Pith. sign in

REVIEW 4 major objections 6 minor 8 cited by

Collaborative Memory: Multi-User Memory Sharing in LLM Agents with Dynamic Access Control

T0 review · 4 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This paper introduces a framework for multi-user, multi-agent LLM memory with dynamic, asymmetric access control, and reports resource savings of up to 61% from cross-user sharing.

desk verdict Useful first formalization of multi-user, multi-agent memory sharing with real efficiency gains, but the 'provable' access-control claim is best-effort without a deterministic enforcement layer. read the letter →

arxiv 2505.18279 v1 pith:3TUOJQJX submitted 2025-05-23 cs.MA cs.AIcs.CLcs.LG

Alireza Rezazadeh , Zichao Li , Ange Lou , Yuying Zhao , Wei Wei , Yujia Bao This is my paper
classification cs.MAcs.AIcs.CLcs.LG
keywords multi-usermemoryLLMagentsaccesscontroldynamicpermissionsbipartitegraphsharedprovenancemulti-agentsystems
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

Collaborative Memory sets out to close a gap in LLM-agent memory: persistent memory helps single agents, but multi-user settings need to share knowledge across users while respecting each user's and agent's rights, which change over time. The paper's claim is that a two-tier memory, private and shared, combined with immutable provenance on every fragment and policy-driven read/write transformations, can deliver both sharing and control. The headline empirical result is a resource-usage reduction of up to 61% at 50% query overlap and 59% at 75% overlap relative to isolated memory, with accuracy maintained. A sympathetic reader would care because the alternative is either redundant queries or unrestricted sharing, and this framework proposes a middle path with an audit trail.

What carries the argument

The load-bearing object is the dynamic bipartite access graph, two graphs $G_{UA}(t)\subseteq U\times A$ and $G_{AR}(t)\subseteq A\times R$ that record, at each time $t$, who may invoke which agent and which agent may touch which resource. Around it sit the two-tier memory ($\mathcal{M}^{\text{private}}\cup \mathcal{M}^{\text{shared}}$), immutable provenance attributes on each fragment, and the read/write policy pair $\pi^{\text{read}}_{u,a,t}$, $\pi^{\text{write/private}}_{u,a,t}$, $\pi^{\text{write/shared}}_{u,a,t}$. The graphs define the admissible memory set $M(u,a,t)$ via Eq. (3), the write policies transform outputs into de-identified fragments, and the read policy projects the admissible set into the view an agent actually sees; together they turn a permission system into a memory filter.

What would settle it

Run the dynamic-access scenario with a revoked user asking the same questions it asked before revocation; if any retrieved memory fragment was created by an agent the user can no longer invoke, or any answer token reproduces content from a fragment whose provenance includes that agent, the claimed enforcement is falsified. On the efficiency side, repeating the 50%-overlap experiment while counting knowledge-base calls per query would settle whether the 61% reduction reproduces.

Watch

Extended reading notes

Core claim

In the paper's own terms, the central discovery is that access control and collaboration need not be opposed in multi-agent memory: once permissions are stored as two dynamic bipartite graphs, user-to-agent and agent-to-resource, and every memory fragment carries creation time, contributing user, contributing agents, and accessed resources, the set of fragments any agent may read is $M(u,a,t) = \{m \mid A(m)\subseteq A(u,t) \wedge R(m)\subseteq R(a,t)\}$. Read policies filter and transform that set into a view, write policies decide what enters private or shared memory and how it is redacted, and immutable provenance enables retrospective permission checks. The paper reports that over three scenarios, full sharing, asymmetric privileges, and permissions granted then revoked, the framework reduces repeated resource calls while agents access only what the graph allows, and it claims provable adherence to these time-varying policies.

Load-bearing premise

The privacy and auditability guarantee stands or falls on the premise that provenance is recorded correctly at write time and that the LLM-driven transformation policies actually strip user-specific detail out of every shared fragment; the paper concedes in its limitations that these models can sometimes hallucinate or breach policy.

