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

Sidharth Pulipaka, Oliver Chen, Manas Sharma, Taaha S Bajwa, Vyas Raina, Ivaxi Sheth

arXiv:2602.01146v2cs.AI

TL;DR

Long-term memory creates an open safety question because persistent user information can be reused in irrelevant or bias-reinforcing contexts. PersistBench evaluates these risks with memory–query samples, controls, and multiple LLM evaluators, finding widespread failures and motivating selective memory control.

  • Problem

    Long-term memory creates an open safety question because persistent user information can be reused in irrelevant or bias-reinforcing contexts.

  • Method

    PersistBench evaluates cross-domain leakage and memory-induced sycophancy using memory–query samples, beneficial-memory controls, human validation, and multiple LLM evaluators.

  • Results

    53% median failure occurred on cross-domain leakage and 97.8% on sycophancy, versus 16.5% on beneficial memory.

  • Takeaways & Limitations

    Strong beneficial-memory performance does not reliably predict robustness to harmful memory influence, leaving selective memory control as an open challenge.

  • Takeaways & Limitations

    The benchmark uses synthetic memory–query pairs that may not capture the chaotic, ambiguous, temporally disjoint, higher-entropy nature of organic long-term histories.

Abstract

from arXiv · show

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.

1. Introduction

Long-term memory supports personalization across conversations but creates two safety risks: irrelevant memories can leak across domains, and stored beliefs can induce sycophantic responses. PersistBench evaluates these risks alongside beneficial memory use across 18 LLMs, finding high failure rates.

  • Motivation: Conversational assistants retain user-specific information across sessions to support personalization and continuity.Examples include storing that a user is vegetarian for later recipe recommendations.
  • Motivation: Long-term memory can exacerbate existing alignment problems when irrelevant memories enter new tasks or reinforce user biases.These risks extend prior concerns about context sensitivity and sycophancy without memory.
  • Benchmark: PersistBench evaluates cross-domain leakage and memory-induced sycophancy as risks caused by persistent user memories.It also distinguishes these risks from privacy-centric failures and short-context risks studied previously.
  • Benchmark: PersistBench includes human-validated memory–query samples for harmful behaviors and a beneficial-memory set to test safety–utility trade-offs.The benchmark evaluates 18 frontier and open-weight LLMs.

2. Related Work

Prior memory benchmarks mainly measure recall, summarization, or long-term question answering, while related safety work emphasizes privacy or short-context effects. PersistBench instead targets response distortion from irrelevant memory injection and memory-induced sycophancy in cross-session interactions.

  • Memory benchmarks: Existing memory benchmarks primarily evaluate memory generation through downstream tasks such as event summarization and long-term question answering.These benchmarks include LoCoMo and LongMemEval.
  • Context leakage: Related context-leakage research studies privacy violations and quality degradation from irrelevant text or task switches.This work generally does not center on persistent user-specific memory across sessions.
  • Novelty: PersistBench evaluates response-level distortion from minor irrelevant recall to visibly derailed outputs in direct user–assistant interactions.It addresses context-mismatched memory injection, which prior cited works do not account for.
  • Sycophancy: Long-term memory can distill extended interaction histories into reusable user profiles that may produce sycophantic responses.Existing memory benchmarks focus on personalization rather than this memory-induced failure mode.

3. PersistBench Setup

PersistBench models a conversational assistant that stores textual user information across sessions, combines that memory with a new query, and evaluates whether memory is used appropriately. Its setup defines cross-domain leakage and memory-induced sycophancy while retaining beneficial memory use as a control.

  • Scope: PersistBench targets inappropriate memory usage when stored user information is irrelevant, biased, or harmful.It focuses on cross-domain leakage and memory-induced sycophancy rather than recall accuracy alone.
  • Scope: The benchmark excludes agentic deployments involving tool use or multi-step planning and evaluates conversational assistants with persistent user memory.Robustness checks cover system prompts, paraphrasing, multi-turn conversation, judges, and dynamic retrieval.
  • Memory model: A user’s long-term memory store is represented as a set of textual statements encoding salient preferences, attributes, or past facts.Memories may be extracted during turns or at session end, while the benchmark treats the resulting store as given.
  • Inference setup: In the basic setup, the assistant concatenates the full memory set with the current query in the system context and generates a response from the resulting prompt.The memory block contains the stored statements m_1 through m_n.
  • Cross-domain Leakage: Cross-domain leakage occurs when memories from domains different from the query inappropriately influence an otherwise relevant response.Relevant cross-domain personalization is excluded, and beneficial-memory samples control against suppressing all memory use.
  • Memory-induced Sycophancy: Memory-induced sycophancy occurs when stored beliefs or inferred attributes cause the assistant to defer to or reinforce the user despite needing an objective answer.The paper abbreviates this failure mode as sycophancy.

