM benchmarks for long-horizon reasoning and user modeling.
  arXiv  Detail & Related papers  (2026-03-17T13:22:54Z)
• EvolMem: A Cognitive-Driven Benchmark for Multi-Session Dialogue Memory [63.84216832544323]
  EvolMem is a new benchmark for assessing multi-session memory capabilities of large language models (LLMs) and agent systems.<n>To construct the benchmark, we introduce a hybrid data synthesis framework that consists of topic-initiated generation and narrative-inspired transformations.<n>Extensive evaluation reveals that no LLM consistently outperforms others across all memory dimensions.
  arXiv  Detail & Related papers  (2026-01-07T03:14:42Z)
• Agentic Memory: Learning Unified Long-Term and Short-Term Memory Management for Large Language Model Agents [57.38404718635204]
  Large language model (LLM) agents face fundamental limitations in long-horizon reasoning due to finite context windows.<n>Existing methods typically handle long-term memory (LTM) and short-term memory (STM) as separate components.<n>We propose Agentic Memory (AgeMem), a unified framework that integrates LTM and STM management directly into the agent's policy.
  arXiv  Detail & Related papers  (2026-01-05T08:24:16Z)
• Evaluating Long-Term Memory for Long-Context Question Answering [100.1267054069757]
  We present a systematic evaluation of memory-augmented methods using LoCoMo, a benchmark of synthetic long-context dialogues annotated for question-answering tasks.<n>Our findings show that memory-augmented approaches reduce token usage by over 90% while maintaining competitive accuracy.
  arXiv  Detail & Related papers  (2025-10-27T18:03:50Z)
• Mnemosyne: An Unsupervised, Human-Inspired Long-Term Memory Architecture for Edge-Based LLMs [1.2582867366903179]
  We introduce Mnemosyne, an unsupervised, human-inspired long-term memory architecture for edge-based large language models (LLM)<n>Our approach uses graph-structured storage, modular substance and redundancy filters, memory committing and pruning mechanisms, and probabilistic recall with temporal decay and refresh processes modeled after human memory.<n>Mnemosyne is designed for use in longitudinal healthcare assistants, where repetitive and semantically similar but temporally distinct conversations are limited by naive retrieval.
  arXiv  Detail & Related papers  (2025-10-07T03:32:53Z)
• LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory [68.97819665784442]
  We introduce LongMemEval, a benchmark designed to evaluate five core long-term memory abilities of chat assistants.<n>LongMemEval presents a significant challenge to existing long-term memory systems.<n>We present a unified framework that breaks down the long-term memory design into three stages: indexing, retrieval, and reading.
  arXiv  Detail & Related papers  (2024-10-14T17:59:44Z)
• Assessing Episodic Memory in LLMs with Sequence Order Recall Tasks [42.22616978679253]
  We introduce Sequence Order Recall Tasks (SORT), which we adapt from tasks used to study episodic memory in cognitive psychology. SORT requires LLMs to recall the correct order of text segments, and provides a general framework that is both easily extendable and does not require any additional annotations. Based on a human experiment with 155 participants, we show that humans can recall sequence order based on long-term memory of a book.
  arXiv  Detail & Related papers  (2024-10-10T17:17:38Z)
• Towards Lifelong Dialogue Agents via Timeline-based Memory Management [26.95907827895548]
  We present THEANINE, a framework for lifelong dialogue agents.<n> THEANINE discards memory removal and manages large-scale memories by linking them based on their temporal and cause-effect relation.<n>Along with THEANINE, we introduce TeaFarm, a counterfactual-driven evaluation scheme.
  arXiv  Detail & Related papers  (2024-06-16T16:17:46Z)
• Recursively Summarizing Enables Long-Term Dialogue Memory in Large Language Models [30.48902594738911]
  Given a long conversation, large language models (LLMs) fail to recall past information and tend to generate inconsistent responses.<n>We propose to generate summaries/ memory using large language models (LLMs) to enhance long-term memory ability.
  arXiv  Detail & Related papers  (2023-08-29T04:59:53Z)
• Augmenting Language Models with Long-Term Memory [142.04940250657637]
  Existing large language models (LLMs) can only afford fix-sized inputs due to the input length limit. We propose a framework, Language Models Augmented with Long-Term Memory (LongMem), which enables LLMs to memorize long history.
  arXiv  Detail & Related papers  (2023-06-12T15:13:39Z)
• SCM: Enhancing Large Language Model with Self-Controlled Memory Framework [54.33686574304374]
  Large Language Models (LLMs) are constrained by their inability to process lengthy inputs, resulting in the loss of critical historical information.<n>We propose the Self-Controlled Memory (SCM) framework to enhance the ability of LLMs to maintain long-term memory and recall relevant information.
  arXiv  Detail & Related papers  (2023-04-26T07:25:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.