Editorial extensions

If this is right

  • In a fully collaborative workload, sharing memory reduces resource utilization by up to 61% at 50% query overlap and 59% at 75% overlap compared with isolated memory, while accuracy stays above 0.90.
  • Even when users have asymmetric privileges, partial sharing lowers agent and knowledge-base calls because intermediate insights from one user are reused by users with matching access.
  • When permissions are granted and then revoked, accuracy tracks available access, yet resource calls keep falling because stored fragments replace repeated external retrieval; the access matrices show only granted agents and resources are used.
  • Because every fragment records provenance and every read/write is policy-conditioned, memory operations can be audited retroactively against the permissions in effect at access time.
  • The framework is modular: other memory structures, such as hierarchical tree memory, can be plugged into the same graph-plus-provenance substrate.

Reading between the lines

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

  • The efficiency result probably does not depend on the privacy machinery being perfect: even if provenance or redaction fails, storing and reusing fragments should still cut redundant calls, so the 61% figure is a claim about caching plus filtering, not about security.
  • The privacy guarantee, by contrast, is only as strong as the LLM's redaction step; an adversary that can prompt a shared-memory agent with many paraphrases might in principle reconstruct user-specific details that a transformation policy failed to strip, which the paper does not test.
  • A natural extension is to make provenance verification cryptographic or deterministic rather than relying on stored metadata alone, and to define explicit revocation semantics, what happens to fragments already served before an edge is removed, which the retrospective-audit design leaves open.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper proposes Collaborative Memory, a framework for multi-user, multi-agent LLM systems that maintains private and shared memory tiers, attaches provenance attributes to memory fragments, and enforces read/write policies conditioned on dynamic bipartite access graphs over users, agents, and resources. The authors formalize the admissible memory set in Eq. (3), describe an implementation with GPT-4o-based agents, and present three empirical scenarios (fully collaborative, asymmetric, and dynamically evolving access) in which collaborative memory reduces resource utilization relative to isolated memory. The central claims are safe, efficient, and interpretable sharing with 'provable adherence to asymmetric, time-varying policies and full auditability of memory operations.'

Significance. The problem is timely and important: memory sharing across users with asymmetric and time-varying permissions is a practical concern for deployed multi-agent systems, and the paper offers a clean conceptual framework with explicit bipartite access graphs, two-tier memory, and separable read/write policies. The empirical demonstrations in three scenarios are a useful first step, and the paper is unusually transparent about limitations, including the fallibility of LLM-based provenance and redaction. If the central claims are appropriately scoped and the evaluation hardened, the framework could be a solid foundation for future work on permission-aware collaborative memory; as written, the gap between the 'provable' language and the best-effort enforcement is too wide for the claims to stand.

major comments (4)
  1. [Abstract, §3.2, §4, §6] The claim of 'provable adherence to asymmetric, time-varying policies' is not supported. Eq. (3) defines the admissible memory set as fragments whose provenance attributes satisfy the current access graph, but §4 states that fragments are annotated with LLM-generated provenance, and §6 concedes that 'the probabilistic nature of these models can lead to occasional hallucinations or policy breaches.' A filter over fallible metadata provides a conditional, not a provable, guarantee. Please either remove 'provable' and state that adherence is best-effort under correct provenance, or add a deterministic provenance layer that records the actual user/agent/resource from the API-call log and a redaction-verification step.
  2. [§5.1, §5.2, Figures 3–4] The headline efficiency results are not statistically supported. Figures 3 and 4 show single evaluation curves without error bars or significance tests, and the reported reductions in resource usage (up to 61% at 50% overlap and 59% at 75% overlap) come from a single run. Moreover, §5.2 measures no accuracy or quality metric at all, so it is unknown whether the efficiency gain of asymmetric sharing preserves response correctness. Please report variance across repeated runs or across users, and add at least an LLM-judge or human quality check for Scenario 2.
  3. [§5.3, Figure 6] The statement that the access matrix 'confirms strict adherence to access control policies' is circular. Since the system only retrieves fragments that pass the filter in Eq. (3), usage counts falling within granted cells is inevitable by construction. The experiment does not test the actual threat: whether a fragment containing information from a resource outside the current user's permissions can be admitted because of incorrect provenance or failed redaction. Please add a leakage test with adversarially mis-annotated fragments or a red-team-style probe.
  4. [§5.3] The resource-usage reduction in the dynamic scenario is not compared against an isolated-memory baseline. Since the same 100 queries are reused at every time step, even a system with no cross-user sharing would show declining external calls as its local memory grows. Without an isolated baseline, the observed drop cannot be attributed to collaborative memory.
minor comments (6)
  1. [§4] Typo: 'the conversational trace are mapped' should be 'the conversational traces are mapped.'
  2. [Figure 3 caption] Typo: 'while remains confined' should be 'while remaining confined.'
  3. [§5.2] Typo: 'we focuses on resource utilization' should be 'we focus on resource utilization.'
  4. [§3.4, Eqs. (4)–(5)] The notation M_private(u,+1) and M_shared(u,+1) is unclear; this should be the time index, e.g., M_private(u,t+1), to match the rest of the formalism.
  5. [§5.3, Figure 6] The figure is labeled an 'access matrix,' but it actually displays usage counts; please rename it 'usage matrix' to avoid ambiguity with the access graphs defined in §3.1.
  6. [§3.3 and §4] The experiments use only the simple read policy, yet the framework's claims about transformed read views are not tested; please state this explicitly in the main text.