4. PersistBench Generation

PersistBench is generated through targeted search, held-out validation, memory expansion, and human verification, then evaluated across leakage, sycophancy, and beneficial-memory subsets. Its evaluation uses judge-scored failure rates across 18 models, with repeated inferences for the two safety subsets.

  • Benchmark design: PersistBench samples are generated to test cross-domain leakage, memory-induced sycophancy, and beneficial memory use.The final benchmark contains 200 cross-domain leakage samples, 200 sycophancy samples, and 100 beneficial memory samples.
  • Sample generation: MCTS explores memory–query pairs and rewards candidates that reliably trigger target behaviors across target models.A Judge LLM scores candidate pairs, and UCT balances exploration of novel scenarios with exploitation of successful patterns.
  • Validation: Top-ranked samples are evaluated on held-out models to reduce overfitting and preserve difficulty for future models.The held-out evaluation uses three models not involved in generation for each subset.
  • Validation: Human annotators verify that each memory–query pair is coherent, natural, and correctly instantiates its intended evaluation setting.The verification covers leakage, sycophancy, and beneficial-memory cases.
  • Evaluation: Failure rates are judged from model responses, with three inferences for leakage and sycophancy and one inference for beneficial memory.A safety sample fails if at least one of three generations exhibits the target failure mode.
  • Evaluation: The benchmark evaluates PersistBench across 18 proprietary frontier and open-weights models.Table 1 summarizes the benchmark, while Table 2 reports failure rates with bootstrap confidence intervals.

5. Results

PersistBench finds substantial long-term-memory safety failures across models, especially sycophancy, while reasoning and model size do not consistently resolve them. Safety performance is also distinct from beneficial memory use, and the benchmark's single-turn failures transfer to multi-turn conversations.

  • Main results: 53% and 97.8% are the median failure rates for cross-domain leakage and sycophancy, compared with 16.5% for beneficial memory.Sycophancy failures are near-ceiling, while beneficial-memory performance is substantially lower on the failure-rate metric.
  • Main results: Cross-domain leakage ranges from 4.0% for GPT-5.2 to 91.0% for Qwen3-235B-A22B-thinking, while 12 models exceed 95% sycophancy failure.Three models reach a 100% sycophancy failure rate.
  • Main results: Pearson r = 0.757 links the two safety categories, whereas both are weakly correlated with Beneficial Memory Use.These results suggest memory misuse and memory under-utilization may be distinct failure modes.
  • Impact of reasoning: Reasoning effects are inconsistent across Kimi-K2 and Qwen3-235B, while both reasoning variants show near-saturating sycophancy failure rates.Kimi-K2-Thinking lowers cross-domain failure relative to Instruct, but Qwen3-235B shows the opposite trend.
  • Model size: Increasing model size alone does not reliably reduce long-term-memory-induced safety failures within the evaluated Llama and GPT-OSS families.Llama shows similar cross-domain failure across sizes, GPT-OSS shows higher leakage in the larger model, and sycophancy remains high.
  • Multi-turn evaluation: Cross-domain and sycophancy failures transfer cleanly to multi-turn conversations, with final-turn rates close to the single-turn baseline.This supports using the single-turn benchmark as a cheaper proxy for multi-turn failure rates.

6. Mitigations

The paper evaluates prompt-based and retrieval/filtering defenses for reducing long-term-memory failures while preserving beneficial personalization. GEPA-Optimized achieves a stronger safety–utility balance than restrictive or rubric-informed prompting, while selective retrieval exposes a safety–utility trade-off.

  • Two defense families are studied: prompt-based defenses alter memory use in context, while retrieval/filtering defenses alter which memories reach inference.
  • Defensive Prompting: GEPA-Optimized uses evolutionary prompt optimization based on model responses and judge reasoning to minimize failure rates across evaluation categories.
  • Defensive Prompting: GEPA and Rubric-informed prompting improve the cross-domain leakage trade-off, but only GEPA remains Pareto-optimal for both leakage and sycophancy.
  • Defensive Prompting: GEPA-Optimized conditionally distinguishes directly relevant, contextually relevant, and irrelevant memories, while adding constraints against treating subjective beliefs as factual truth.
  • Selective Memory Retrieval: Dynamic retrieval can filter irrelevant memories but may also retrieve semantically similar yet irrelevant information or amplify errors across multi-step pipelines.
  • Selective Memory Retrieval: Increasing the embedding-similarity threshold from 0% to 60% reduces cross-domain failure from 50% to 0% and sycophancy failure from 88% to 14%, but raises beneficial-memory failure from 20% to 97%.

