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arxiv: 2606.06055 · v1 · pith:TOE32436new · submitted 2026-06-04 · 💻 cs.AI

When Should Memory Stay Silent: Measuring Memory-Use Boundaries in Memory-Augmented Conversational Agents

Lingxiang Xu , Jiaoyun Yang , Min Hu , Hongtu Chen , Ning An This is my paper

Pith reviewed 2026-06-28 01:55 UTC · model grok-4.3

classification 💻 cs.AI
keywords memory-augmented agentssensitive memory integrationLLM evaluationpersonalization safetyretrieval systemsconversational agentsmemory boundaries
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The pith

Access to memory causes conversational agents to integrate sensitive content into responses even under benign prompts.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents RBI-Eval as a controlled way to test when memory-augmented agents should keep sensitive memories silent. It compares model outputs on identical benign prompts both with and without access to sensitive memory across full context and three retrieval setups. The evaluation covers four base language models and shows clear behavioral changes once memory becomes available. Separation scores drop for all models but by markedly different amounts, and retrieval lowers exposure without stopping integration at generation time. The work concludes that safe personalization therefore needs memory-aware choices at both retrieval and generation stages.

Core claim

RBI-Eval shows that providing access to sensitive memory produces substantial divergence from matched no-memory references. The separation score for sensitive-memory integration decreases by 8.9%--26.6% for one model and by 51.1%--82.9% for the other three. Control experiments establish that the change is specific to sensitive content rather than general personalization. Retrieval systems cut exposure but do not prevent integration once sensitive memory reaches the generator. These results indicate that safe personalization requires memory-aware decisions at both retrieval and generation time.

What carries the argument

RBI-Eval, a measurement study built on a probe set that compares model behavior with and without sensitive memory under identical benign prompts and quantifies the difference via a separation score.

If this is right

  • Retrieval systems reduce exposure to sensitive memory but do not eliminate its integration once the content reaches the generator.
  • The integration effect is specific to sensitive content rather than a general personalization tendency.
  • Models differ sharply in how readily they integrate sensitive memory, so safeguards must account for model variation.
  • Memory-aware decisions are required at both the retrieval stage and the generation stage for safe personalization.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Designers may need to add explicit suppression logic at generation time even when retrieval has already filtered content.
  • Similar probe-based tests could be applied to measure boundaries for other memory categories such as factual versus preference-based content.
  • Training objectives that penalize use of sensitive memory under benign prompts might raise separation scores across models.
  • Current retrieval-augmented pipelines leave a gap between what is retrieved and what should be used that generation-time checks could close.

Load-bearing premise

The probe set and separation score definition accurately capture scenarios in which sensitive memory content should remain unused under benign prompts.

What would settle it

Re-running the same probe set and finding no measurable drop in separation score when memory is supplied would show that the claimed integration does not occur.

Figures

Figures reproduced from arXiv: 2606.06055 by Hongtu Chen, Jiaoyun Yang, Lingxiang Xu, Min Hu, Ning An.

Figure 1
Figure 1. Figure 1: Memory access shifts response framing: a [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: RBI-Eval construction pipeline. LoCoMo dialogues are distilled into persona profiles, then used to [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Paired changes in sensitive-history integration [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: DeepSeek target-memory exposure and integration-if-exposed under neutral prompting. Yet the model contrast remains, so the interface is not deterministic. Memory packaging and generator policy interact. 3.4 Q3: How Much Comes from Retrieval Exposure? We decompose integration risk by whether a sensi￾tive target memory was retrieved; Appendix [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Complete history-augmentation prompt schema; full per-persona values and template markdown are [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Complete neutral-generation prompt. The prompt supplies memory context and condition name when [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Complete condition-blind judge prompt. The executable user payload hides evaluated model identity, [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: DeepSeek BSS-point loss by probe family; higher means larger loss versus matched no-memory responses. [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Retrieved memories for a fixed persona/query; useful outing context can also expose private anchors. [PITH_FULL_IMAGE:figures/full_fig_p021_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: MemU-conditioned responses across base LLMs; plausible advice reuses unrequested private history. [PITH_FULL_IMAGE:figures/full_fig_p021_10.png] view at source ↗
read the original abstract

Long-term memory enables language model agents to support personalized interactions, but it remains unclear when available memories warrant integration into responses. Existing memory evaluations emphasize retrieval accuracy and downstream task utility, while overlooking whether retrieved sensitive memory content is warranted in the current turn. We introduce RBI-Eval, a controlled measurement study built around a probe set that compares model behavior with and without access to sensitive memory under identical benign prompts. We evaluate four base LLMs against a matched no-memory reference across four memory-access settings: full-context exposure and three retrieval systems. Our results reveal substantial behavioral divergence. With memory available, the separation score for sensitive-memory integration decreases by 8.9\%--26.6\% relative to the matched no-memory reference for GPT-5.4-mini, but by 51.1\%--82.9\% for Claude-Sonnet-4.6, DeepSeek-V4-Flash, and Qwen3.5-9B. Control experiments on DeepSeek and GPT-5.4-mini show this effect is specific to sensitive content, rather than general personalization. Retrieval systems reduce exposure but do not eliminate integration once sensitive memory reaches the generator. These findings suggest safe personalization requires memory-aware decisions at both retrieval and generation time.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces RBI-Eval, a controlled empirical measurement framework that compares LLM behavior with and without access to sensitive memory under identical benign prompts. It evaluates four base models across full-context and three retrieval settings, reports separation-score decreases of 8.9–26.6% for GPT-5.4-mini and 51.1–82.9% for the other three models relative to no-memory baselines, includes controls showing specificity to sensitive content, and concludes that safe personalization requires memory-aware decisions at both retrieval and generation time.

