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REVIEW 3 major objections 5 minor 34 references

DP-MemView: A Memory Interface for Attribute-Level Transcript Privacy in Long-Term LLM Agents

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read A memory-interface contract composes every repeated LLM-agent release into one pure differential-privacy bound per protected attribute.

desk verdict A correct and honest conditional DP guarantee for the memory interface, but the main clause — content-independent retrieval — is exactly what real semantic memory does not provide, and the experiments never leave that clause. read the letter →

arxiv 2608.03130 v1 pith:UQSUEX2N submitted 2026-08-04 cs.CR cs.CLcs.LG

Jong Wook Kim , Byoungjae Min , Kennedy Edemacu , Yoonhyuk Choi , Sae-Hong Cho , Beakcheol Jang This is my paper
classification cs.CRcs.CLcs.LG
keywords long-termmemoryLLMagentsadaptivetranscriptprivacydifferentialexponentialmechanismattribute-levelmemory-viewinterfacepersonalization
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Long-term memory lets LLM agents personalize across sessions, but it also lets a semi-trusted application infer protected attributes—such as a health condition or debt—by watching many ordinary, memory-conditioned responses that never state the fact outright. This paper tries to establish that this cumulative leakage can be closed at the memory interface: instead of handing raw memories to the response model, the interface privately selects public "views" (response-conditioning instructions, such as blood-sugar-conscious advice) with the exponential mechanism, and charges every selection to each protected attribute whose memory group was read. The paper proves that if the interface honors six stated invariants—common view support, complete per-attribute charging, no memory-dependent side channels, store-independent routing and ordering, pathwise budget caps with a generic fallback, and downstream post-processing—the entire adaptive transcript of queries, signals, and responses satisfies pure $B_a$-differential privacy for each protected attribute, even against an adversary who chooses every query from the observed history. A sympathetic reader would care because this converts a diffuse "it might leak over time" worry into a composable, prior-independent bound on how much a conversation can shift an adversary's beliefs, and the experiments indicate that personalization and response quality survive. The guarantee's reach is explicitly conditional: content-dependent retrieval is outside the theorem, protection depends on each attribute group being completely specified, and the multi-slot ordering subcase of the contract is argued by proof and counterexample rather than isolated empirically.

What carries the argument

The load-bearing object is the DP-MemView interface contract of Definition 1, enforced by per-attribute privacy ledgers at the trusted memory-interface boundary. Raw memory never reaches the response model: for each active slot, the interface samples a "view" from a public vocabulary shared across adjacent stores—a fixed generic view (the store-independent fallback) plus response-conditioning modes such as blood-sugar-conscious advice—using the exponential mechanism with a policy-fixed sensitivity bound $\Delta u_{t,g}$ on the internal score vector. Every read is charged to all attributes whose protected group intersects the read set ($\Gamma^\star \subseteq \Gamma_{t,g}$); a selection is ad

What would settle it

For the theorem itself: on a small store where the full transcript distribution can be enumerated exactly, test every event $O$ for $\Pr[\tau_T \in O \mid M] \leq e^{B_a} \Pr[\tau_T \in O \mid M']$; a single violating event would refute Theorem 1's composition claim. For the premise: implement a variant with content-dependent retrieval (for example, semantic-similarity search over private memory contents) while otherwise following the contract, and measure transcript distinguishability over a longer horizon—growing leakage with turn count would confirm the paper's explicit statement that such

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Extended reading notes

Core claim

The paper's central claim is Theorem 1: if a memory interface satisfies the six invariants (C1)–(C6) of its Definition 1, then for every protected attribute $a$, every adjacent pair of memory stores $M \sim_a M'$ (identical outside the positions that may imply $a$), and every adaptive query strategy, the augmented transcript $\tilde{\tau}_T$—queries, privately selected views, observable control signals, and responses—satisfies pure $B_a$-DP; the application-facing transcript $\tau_T$, which hides the views, inherits the same bound by post-processing. The practical reading: no matter how the application adapts its queries, observing the whole conversation can multiply an adversary's prior odd

Load-bearing premise

The load-bearing premise is that the read policy is content-independent—the memory positions consulted for a query are fixed by the public query, the memory layout, and the released history, never by what the stored memories say—because the paper states that content-dependent retrieval is not covered by the transcript-privacy theorem; the guarantee is additionally conditional on every position that may imply a protected attribute being included in that attribute's group.