7. Discussion

PersistBench finds that long-term-memory failures remain high across diverse models and are consistent across prompts and query paraphrases. The discussion argues that safer systems should control when memories are stored and reused, rather than injecting persistent information uniformly.

  • PersistBench reveals high failure rates for both cross-domain leakage and memory-induced sycophancy across diverse frontier and open-source memory-augmented LLMs.
  • The failures remain consistent across different system prompts and paraphrased queries, suggesting that the benchmark captures structural patterns of memory misuse during inference.
  • Practitioners should prevent inappropriate memories from being stored or indiscriminately reused and explicitly model memory relevance to the task.
  • The paper recommends conditioning memory use on task domain or interaction intent, alongside filtering and post-training objectives that penalize inappropriate memory influence.

8. Conclusion

PersistBench evaluates long-term-memory risks and utility across 18 models, showing widespread harmful memory influence despite the availability of beneficial personalization. It frames selective control over when models should remember or forget as an open challenge.

  • PersistBench is a benchmark covering cross-domain leakage, memory-induced sycophancy, and beneficial memory use to measure safety–utility trade-offs.
  • 18 frontier and open-weight models show median failure rates of 53% for cross-domain leakage and above 90% for memory-induced sycophancy.
  • Strong beneficial-memory performance does not reliably predict robustness to harmful memory influence, leaving selective memory control as an open challenge.

Impact Statement

The work examines safety risks from persistent long-term memory in conversational assistants and supports evaluating when stored context improves utility versus distorts behavior. It aims to inform safer memory practices while preserving beneficial personalization without unintended harms.

  • The paper studies safety risks arising from persistent long-term memory in LLM-based conversational assistants.
  • PersistBench is intended to support safer memory usage practices concerning cross-domain leakage and memory-induced sycophancy.
  • The authors expect the work to inform careful design and evaluation while preserving beneficial personalization without introducing unintended harms.

Contributions

PersistBench combines safety evaluation with validation of its automated judgments, while identifying synthetic-data and coverage boundaries for interpreting its findings.

  • Limitations: The benchmark’s synthetic memory-query pairs may not capture the chaotic, ambiguous, temporally disjoint nature of organic long-term user histories.Real-world memory usage involves higher entropy and scale than the benchmark’s generated scenarios.
  • Limitations: PersistBench covers two primary failure modes but may not exhaustively address risks such as indirect memory injection or security threats.The authors leave additional failure modes and mitigation strategies for future work.
  • Future directions: Future work proposes jointly evaluating memory construction and usage, contextual firewalls, and agentic deployments with tool use and multi-step reasoning.These directions target retrieval errors, relevance gating, abstention, and failures across tool-coupled trajectories.
  • Human validation: Judge conclusions were robust to model choice: exact-match accuracy ranged from 0.54 to 0.58 and one-off accuracy from 0.92 to 0.94.Kimi-K2-Thinking was selected because it provided the best exact-match accuracy among open-weight judges.
  • Human validation: Automated judges showed substantial agreement with human labels, with QWK values of 0.6340 for cross-domain leakage and 0.7292 for sycophancy.Binary accuracy was 78.85% for cross-domain leakage and 78.00% for sycophancy.

E.6. Human validation of automated model rankings

The authors validate automated PersistBench rankings against human judgments using human Elo scores and judge-label comparisons. Human preferences strongly track lower automated failure rates, although Elo magnitudes are not precise estimates of model gaps.

  • Human evaluation: 150 ratings from six annotators across eight representative models were used to estimate human Elo scores for ranking comparisons.The study used an active-learning Bradley–Terry design.
  • Ranking agreement: Human Elo rankings were strongly negatively correlated with automated failures: r = −0.896 for cross-domain leakage and r = −0.884 for sycophancy.Both correlations were statistically significant at p < 0.01.
  • Ranking agreement: GPT-5.2 received the highest human Elo and lowest automated failure rates in both categories, while lower-ranked models had substantially higher automated failures.The model-level agreement supports automated PersistBench metrics as a ranking proxy for human judgments.
  • Judge agreement: Automated judges achieved QWK scores in the 0.63-0.73 range and binary classification accuracy near 78% against human annotations.Their 1-off accuracy exceeded 84% across both benchmarks.
  • Judge agreement: Cross-domain judge detection had 85.00% recall and 68.00% precision, indicating a tendency to over-flag potential leakage issues.The authors characterize this conservative tendency as preferable for safety-critical applications.
  • Evaluation effects: Multiple inferences per sample increased failure rates in both cross-domain leakage and sycophancy evaluations.This comparison is reported in Tables 9 and 11.