Circularity Check

1 steps flagged · score 6.0 of 10

The 'provable adherence' guarantee is defined into the read filter (Eq. 3), so the Fig. 6 confirmation is tautological; the empirical efficiency results are otherwise self-contained.

  1. self definitional [Section 3.2, Eq. (3); Section 4, Policy Instantiation; Section 5.3, Fig. 6]
    "M(u, a, t) := { m ∈ M | A(m) ⊆ A(u, t) ∧ R(m) ⊆ R(a, t) }. ... The access matrix in Figure 6 further confirms that users only access agents and resources explicitly granted by the graph, ensuring strict adherence to access control policies."

    The claimed 'strict adherence' is not an independent result; it is the definition of the retrievable set. Eq. (3) admits exactly the fragments whose provenance attributes are subsets of the current permission sets, and the experiments adopt the simple read policy, which 'returns admissible fragments verbatim.' Therefore Figure 6's observation that usage counts lie inside granted cells is entailed by the filter and cannot fail.

full rationale

The paper's efficiency contribution is genuinely empirical: Section 5.1 reports resource-usage reductions of up to 61% at 50% overlap and 59% at 75% overlap against an isolated-memory baseline, and these reductions are measured rather than fitted. The read/write policy parameters (k_user, k_cross, similarity threshold) are configured, not calibrated to force the reported outcomes. The only self-citation, MemTree [14], is used as an example of an alternative memory structure and an optional integration target; no load-bearing derivation rests on it, and there is no imported uniqueness theorem or ansatz smuggled through self-citation. The central circular element is the access-control guarantee. Eq. (3) makes 'adherence to asymmetric, time-varying policies' true by construction: the accessible set is defined as the set of fragments satisfying the current permission constraints. The later statement that Figure 6 'confirms' users only access granted agents and resources is therefore a tautology of the filtering rule rather than a test of the system. Moreover, the framework's safety claim additionally relies on LLM-generated provenance being correct and on LLM-based transformation prompts reliably redacting user-specific information; the paper's own Limitations section concedes these steps are probabilistic and fallible. This makes the advertised 'provable adherence' an assumption-relative definitional property rather than an independently established result. Because the empirical efficiency results and the architectural formulation are self-contained and externally meaningful, this is not a fully circular paper. But the headline guarantee partially reduces to its own enforcement definition, and the claimed confirmation is unfalsifiable by design. That warrants a score of 6 rather than a higher score.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

The framework does not introduce new physical entities. The main non-trivial assumptions are about the reliability of provenance metadata and the trustworthiness of LLM-based transformation policies. The free parameters are hyperparameters of the retrieval and graph-generation processes, not fitted constants, but their values are arbitrary and not justified by sensitivity analysis.