Editorial extensions

If this is right

  • With cap $B_a$, any finite sequence of memory-conditioned interactions leaks at most a factor $e^{B_a}$ in likelihood ratio about attribute $a$, regardless of how the application adapts its queries.
  • Observing any transcript event can change the prior odds for a protected attribute by at most $e^{B_a}$ in either direction, for any prior.
  • For stores differing across several protected groups, the transcript guarantee holds with total budget equal to the minimum-cost cover of the differing positions by protected groups.
  • On both evaluation tracks, the two budget modes keep attribute distinguishability near chance (AUC near 0.5) across three response LLMs while matching or exceeding raw-memory baselines on overall response quality and recovering target-required personalization.
  • Ablations show that private selection alone is insufficient: omitting complete charging, the randomized selection rule, or the cumulative cap each breaks the $B_a$-DP certificate and raises transcript distinguishability.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper explicitly leaves content-dependent retrieval out of the theorem; a natural extension is to make retrieval itself private (an adjacency-invariant or differentially private index) and charge its cost to the same attribute ledgers, which would close that gap.
  • Because the contract treats any observable release conditioned on public state as post-processing, the same ledger accounting should transfer to non-text channels—tool calls, action trajectories, API payloads—making the interface a template for agent privacy beyond response text.
  • The proof's ledger induction implies that generic-fallback events are identical across adjacent stores conditioned on the same released prefix, so an observable "budget exhausted" indicator would carry no additional privacy loss under the contract.
  • A sharper evaluation would audit the likelihood-ratio bound directly—enumerating transcript events on small stores or using confidence-rated adversarial queries—rather than relying on classifier AUC as the privacy measure.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper proposes DP-MemView, a trusted memory-interface layer for long-term LLM agents. Instead of passing raw memory to a response LLM, the interface privately selects slot-level views from public vocabularies using the exponential mechanism, charges every protected attribute whose memory group intersects the read set, and enforces per-attribute pure-DP caps with a fixed generic fallback. The central formal contribution is Theorem 1: under six interface invariants (C1--C6), every adaptive transcript satisfies pure B_a-DP with respect to group-replacement adjacency. Corollary 1 extends this to stores differing in multiple protected groups via a minimum-cover cost, and Appendix B.4 derives a prior-independent posterior-odds bound. The paper also reports controlled experiments on paired synthetic stores (PairedMem) and a public-corpus transfer track with three response LLMs, showing that both online and preallocated budget modes keep distinguishability close to chance while preserving response quality, plus ablations that isolate the role of each invariant.

Significance. If Theorem 1 stands, the paper is a useful step toward composable, attribute-level privacy for LLM-agent memory. Its strengths are the explicit interface contract, the self-contained composition proof in Appendix B.2, and the isolating failure-mode constructions in Appendix B.5, which correctly show that common support, complete charging, downstream channel isolation, and pathwise caps are each necessary. The PairedMem benchmark is a well-controlled construction (same scaffold, fixed trajectories, matched stores), and the ablation table cleanly demonstrates that private view selection alone is insufficient. The paper is also admirably honest about the limits of its theorem, most notably that content-dependent retrieval is outside the guarantee. However, that same honesty exposes a load-bearing scope gap: the intended deployment of long-term LLM agents normally uses content-addressed semantic retrieval, and the empirical evaluation never exercises that regime. The empirical claim that distinguishability is 'near chance' is also under-supported because no error bars or significance tests are reported, and the transfer-track auditor may lose power from domain shift. The formal result