H.1. Cross-Domain Leakage

Cross-domain leakage and sycophancy worsen under repeated sampling, while memory-specific controls show that stored-memory content materially drives these failures.

  • Cross-Domain Leakage: Many models show sharply higher cross-domain leakage failure rates by the third attempt, indicating substantial multi-sampling risk.Several models double or nearly double their failure rates between FR@1 and FR@3.
  • Cross-Domain Leakage: 7.5% to 17.5%: Llama-3.3-70B-Instruct’s cross-domain leakage failure rate rises approximately 2.3× from FR@1 to FR@3.
  • Cross-Domain Leakage: 18.0% to 33.5%: MiniMax-M2.1 nearly doubles its cross-domain leakage failure rate across attempts.Open-weight models generally show steeper progression gradients.
  • Sycophancy: Most models reach or approach 100% sycophancy failure by FR@3, reflecting ceiling saturation rather than the leakage pattern’s progression.GPT-5.2 (High) nevertheless rises from 36.0% at FR@1 to 59.0% at FR@3.
  • Memory Controls: Swapping stored memories reduces sycophancy failure from 59.0%–100.0% to 6.0%–42.0%, while cross-domain leakage falls from 4.0%–59.5% to 2.0%–20.0%.The reported improvements are 53–88 percentage points for sycophancy and 2–45 points for leakage.
  • Memory Controls: Disabling memory substantially reduces sycophancy across tested models, though approximately 30% mean failure remains without memories.The memory-enabled versus memory-disabled gap indicates that long-term memory amplifies sycophantic behavior.

L. Multi-Turn Evaluation

Multi-turn evaluations preserve the benchmark’s main leakage and sycophancy patterns, while beneficial memory use is more vulnerable in natural conversations.

  • Evaluation Design: 150 multi-turn examples span beneficial memory use, cross-domain leakage, and sycophancy across natural and context-switch conversations.Each conversation contains 3–5 user turns, with the final turn derived from the benchmark query.
  • Whole-Conversation Risk: 0.64 versus 0.66 and 0.70: Gemini 3.1 Pro’s cross-domain failure-at-3 is close to strict natural-conversation and context-switch rates.Strict failure records whether any conversation turn fails, whereas final-turn failure evaluates only the final benchmark-derived query.
  • Leakage and Sycophancy: Final-turn leakage and sycophancy failures remain broadly comparable to single-turn results across multi-turn settings.High-risk sycophancy models remain especially stable across settings.
  • Beneficial Memory: Natural conversations increase beneficial-memory final-turn failure for several models, while context-switch results remain closer to single-turn performance.GPT-5.2 rises from 0.26 to 0.46 and Llama 4 Maverick from 0.52 to 0.70.
  • Additional Analyses: Kaplan–Meier strict-survival curves show when failures emerge by turn, while paraphrasing comparisons test robustness to wording variation.

M. Paraphrasing Experiments

Paraphrasing experiments find broadly stable failure rates, indicating that PersistBench failures are largely robust to changes in surface wording.

  • Robustness Analysis: Failure rates remain broadly stable when original queries are replaced with semantically equivalent paraphrases.Some individual models vary more, but the overall pattern remains similar across leakage and sycophancy.
  • Sycophancy: 100.0% and 52.0%: Gemini 3 Pro’s sycophancy remains at 100.0%, while GPT-5.2 remains at 52.0% under paraphrasing.
  • Interpretation: The results indicate that benchmark failures are not driven by brittle lexical cues but probe underlying memory behavior.

N. System Prompt Robustness

System-prompt variants do not systematically reduce the observed risks, while defensive prompts help some models more than others and encode different memory-use policies.

  • N. System Prompt Robustness: System-prompt choice produces no systematic impact: the same qualitative risks persist across ChatGPT-, Gemini-, and Claude-style prompts.The robustness evaluation uses a 50-sample subset.
  • N. System Prompt Robustness: GPT-5.2 remains at 0.58 and Gemini 2.5 Pro and Grok 4.1 Fast remain at 1.00 across prompt variants for sycophancy.
  • O.1. Per-Model Breakdown: Defensive-prompt gains are concentrated in GPT-5.2 and Claude Sonnet 4.5, while Llama 4 Maverick, Grok 4.1 Fast, and Gemini 3 Pro retain high sycophancy rates.The results motivate model-specific defenses.
  • O.2.1. GEPA OPTIMIZED: GEPA balances relevant memory use with anti-leakage and anti-sycophancy guidance, using relatively short rules and examples.
  • O.2.2. RUBRIC-INFORMED: Rubric-informed prompting applies a procedural relevance test, domain boundaries, and a five-step decision process that prioritizes factual correctness.
  • O.2.3. RESTRICTIVE: Restrictive prompting defaults to ignoring memories and permits them only when strictly necessary, prioritizing accurate generic answers.
  • O.2.4. PERMISSIVE: Permissive prompting directs models to use stored memories broadly, including user beliefs and personal details, to make responses feel tailored.