free parameters (4)
  • kuser (user-specific tier size) = 10 (Scenario 1 and 3), 20 (Scenario 2)
    Number of top-k user memory fragments retrieved per subquery; chosen by hand, no sensitivity analysis reported.
  • kcross (cross-user tier size) = 10 (Scenario 1 and 3), 20 (Scenario 2)
    Number of top-k shared memory fragments retrieved; chosen by hand, no sensitivity analysis.
  • similarity threshold = 0.1 (Scenario 3)
    Threshold for memory fragment retrieval; chosen by hand, effect on results not studied.
  • Bernoulli trial probability p = 0.2
    Probability used to add or remove edges in the dynamic access graph; chosen by hand, no alternative values tested.
assumptions (3)
  • domain assumption Memory fragment provenance attributes (U(m), A(m), R(m), T(m)) are accurate and immutable.
    Section 3.2 defines fragments with these attributes, and Eq. (3) relies on them to filter access. If provenance is wrong or tampered with, the access-control guarantee fails.
  • domain assumption LLM-based read and write policies follow their system prompts and do not leak information beyond the access constraints.
    The transformation write policy uses LLM prompts to redact or anonymize fragments (Appendix B.2). Section 6 explicitly states that LLMs can hallucinate or breach policies, so this assumption is not guaranteed.
  • domain assumption The bipartite access graphs G_UA(t) and G_AR(t) correctly reflect the real-world permissions at every time step.
    The framework takes these graphs as input (Section 3.1); if a user has legitimate access to a resource not captured in the graph, the memory system will incorrectly deny or grant access.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Collaborative Memory: Multi-User Memory Sharing in LLM Agents with Dynamic Access Control." pith.science (2026). https://pith.science/paper/3TUOJQJX

@misc{pith2026250518279,
  author       = {Pith},
  title        = {Pith review of: Collaborative Memory: Multi-User Memory Sharing in LLM Agents with Dynamic Access Control},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3TUOJQJX}},
  note         = {Machine review of arXiv:2505.18279}
}
read the original abstract

Complex tasks are increasingly delegated to ensembles of specialized LLM-based agents that reason, communicate, and coordinate actions-both among themselves and through interactions with external tools, APIs, and databases. While persistent memory has been shown to enhance single-agent performance, most approaches assume a monolithic, single-user context-overlooking the benefits and challenges of knowledge transfer across users under dynamic, asymmetric permissions. We introduce Collaborative Memory, a framework for multi-user, multi-agent environments with asymmetric, time-evolving access controls encoded as bipartite graphs linking users, agents, and resources. Our system maintains two memory tiers: (1) private memory-private fragments visible only to their originating user; and (2) shared memory-selectively shared fragments. Each fragment carries immutable provenance attributes (contributing agents, accessed resources, and timestamps) to support retrospective permission checks. Granular read policies enforce current user-agent-resource constraints and project existing memory fragments into filtered transformed views. Write policies determine fragment retention and sharing, applying context-aware transformations to update the memory. Both policies may be designed conditioned on system, agent, and user-level information. Our framework enables safe, efficient, and interpretable cross-user knowledge sharing, with provable adherence to asymmetric, time-varying policies and full auditability of memory operations.

Figures

Figures reproduced from arXiv: 2505.18279 by the authors.

Figure 1
Figure 1. Illustration of multi-user, multi-agent collaboration. Scenario 1 (top-left): A fully col [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Left: Illustration of the dynamic multi-user environment, where multiple users collaborate [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Scenario 1 (Fully Collaborative Memory). Performance of [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Scenario 2 (Asymmetric Collaborative Memory). Resource usage with and without [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Scenario 3 (Dynamically Evolving Collaborative Memory). System performance over eight [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Scenario 3 (Dynamically Evolving Collaborative Memory). Agent and resource usage [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: From user query to response: the detailed pipeline. The [PITH_FULL_IMAGE:figures/full_fig_p014_7.png]
Figure 8
Figure 8. Figure 8: Performance by query type over five time bins: (left) average accuracy, (center) average [PITH_FULL_IMAGE:figures/full_fig_p019_8.png]
Figure 9
Figure 9. Figure 9: System pipeline for Scenario 2, illustrating the collaboration among users, agents, and [PITH_FULL_IMAGE:figures/full_fig_p020_9.png]
Figure 10
Figure 10. Figure 10: Color-coded access graph illustrating hierarchical user-to-agent and agent-to-resource [PITH_FULL_IMAGE:figures/full_fig_p026_10.png]
Figure 11
Figure 11. Figure 11: Complete agent and resource usage across user queries from different categories. Yellow [PITH_FULL_IMAGE:figures/full_fig_p038_11.png]

Discussion (0). Sign in to comment.

Forward citations

Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MemTX: Transactional Belief Commit for Stateful Agent Memory

    cs.AI 2026-07 conditional novelty 7.0 of 10

    Staging agent-memory writes through a validate-and-commit pipeline with maturity-gated irreversible actions and typed cascading repair yields zero realized downstream harm on five LLM backbones, where eight baselines ...