major comments (3)
  1. [DP-MemView / Definition 1, condition (C4); Algorithm 1 line 6] The theorem depends critically on a content-independent read policy: C4 requires the read set R_{t,g} to be identical across adjacent stores given the same released prefix, and Algorithm 1 line 6 computes R_{t,g} only from the public query, fixed layout, and history. The paper explicitly states, 'Content-dependent retrieval is not covered by the transcript-privacy theorem; it would need to be adjacency-invariant or separately private, with any retrieval cost charged to every affected attribute.' This is a major limitation because real long-term LLM memory is normally content-addressed (e.g., semantic top-k retrieval over stored facts), so the read set itself varies with M. Once R_{t,g} differs between adjacent stores, the score vector u_{t,g}(M) can differ without any ledger charge, the proof in Appendix B.2 no longer applies, and the B_a-DP certificate does not hold. The experiments nev
  2. [RQ1 / Table 2 and Table 3] No confidence intervals, standard errors, or significance tests are reported for any privacy or utility metric. This is especially important for the 'near chance' claim on the public-corpus transfer track, where the numbers are not obviously at chance: e.g., DP-MemView (on) gives AUC 0.562 (Qwen), 0.563 (Llama), 0.535 (Gemma) and TPR@5 values of 0.113, 0.100, and 0.100 versus the chance value of 0.05; TPR@5 is more than double chance. The PairedMem results are closer to 0.5, but transfer-track values hover around 0.53--0.56, which may or may not be 'near chance' depending on the variance over the 80 pairs. Stochastic methods are averaged over only three EM seeds, and no per-seed spread is given. I am not claiming the results are fabricated, but as reported the empirical support for the abstract's 'near chance' statement is not statistically grounded. Please provide confidence intervals o
  3. [Appendix C.5 / Evaluation protocol; Table 9] The privacy auditor is a logistic-regression classifier trained on PairedMem training transcripts and then applied, without refitting or recalibration, to the corpus-sourced transfer track. Low transfer-track AUC could therefore reflect domain shift (different memory wording, corpus topics, and sentence styles) rather than a genuinely small privacy loss. The paper lists a second 'Pairwise privacy auditor' (Mistral-Small-24B) in Table 9 but never reports its results or explains why it is absent from the tables. Since the auditing method itself is the only empirical measure of transcript distinguishability, I would like to see either (i) results from a stronger, transferable auditor, (ii) a calibration/domain-transfer check for the logistic-regression auditor, or (iii) an explicit graph showing that the auditor can still detect leakage on the transfer track when an interface with known lea
minor comments (5)
  1. [Figure 2] Figure 2 is dense: the slot-level view names, ledger updates, and the fallback path are hard to read at print size. A zoom-in or a step-by-step caption would help.
  2. [Notation, Section 'Problem Definition'] The adjacency symbol M∼a M′ is introduced informally in the text and then used in equations; it would be cleaner to define it as a named relation (e.g., 'a-adjacency') where it first appears.
  3. [Table 2] The '†' footnote states that GenericOnly and TypedMask yield chance-level distinguishability by construction; this is correct, but the † marker should perhaps also appear in the transfer-track rows for consistency.
  4. [Appendix B.5] In the C2 counterexample, the value of N is required to satisfy N > 2Ba/ε0, and the construction later states Nε0 ≤ Bb; these conditions are compatible (choose N sufficiently large and Bb accordingly), but the text could make the order of choices explicit to avoid an apparent tension.
  5. [Conclusion] The conclusion says 'both DP-MemView modes kept transcript distinguishability near chance' without repeating the transfer-track caveat; consider adding a one-sentence reminder that this is for fixed, content-independent read policies.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: Theorem 1 is a self-contained composition argument over explicit interface invariants; no fitted parameter is relabeled as a prediction and no self-citation carries the load.

full rationale

The paper's central claim (Theorem 1) is a conditional statement: if the interface satisfies Definition 1's invariants (C1)-(C6), then the adaptive transcript satisfies pure B_a-DP. The proof is a standard DP composition argument: each admitted EM release contributes at most e^{epsilon_t,g} to the likelihood ratio, uncharged/adversary-invariant releases contribute 1, downstream query/control/LLM components are store-independent by (C4)/(C6), and the pathwise cap (C5) bounds the cumulative exponent by B_a. Conditions (C1)-(C6) are lower-level interface properties (common support, charging, EM sampling, prefix-conditioned identical read sets, cap enforcement, downstream isolation); none of them states the transcript-level DP conclusion, so the theorem is not self-definitional. No parameter is fitted to data and then reported as a prediction: the privacy guarantee is analytical, and the empirical AUC/TPR results are held-out measurements against external baselines. The only self-citation (Choi et al. 2025, RAG membership-inference defense) appears in related work and is not used to justify any load-bearing premise. The paper's own limitation, 'Content-dependent retrieval is not covered by the transcript-privacy theorem', is a genuine scope restriction on the interface contract, not a circular step; it means the theorem applies only when read sets are adjacency-invariant, which is stated explicitly in (C4) and in the read-policy description. Overall the derivation is self-contained and no circular reduction is present.