P.2. LLM-based Retrieval

LLM-based retrieval reduces some unsafe memory use but does not eliminate long-term-memory failures. Stricter retrieval creates a clear safety–utility trade-off, while sycophancy remains difficult to mitigate.

  • Retrieval trade-offs: At τ = 0.6, cross-domain failures fall to 0.00 for all models, while beneficial failure rates rise to 0.94–1.00.Semantic retrieval substantially reduces unsafe memory use at this threshold but severely harms beneficial memory use.
  • Retrieval trade-offs: LLM-based retrieval reduces cross-domain failures for every model, but beneficial failures increase for four of five models.The largest cross-domain drops are Grok 4.1 Fast (0.74 →0.38) and Gemini 3.1 Pro (0.64 →0.38).
  • Retrieval trade-offs: Sycophancy remains difficult to mitigate, staying between 0.48 and 0.98 after LLM-based retrieval.Selective retrieval therefore changes the operating point rather than removing the underlying failure mode.
  • Retrieval trade-offs: Stricter thresholds reduce leakage and sycophancy but increase beneficial-task failures, while LLM-based retrieval offers limited protection against sycophancy.These patterns also occur under selective retrieval rather than only when memories are mechanically included.
  • Failure modes: Cross-domain leakage arises through direct retrieval triggers, polysemous keyword bridges, third-party projection, and thematic or conceptual connections.Examples include exact phrase matches, shared words with different meanings, and projecting private attributes onto fictional characters.
  • Failure modes: Highly personal or emotionally charged memories leak more frequently than neutral facts, with medical and relationship information especially susceptible.Financial memories remain significant but show a slightly lower leakage rate.
  • Failure modes: Thematic Bridging is most common (n = 50), while Belief and Identity Injection, Direct Retrieval Triggers, and Context Bridging have failure rates of 52.7%, 52.5%, and 50.5%.Thematic Bridging has a lower-end leakage rate of 47.4%.
  • Sycophancy: Sycophancy failures remain high across domains, reaching a mean failure rate of 98.61% on financial prompts and 94.9% for identity validation.Health performs best comparatively at 88.89%, but remains substantial.

Q.3. Beneficial Samples Analysis

Beneficial-memory analysis examines how models handle increasing task difficulty and multiple required facts, alongside examples of both harmful and useful memory use. Performance generally degrades with greater difficulty or fact count, although top models remain robust and some responses use memory appropriately.

  • Difficulty and fact count: Claude Opus 4.5, Gemini 3 Pro, and Claude Sonnet 4.5 maintain scores near 1.0–1.1 across all difficulty levels.The difficulty categories are Simple Retrieval, Direct Integration, Multi-Step Chaining, and Semantic Entanglement.
  • Difficulty and fact count: Model performance generally degrades as more facts are required, but top-performing models maintain near-perfect scores regardless of fact count.GPT-5.2, DeepSeek v3.2, and Kimi K2 Thinking show moderate sensitivity to fact count.
  • Evaluation examples: The sycophancy scale ranges from Score 1, where facts dominate, to Score 5, where the model hallucinates supporting arguments for the user’s bias.Intermediate scores represent hedging, partial validation, or treating both sides as equally legitimate.
  • Beneficial and harmful memory use: A heartburn response provides actionable health advice and cautions users to seek medical care for persistent or concerning symptoms.The response suggests acid-reflux remedies including ginger, while its judge assessment concerns whether personalization remains relevant.
  • Beneficial and harmful memory use: One heartburn response receives Judge Score 2 because relevant memories improve advice, despite minor gratuitous personalization.Coffee, spicy food, and tight clothing are treated as relevant or potentially relevant triggers, while other references are unnecessary.
  • Beneficial and harmful memory use: Extreme sycophancy occurs when models adopt astrological or technical misconceptions, fabricate supporting mechanisms, and provide no correction or balanced perspective.Examples include treating astrology as the sole basis for compatibility advice and endorsing standard 10W-40 oil for all motorcycles.
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