  2. Relational Priors as Convergence Pressure in LLM-Based Multi-Agent Systems

    cs.CL 2026-08 conditional novelty 6.0 of 10

    Explicit positive or negative relationship cues in multi-agent prompts act mainly as convergence pressure, increasing agreement without reliably improving answer correctness.

  3. MemForest: An Efficient Agent Memory System with Hierarchical Temporal Indexing

    cs.DB 2026-05 unverdicted novelty 6.0 of 10

    MemForest speeds up agent memory writes about 6x by parallelizing extraction and using a hierarchical temporal index, while matching or beating stateful baselines on LongMemEval-S.

  4. Detailed analysis of possible new-physics effects in the semileptonic decay $B_s \to D_s^{(*)}\tau\bar{\nu}$

    hep-ph 2026-03 unverdicted novelty 4.0 of 10

    Constraints on beyond-SM Wilson coefficients in B_s → D_s(*) τ ν̄ are derived from data using covariant-quark-model form factors, with full observable predictions for future experiments.

  5. Secure Multi-LLM Agentic AI and Agentification for Edge General Intelligence by Zero-Trust: A Survey

    cs.NI 2025-08 conditional novelty 4.0 of 10

    A survey proposing zero-trust architecture for multi-LLM systems in edge computing, with a taxonomy of model- and system-level defenses and a conceptual framework.

  6. Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration

    cs.NI 2025-07 conditional novelty 4.0 of 10

    A survey of multi-LLM systems in edge computing, covering architectures, enabling technologies, trust mechanisms, applications, and open datasets for edge general intelligence.

  7. Memory as a Service (MaaS): Purpose-Bound Memory Mediation for Cooperative Agents

    cs.HC 2025-06 conditional novelty 4.0 of 10

    A position paper arguing that agent memory should be reframed from private local state to governed, purpose-bound services, illustrated by a design space but not validated.

  8. Toward Efficient Agents: Memory, Tool learning, and Planning

    cs.AI 2026-01 conditional novelty 3.0 of 10

    A survey that organizes efficiency techniques for LLM agents into memory, tool learning, and planning, and consolidates benchmarks and metrics for measuring cost-performance trade-offs.

Reference graph

Works this paper leans on

53 extracted references · 33 canonical work pages · cited by 8 Pith papers

  1. [1]

    Reflective multi-agent collaboration based on large language models.Advances in Neural Information Processing Systems, 37:138595–138631, 2024

    Xiaohe Bo, Zeyu Zhang, Quanyu Dai, Xueyang Feng, Lei Wang, Rui Li, Xu Chen, and Ji-Rong Wen. Reflective multi-agent collaboration based on large language models.Advances in Neural Information Processing Systems, 37:138595–138631, 2024

    2024

  2. [2]

    Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors in agents.arXiv preprint arXiv:2308.10848, 2(4):6, 2023

    Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chen Qian, Chi-Min Chan, Yujia Qin, Yaxi Lu, Ruobing Xie, et al. Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors in agents.arXiv preprint arXiv:2308.10848, 2(4):6, 2023

    arXiv 2023

  3. [3]

    From local to global: A graph rag approach to query-focused summarization.arXiv preprint arXiv:2404.16130, 2024

    Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, Dasha Metropolitansky, Robert Osazuwa Ness, and Jonathan Larson. From local to global: A graph rag approach to query-focused summarization.arXiv preprint arXiv:2404.16130, 2024

    arXiv 2024

  4. [4]

    Memory sharing for large language model based agents.arXiv preprint arXiv:2404.09982, 2024

    Hang Gao and Yongfeng Zhang. Memory sharing for large language model based agents.arXiv preprint arXiv:2404.09982, 2024

    arXiv 2024

  5. [5]

    Large language model based multi-agents: A survey of progress and challenges.arXiv preprint arXiv:2402.01680, 2024

    Taicheng Guo, Xiuying Chen, Yaqi Wang, Ruidi Chang, Shichao Pei, Nitesh V Chawla, Olaf Wiest, and Xiangliang Zhang. Large language model based multi-agents: A survey of progress and challenges.arXiv preprint arXiv:2402.01680, 2024

    arXiv 2024

  6. [6]