Assumptions & free parameters 4 free parameters · 7 assumptions · 0 invented entities

No new physical entities are introduced. The 'views', 'ledgers', and 'attribute groups' are software abstractions rather than entities with independent empirical handles. The main assumptions are the content-independent read policy, complete attribute grouping, and the store-independent response channel, all of which the paper states explicitly.

free parameters (4)
  • Privacy budget cap B_a = swept over {1,2,4,8} in experiments
    Precommitted per-attribute pure-DP budget; a policy choice, not fitted to data. The theorem holds for any B_a, but the empirical trade-off depends on its value.
  • Online budget granularity K = {2,3,4}, default 3
    Divides each B_a into equal per-release charges in online mode; chosen by hand, not fitted. Affects utility/privacy trade-off.
  • Planned charge count C_a = computed from planned query schedule
    Used in preallocated mode to set epsilon_t,g = B_a/C_a; a design choice depending on the known query trajectory.
  • Scorer weights = 0.4 relevance, 0.4 compatibility, 0.2 specificity
    Hand-chosen weights in the utility scorer; they determine the sensitivity bound Delta=0.6, which is valid for any weights as long as terms are in [0,1].
assumptions (7)
  • standard math Standard differential privacy composition and exponential mechanism (Dwork-Roth; McSherry-Talwar; Kairouz et al.)
    Used to compose per-release EM guarantees into the transcript guarantee in Theorem 1 and Corollary 1.
  • domain assumption Adjacent stores share the same scaffold and differ only within a protected group I_a; group contents remain slot-compatible
    Problem Definition: adjacency relation defines privacy neighborhoods; if stores differ outside I_a, the guarantee for a does not apply.
  • domain assumption Read sets R_{t,g} are content-independent, determined by public query, fixed layout, and released history
    DP-MemView section: content-dependent retrieval is explicitly excluded from the transcript-privacy theorem; Condition C4 requires identical read sets across adjacent stores under the same prefix.
  • domain assumption The response LLM is a fixed store-independent channel K_LLM(y|q,v,tau)
    Equation (2): if the response model could access raw memory or be influenced by it, downstream release would not be post-processing.
  • domain assumption Complete grouping: every memory position that may imply attribute a is included in I_a
    Problem Definition: the guarantee is conditional on complete grouping; omitted positions fall outside the adjacency relation.
  • domain assumption The scorer has a known policy-fixed sensitivity bound satisfying Eq (6)
    Condition C4 and Eq (6): the EM guarantee requires a bound Delta_u that holds across adjacent stores for every affected attribute.
  • domain assumption All observable controls and the response LLM are independent of raw memory given public inputs, released history, and ledger state
    Condition C6: needed so controls and responses add no privacy loss in Theorem 1.

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Pith. "Pith review of DP-MemView: A Memory Interface for Attribute-Level Transcript Privacy in Long-Term LLM Agents." pith.science (2026). https://pith.science/paper/UQSUEX2N

@misc{pith2026260803130,
  author       = {Pith},
  title        = {Pith review of: DP-MemView: A Memory Interface for Attribute-Level Transcript Privacy in Long-Term LLM Agents},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UQSUEX2N}},
  note         = {Machine review of arXiv:2608.03130}
}
read the original abstract

Long-term memory enables persistent personalization in LLM agents, but repeated memory-conditioned responses can cumulatively reveal protected attributes even when they are never stated explicitly. We formalize this threat as adaptive transcript privacy and introduce DP-MemView, a differentially private interface that privately selects public response-conditioning views and exposes those views---rather than raw memory---to the response LLM. Each private selection is charged to every protected attribute whose memory group intersects the read set. Per-attribute ledgers block any selection that would exceed its cap and return a fixed generic view instead. Under an explicit interface contract, we prove pure B_a-DP for the entire adaptive transcript. We also extend the result to stores that differ across multiple protected groups and bound how much observing the transcript can change an adversary's prior odds. We evaluate the online and preallocated modes with three response LLMs on a controlled adjacent-store benchmark and a public-corpus transfer track. Both modes keep transcript distinguishability near chance while preserving target-required personalization and overall response quality. Further diagnostics show that removing key safeguards causes mismatched output support, missing ledger charges, revealing side channels, or growing long-horizon leakage.