Significance. If the probe-set validity and separation-score definition hold, the work provides a direct, controlled demonstration that memory availability induces measurable integration of sensitive content even under benign prompts, with retrieval mitigating but not eliminating the effect. The inclusion of matched no-memory references and content-specificity controls is a strength that allows clear attribution of the observed divergence.

major comments (2)
  1. [RBI-Eval construction paragraph] The paragraph describing RBI-Eval construction (abstract and methods): the headline claim that observed separation-score decreases reflect unwarranted sensitive-memory integration under benign prompts rests on the assumption that the probe prompts legitimately warrant non-use of the sensitive memory; the manuscript provides no explicit criteria, examples, or construction details for the probe set, nor any statistical tests or error bars, which is load-bearing for interpreting the reported 8.9–82.9% ranges and model differences.
  2. [Results] Results section (model comparisons): the separation-score definition and its computation are not shown, so it is impossible to verify whether the metric cleanly isolates integration from other factors such as prompt relevance or generation stochasticity; this directly affects the cross-model claims.
minor comments (2)
  1. [Abstract] The abstract states four memory-access settings but does not name the three retrieval systems; adding their names and a brief citation would improve clarity.
  2. [Control experiments paragraph] Control experiments are mentioned for DeepSeek and GPT-5.4-mini only; a short note on why the other two models were omitted would prevent reader confusion.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. The comments highlight areas where additional methodological detail will improve clarity and verifiability. We address each point below and will incorporate revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: [RBI-Eval construction paragraph] The paragraph describing RBI-Eval construction (abstract and methods): the headline claim that observed separation-score decreases reflect unwarranted sensitive-memory integration under benign prompts rests on the assumption that the probe prompts legitimately warrant non-use of the sensitive memory; the manuscript provides no explicit criteria, examples, or construction details for the probe set, nor any statistical tests or error bars, which is load-bearing for interpreting the reported 8.9–82.9% ranges and model differences.

    Authors: We agree that the current description of probe-set construction is insufficiently detailed. The probe prompts were designed to be benign and topically unrelated to the sensitive memory content (e.g., general queries about daily activities when the memory concerns medical or financial details), but explicit criteria, sample prompts, and construction protocol are indeed missing. In revision we will add a dedicated subsection detailing the probe-set generation process, inclusion/exclusion criteria, and examples. We will also report bootstrap confidence intervals and appropriate statistical tests (e.g., paired t-tests or Wilcoxon tests) for the separation-score differences to support the reported ranges. revision: yes

  2. Referee: [Results] Results section (model comparisons): the separation-score definition and its computation are not shown, so it is impossible to verify whether the metric cleanly isolates integration from other factors such as prompt relevance or generation stochasticity; this directly affects the cross-model claims.

    Authors: We acknowledge that the separation-score formula and its exact computation steps are not presented in the results section. The metric is defined as the normalized difference in the rate of sensitive-content leakage between the memory-available and no-memory conditions, with controls for prompt relevance via matched benign prompts and stochasticity via multiple generations per prompt. In the revised manuscript we will insert an explicit definition, the mathematical formulation, and a description of how relevance and stochasticity are controlled, allowing readers to assess whether the metric isolates integration effects. revision: yes

Circularity Check

0 steps flagged

Empirical measurement study with no derivations or fitted parameters

full rationale

The paper is a controlled empirical evaluation introducing RBI-Eval to compare LLM behavior with versus without sensitive memory access under identical benign prompts. All reported results are direct observational differences (separation score changes relative to no-memory baselines) across models and retrieval settings, with control experiments for specificity. No mathematical derivations, first-principles predictions, parameter fitting, or self-citation chains are present; the central claims rest on the probe-set construction and score definition, which are inputs to the measurement rather than outputs that reduce to themselves by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The study rests on standard assumptions of LLM evaluation (prompts are benign, separation score measures integration) without introducing new free parameters or entities.

axioms (1)
  • domain assumption The separation score quantifies unwarranted sensitive-memory integration under identical benign prompts
    Central metric of the RBI-Eval framework described in the abstract

pith-pipeline@v0.9.1-grok · 5762 in / 1274 out tokens · 19266 ms · 2026-06-28T01:55:42.249554+00:00 · methodology

discussion (0)

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Reference graph

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