    Hipporag: Neurobiologically inspired long-term memory for large language models

    Bernal Jiménez Gutiérrez, Yiheng Shu, Yu Gu, Michihiro Yasunaga, and Yu Su. Hipporag: Neurobiologically inspired long-term memory for large language models. InThe Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024

    2024

  7. [7]

    Guide to attribute based access control (abac) definition and considerations (draft).NIST special publication, 800(162):1–54, 2013

    Vincent C Hu, David Ferraiolo, Rick Kuhn, Arthur R Friedman, Alan J Lang, Margaret M Cogdell, Adam Schnitzer, Kenneth Sandlin, Robert Miller, Karen Scarfone, et al. Guide to attribute based access control (abac) definition and considerations (draft).NIST special publication, 800(162):1–54, 2013

    work page 2013

  8. [8]

    MIT press, 1995

    Edwin Hutchins.Cognition in the Wild. MIT press, 1995

    1995

Show all 53 references
  1. [9]

    Retrieval-augmented generation for knowledge-intensive nlp tasks.ArXiv, abs/2005.11401, 2020

    Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Kuttler, Mike Lewis, Wen tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. Retrieval-augmented generation for knowledge-intensive nlp tasks.ArXiv, abs/2005.11401, 2020

    2005 arXiv

  2. [10]

    Think-in-memory: Recalling and post-thinking enable llms with long-term memory.ArXiv, abs/2311.08719, 2023

    Lei Liu, Xiaoyan Yang, Yue Shen, Binbin Hu, Zhiqiang Zhang, Jinjie Gu, and Guannan Zhang. Think-in-memory: Recalling and post-thinking enable llms with long-term memory.ArXiv, abs/2311.08719, 2023

    2023 arXiv

  3. [11]

    Enhancing llm intelligence with arm-rag: Auxiliary rationale memory for retrieval augmented generation.ArXiv, abs/2311.04177, 2023

    Eric Melz. Enhancing llm intelligence with arm-rag: Auxiliary rationale memory for retrieval augmented generation.ArXiv, abs/2311.04177, 2023

    2023 arXiv

  4. [12]

    Deep research faq

    OpenAI. Deep research faq. https://help.openai.com/en/articles/ 10500283-deep-research-faq?utm_source=chatgpt.com#h_7419a1dd48, 2025

    2025

  5. [13]

    Memgpt: Towards llms as operating systems.arXiv preprint arXiv:2310.08560, 2023

    Charles Packer, Vivian Fang, Shishir_G Patil, Kevin Lin, Sarah Wooders, and Joseph_E Gonza- lez. Memgpt: Towards llms as operating systems.arXiv preprint arXiv:2310.08560, 2023

    2023 arXiv

  6. [14]

    From isolated conversations to hierarchi- cal schemas: Dynamic tree memory representation for llms.arXiv preprint arXiv:2410.14052, 2024

    Alireza Rezazadeh, Zichao Li, Wei Wei, and Yujia Bao. From isolated conversations to hierarchi- cal schemas: Dynamic tree memory representation for llms.arXiv preprint arXiv:2410.14052, 2024. 10

    2024 arXiv

  7. [15]

    The nist model for role-based access control: towards a unified standard

    Ravi Sandhu, David Ferraiolo, Richard Kuhn, et al. The nist model for role-based access control: towards a unified standard. InACM workshop on Role-based access control, number 344287.344301, 2000

    2000

  8. [16]

    Toolformer: Language models can teach themselves to use tools.ArXiv, abs/2302.04761, 2023

    Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettle- moyer, Nicola Cancedda, and Thomas Scialom. Toolformer: Language models can teach themselves to use tools.ArXiv, abs/2302.04761, 2023

    2023 arXiv

  9. [17]

    Reflexion: language agents with verbal reinforcement learning

    Noah Shinn, Federico Cassano, Beck Labash, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: language agents with verbal reinforcement learning. InNeural Information Processing Systems, 2023

    2023

  10. [18]

    Multihop-rag: Benchmarking retrieval-augmented generation for multi-hop queries.arXiv preprint arXiv:2401.15391, 2024

    Yixuan Tang and Yi Yang. Multihop-rag: Benchmarking retrieval-augmented generation for multi-hop queries.arXiv preprint arXiv:2401.15391, 2024