Figures

Figures reproduced from arXiv: 2608.03130 by the authors.

Figure 1
Figure 1. Cumulative attribute leakage through benign per [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Running example of DP-MemView for an affordable-dinner query. The interface activates the food and finance slots, maps each read set to the affected attributes, and applies the corresponding privacy-budget checks. The food slot releases the fixed generic fallback, while the finance slot releases the selected strict-budget view and charges the debt ledger. Only qt, the released view tuple vt, and τ<t are passed to th… view at source ↗

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Works this paper leans on

34 extracted references · 26 canonical work pages

  1. [1]

    Evaluating Very Long-Term Conversational Memory of

    Maharana, Adyasha and Lee, Dong-Ho and Tulyakov, Sergey and Bansal, Mohit and Barbieri, Francesco and Fang, Yuwei , booktitle=. Evaluating Very Long-Term Conversational Memory of

  2. [2]

    arXiv preprint arXiv:2507.05257 , year=

    Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions , author=. arXiv preprint arXiv:2507.05257 , year=

    arXiv

  3. [3]

    Unveiling Privacy Risks in

    Wang, Bo and He, Weiyi and Zeng, Shenglai and Xiang, Zhen and Xing, Yue and Tang, Jiliang and He, Pengfei , booktitle=. Unveiling Privacy Risks in

    work page

  4. [4]

    Lyu, Xingyu and He, Jianfeng and Wang, Ning and Hu, Yidan and Li, Tao and Chen, Danjue and Li, Shixiong and Chen, Yimin , journal=

    work page

  5. [5]

    arXiv preprint arXiv:2605.09530 , year=

    MemPrivacy: Privacy-Preserving Personalized Memory Management for Edge-Cloud Agents , author=. arXiv preprint arXiv:2605.09530 , year=

    arXiv

  6. [6]

    Agent-Memory Protocol: A Privacy-Focused Protocol for

    Wu, Junde and Hu, Minhao and Zhu, Jiayuan and Wang, Jiaye and Jin, Yueming , booktitle=. Agent-Memory Protocol: A Privacy-Focused Protocol for

    work page

  7. [7]

    Bagdasarian, Eugene and Yi, Ren and Ghalebikesabi, Sahra and Kairouz, Peter and Gruteser, Marco and Oh, Sewoong and Balle, Borja and Ramage, Daniel , booktitle=

    work page

  8. [8]

    Mireshghallah, Niloofar and Kim, Hyunwoo and Zhou, Xuhui and Tsvetkov, Yulia and Sap, Maarten and Shokri, Reza and Choi, Yejin , booktitle=. Can

Show all 34 references
  1. [9]

    2025 , howpublished=

    Introducing Apps in. 2025 , howpublished=

    2025

  2. [10]

    2026 , note=

    About Personalization with Connected Apps , howpublished=. 2026 , note=

    2026

  3. [11]

    Findings of the Association for Computational Linguistics: EMNLP 2025 , pages=

    Safeguarding Privacy of Retrieval Data against Membership Inference Attacks: Is This Query Too Close to Home? , author=. Findings of the Association for Computational Linguistics: EMNLP 2025 , pages=

    2025

  4. [12]

    arXiv preprint arXiv:2412.04697 , year=

    Privacy-Preserving Retrieval Augmented Generation with Differential Privacy , author=. arXiv preprint arXiv:2412.04697 , year=

  5. [13]

    arXiv preprint arXiv:2602.14374 , year=

    Differentially Private Retrieval-Augmented Generation , author=. arXiv preprint arXiv:2602.14374 , year=

  6. [14]

    Proceedings of the Annual IEEE Symposium on Foundations of Computer Science , pages=

    Mechanism Design via Differential Privacy , author=. Proceedings of the Annual IEEE Symposium on Foundations of Computer Science , pages=

  7. [15]

    Foundations and Trends in Theoretical Computer Science , volume=

    The Algorithmic Foundations of Differential Privacy , author=. Foundations and Trends in Theoretical Computer Science , volume=

  8. [16]

    Proceedings of the International Conference on Foundations of Software Science and Computational Structures , pages=

    On the Foundations of Quantitative Information Flow , author=. Proceedings of the International Conference on Foundations of Software Science and Computational Structures , pages=

  9. [17]

    Proceedings of the IEEE Computer Security Foundations Symposium , pages=

    Measuring Information Leakage Using Generalized Gain Functions , author=. Proceedings of the IEEE Computer Security Foundations Symposium , pages=

  10. [18]

    Zhong, Wanjun and Guo, Lianghong and Gao, Qiqi and Ye, He and Wang, Yanlin , booktitle=

  11. [19]

    and Xu, Ruifeng and Wong, Kam-Fai , booktitle=

    Du, Yiming and Wang, Bingbing and He, Yang and Liang, Bin and Wang, Baojun and Li, Zhongyang and Gui, Lin and Pan, Jeff Z. and Xu, Ruifeng and Wong, Kam-Fai , booktitle=

  12. [20]

    Huang, Zhaopei and Dai, Qifeng and Wu, Guozheng and Wu, Xiaopeng and Li, Xubin and Ge, Tiezheng and Wang, Wenxuan and Jin, Qin , booktitle=

  13. [21]

    Dai, Renke and Hu, Hebin and Zhang, Jiahui and Kang, Yilin and Tan, Ah-Hwee , booktitle=

  14. [22]

    and Stoica, Ion and Gonzalez, Joseph E

    Packer, Charles and Wooders, Sarah and Lin, Kevin and Fang, Vivian and Patil, Shishir G. and Stoica, Ion and Gonzalez, Joseph E. , journal=. 2023 , url=

    2023

  15. [23]

    Proceedings of the Annual ACM Symposium on User Interface Software and Technology , pages=

    Generative Agents: Interactive Simulacra of Human Behavior , author=. Proceedings of the Annual ACM Symposium on User Interface Software and Technology , pages=

  16. [24]

    Proceedings of the Annual Meeting of the Association for Computational Linguistics , pages=

    In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents , author=. Proceedings of the Annual Meeting of the Association for Computational Linguistics , pages=

  17. [25]

    Shao, Yijia and Li, Tianshi and Shi, Weiyan and Liu, Yanchen and Yang, Diyi , booktitle=

  18. [26]

    Privacy in Action: Towards Realistic Privacy Mitigation and Evaluation for

    Wang, Shouju and Yu, Fenglin and Liu, Xirui and Qin, Xiaoting and Zhang, Jue and Lin, Qingwei and Zhang, Dongmei and Rajmohan, Saravan , booktitle=. Privacy in Action: Towards Realistic Privacy Mitigation and Evaluation for

  19. [27]

    Hello Again!

    Li, Hao and Yang, Chenghao and Zhang, An and Deng, Yang and Wang, Xiang and Chua, Tat-Seng , booktitle=. Hello Again!

  20. [28]

    Kang, Jiazheng and Ji, Mingming and Zhao, Zhe and Bai, Ting , booktitle=. Memory

  21. [29]

    A-MEM: Agentic Memory for

    Xu, Wujiang and Liang, Zujie and Mei, Kai and Gao, Hang and Tan, Juntao and Zhang, Yongfeng , journal=. A-MEM: Agentic Memory for. 2025 , url=

    2025

  22. [30]

    A Persona-Aware

    Liu, Dongshuo and Wu, Zhijing and Song, Dandan and Huang, Heyan , booktitle=. A Persona-Aware

  23. [31]

    Proceedings of the International Conference on Machine Learning , pages=

    The Composition Theorem for Differential Privacy , author=. Proceedings of the International Conference on Machine Learning , pages=

  24. [32]

    Proceedings of the International Conference on Neural Information Processing Systems , pages=

    Privacy Odometers and Filters: Pay-as-You-Go Composition , author=. Proceedings of the International Conference on Neural Information Processing Systems , pages=

  25. [33]

    Medical Question Answering Datasets , author=

  26. [34]

    Proceedings of the Annual Meeting of the Association for Computational Linguistics , pages=

    Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset , author =. Proceedings of the Annual Meeting of the Association for Computational Linguistics , pages=

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