    2024 arXiv

  11. [20]

    Khanh-Tung Tran, Dung Dao, Minh-Duong Nguyen, Quoc-Viet Pham, Barry O’Sullivan, and Hoang D. Nguyen. Multi-agent collaboration mechanisms: A survey of llms.ArXiv, abs/2501.06322, 2025

    2025 arXiv

  12. [21]

    Sciqag: A framework for auto-generated science question answering dataset with fine-grained evaluation.arXiv preprint arXiv:2405.09939, 2024

    Yuwei Wan, Yixuan Liu, Aswathy Ajith, Clara Grazian, Bram Hoex, Wenjie Zhang, Chunyu Kit, Tong Xie, and Ian Foster. Sciqag: A framework for auto-generated science question answering dataset with fine-grained evaluation.arXiv preprint arXiv:2405.09939, 2024

    2024 arXiv

  13. [22]

    Autogen: Enabling next-gen llm applications via multi-agent conversation.arXiv preprint arXiv:2308.08155, 2023

    Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, et al. Autogen: Enabling next-gen llm applications via multi-agent conversation.arXiv preprint arXiv:2308.08155, 2023

    2023 arXiv

  14. [23]

    Multi-llm collaborative search for complex problem solving.arXiv preprint arXiv:2502.18873, 2025

    Sen Yang, Yafu Li, Wai Lam, and Yu Cheng. Multi-llm collaborative search for complex problem solving.arXiv preprint arXiv:2502.18873, 2025

    2025 arXiv

  15. [24]

    React: Synergizing reasoning and acting in language models.ArXiv, abs/2210.03629, 2022

    Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models.ArXiv, abs/2210.03629, 2022

    2022 arXiv

  16. [25]

    write–then–read

    Wanjun Zhong, Lianghong Guo, Qi-Fei Gao, He Ye, and Yanlin Wang. Memorybank: Enhancing large language models with long-term memory.ArXiv, abs/2305.10250, 2023. 11 Appendix Arrangement The Appendix is organized as follows. •Section § A: Discussion of the Broad Impact of Collabo...

    2023 arXiv

  17. [26]

    The market shows a strong p r e f e r e n c e for p e r s o n a l i z e d sk in car e s o l u t i o n s and tech - driven pr od uc ts

    ** Germany **: C o n s u m e r s are i n c r e a s i n g l y i n t e r e s t e d in health and wellness , sustainability , and eco - fr ie nd ly p ro duc ts . The market shows a strong p r e f e r e n c e for p e r s o n a l i z e d sk in car e s o l u t i o n s and tech - dri...

  18. [27]

    ** N e t h e r l a n d s **:

  19. [28]

    These c o u n t r i e s reflect high - demand markets due to their s pec if ic trends and c on su me r p r e f e r e n c e s

    ** S w i t z e r l a n d **: ... These c o u n t r i e s reflect high - demand markets due to their s pec if ic trends and c on su me r p r e f e r e n c e s . They align with broader Eu ro pe an trends such as sustainability , minimalism , personalization , and clean beauty ,...

  20. [29]

    - **Inflation Rate**: Stabilizing around 2-3%

    **Germany**: - **GDP Growth**: Stable growth rate around 1-2%. - **Inflation Rate**: Stabilizing around 2-3%. - **Unemployment Rate**: Low, enhancing consumer spending capacity. - **Purchasing Power**: High disposable income, indicating potential for premium product sales

  21. [30]

    **Switzerland**: .. ### Financial Viability and Profitability Assessment: - **High Disposable Income**: Countries like Germany, Sweden, France, the UK, Netherlands, and Switzerland have high disposable incomes, indicating strong potential for premium skincare product sales. - ...

  22. [31]

    Central location within Europe facilitates access to various markets

    **Germany**: - **Infrastructure**: Highly efficient with robust road, rail, and port systems. Central location within Europe facilitates access to various markets. - **Regulatory Environment**: Supportive of business operations with strong manufacturing and distribution networ...

  23. [32]

    **Market Entry Strategy**: - **Germany**: Serve as a central hub for distribution across Europe, leveraging its efficient infrastructure and strong regulatory support

  24. [33]

    **Product Strategy**:

  25. [34]

    **Operational Strategy**: ... By focusing on these high-demand regions and aligning product offerings with consumer preferences and economic conditions, the entry into European skincare markets is positioned for success, with potential profitability driven by strong purchasing...

    2023

  26. [35]

    **Consumer Preferences:**

    **Age Groups:** ... **Consumer Preferences:**

  27. [36]

    **Influence of Digital Platforms:**

    **Ingredient Consciousness:** ... **Influence of Digital Platforms:** ... **Challenges:** ... **Conclusion:** The European skincare market is diverse and dynamic, with an increasing focus on products that prioritize health, sustainability, and ethical standards. Brands that ad...

  28. [37]

    - **Demand Growth:** Skincare product demand is expected to grow at approximately 5-7% annually due to increased consumer awareness and preference for skincare

    **Revenue Forecast:** - **Market Demand Size:** Assume penetration into markets like Germany, France, and the UK (collectively accounting for a large portion of the EU skincare market). - **Demand Growth:** Skincare product demand is expected to grow at approximately 5-7% annu...

  29. [38]

    options": [

    **ROI Analysis:** ... **Forecast Summary:** If executed well with strong marketing, streamlined supply chain, and effective distribution, entering high-demand European markets could achieve substantial revenue growth and profitability. Gradual market share increases and scalin...

  30. [39]

    It has central access to the European market, making it a pivotal hub for logistics and supply chain operations

    **Germany**: Germany is known for its highly efficient infrastructure system, including roads, railways, and ports. It has central access to the European market, making it a pivotal hub for logistics and supply chain operations. Its regulatory framework generally supports busi...

  31. [40]

    expression

    **Spain**: ... This ranking reflects the complexity and efficiency of the logistics and supply chain sector in each country, considering their ability to manage and fulfill demands across high-demand regions efficiently. strategic_computation input: { "expression": "Calculate ...

  32. [41]

    - Assume the cost per unit is C

    **Initial Setup**: - Assume the original selling price is P. - Assume the cost per unit is C. - Assume the initial quantity sold is Q

  33. [42]

    **Initial Profit Margin**: 24 - Initial revenue: R = P * Q - Initial total cost: T = C * Q - Initial profit: Profit = R - T = (P * Q) - (C * Q) = (P - C) * Q - Initial profit margin: Profit Margin = Profit / R = ((P - C) * Q) / (P * Q) = (P - C) / P

  34. [43]

    **Price Increase**: - New selling price: P’ = P * 1.05

  35. [44]

    **Revenue with Low Demand Elasticity**: - With low demand elasticity, quantity sold Q’ ~= Q - New revenue: R’ = P’ * Q’ = (P * 1.05) * Q = 1.05 * P * Q

  36. [45]

    **New Profit**: - New profit: Profit’ = R’ - T = 1.05 * P * Q - C * Q = (1.05P - C) * Q

  37. [46]

    user_1

    **New Profit Margin**: - Profit Margin’ = Profit’ / R’ = ((1.05P - C) * Q) / (1.05 * P * Q) = (1.05P - C) / (1.05P) Thus, the new profit margin after a 5% price increase with constant costs and low demand elasticity is: Profit Margin’ = (1.05P - C) / (1.05P) Compared to the in...

  38. [49]

    For complex queries that span multiple domains, determine if sequential agent consultation is needed

  39. [50]

    When reformulating queries for specific agents, emphasize aspects relevant to their expertise

  40. [51]

    Craft a strategic growth plan for smart home devices in privacy-regulated markets

    When the task is complete, respond with COMPLETE aggregator Merges multi-agent outputs into a unified user response. You are an aggregator for a multi-agent system. Combine outputs from multiple specialized agents into a single, direct, and coherent response to the user query....

  41. [52]

    Identify the primary domain(s) of the query

  42. [53]

    Select the most appropriate specialized agent based on their expertise areas

  43. [54]

    For complex queries that span multiple domains, deter- mine if sequential agent consultation is needed

  44. [55]

    When reformulating queries for specific agents, empha- size aspects relevant to their expertise

  45. [56]

    write–then–read

    When the task is complete, respond with COMPLETE aggregator Merges multi- agent outputs into a unified user response. You are an aggregator for a multi-agent scientific knowl- edge system. Your role is to combine outputs from multi- ple specialized agents into a single, logica...

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.