Pith. sign in

REVIEW 2 major objections 2 minor 57 references

Literature on always-on LLM agents focuses more on accumulating and retrieving state than on governing, recovering, or relinquishing it.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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

T0 review · grok-4.3

2026-06-30 03:41 UTC pith:KBK2JASQ

load-bearing objection This survey codes 435 works to show literature bias toward state accumulation over governance in LLM agents and proposes AOEP-v0 as a pilot fix, but the coding methods stay opaque. the 2 major comments →

arxiv 2606.30306 v1 pith:KBK2JASQ submitted 2026-06-29 cs.MA cs.AI

Always-OnAgents:A Survey of Persistent Memory, State, and Governance in LLMAgents

Tianyu Ding , Aditya Nannapaneni , Bingfan Liu , Ling Zhang This is my paper
classification cs.MA cs.AI
keywords always-on agentspersistent stateLLM agentsstate managementevaluation protocolgovernancememory retrievalstate recovery
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper examines persistent-state systems in always-on agents, where future behavior depends on durable records such as memories, permissions, commitments, and audit trails. It codes 435 works along six axes—authority, scope, mutability, provenance, recoverability, and actionability—and maps state through a lifecycle of write, validate, organize, retrieve, act, update, forget, audit, and rollback. The survey finds heavier emphasis on accumulation and retrieval than on governance or recovery operations. To make the imbalance concrete, the authors introduce the Always-On Evaluation Protocol (AOEP-v0), which scores agents on state mutation and recovery obligations instead of answer quality alone. The agenda links the topic to databases, distributed systems, and machine unlearning.

Core claim

Across a 435-work coded corpus, treated as a scoped map rather than an exhaustive census, the literature concentrates more heavily on accumulating and retrieving state than on governing, recovering, or relinquishing it. The survey introduces the Always-On Evaluation Protocol (AOEP-v0), a pilot evaluation contract that makes these governance requirements concrete by scoring state mutation and recovery obligations rather than answer quality alone.

What carries the argument

Six diagnostic axes (authority, scope, mutability, provenance, recoverability, actionability) applied to each state item across a lifecycle of write, validate, organize, retrieve, act upon, update, forget, audit, and rollback.

Load-bearing premise

The six diagnostic axes and the described lifecycle stages adequately capture the key aspects of persistent state management in always-on agents and allow meaningful coding of the literature.

What would settle it

A re-coding of the same 435 works that finds roughly equal coverage of accumulation, governance, recovery, and relinquishment would falsify the reported concentration.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Agent evaluations must incorporate explicit checks for state recovery and rollback obligations rather than answer quality alone.
  • Systems need built-in mechanisms to forget or roll back state when permissions change or errors occur.
  • Provenance and audit records must be treated as first-class state items with the same management requirements as memories.
  • Shared state and externally committed effects require coordination protocols drawn from databases and distributed systems.

Where Pith is reading between the lines

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

  • Current agent benchmarks that ignore long-term state consistency would likely receive low AOEP-v0 scores.
  • Techniques from machine unlearning could be adapted to implement the forgetting stage for always-on agents.
  • Capability-based security models may offer concrete ways to enforce authority and scope axes on agent state.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 2 minor

Summary. The paper surveys always-on LLM agents as persistent-state systems whose behavior depends on durable records including memories, ledgers, permissions, and commitments. It analyzes a 435-work corpus through six diagnostic axes (authority, scope, mutability, provenance, recoverability, actionability) and a nine-stage lifecycle (write, validate, organize, retrieve, act, update, forget, audit, rollback). The central observation is that the literature concentrates on state accumulation and retrieval while under-emphasizing governance, recovery, and relinquishment. The authors introduce AOEP-v0, a pilot evaluation protocol that scores state mutation and recovery obligations rather than answer quality alone, and connect the agenda to databases, distributed systems, formal methods, capability security, and machine unlearning.

Significance. If the coding methodology is made rigorous, the observational map usefully identifies an imbalance in current research priorities and supplies a concrete evaluation contract (AOEP-v0) that could shift assessment practices away from answer quality alone. The explicit linkage to established fields (databases, formal methods, machine unlearning) is a constructive strength; the work is framed as a scoped map rather than a census, which appropriately limits its claims.

major comments (2)
  1. [corpus construction / abstract] The central claim of literature concentration rests on the coding of 435 works using the six axes and lifecycle stages. The manuscript provides no details on coding methodology, inter-rater reliability, exclusion criteria, or how borderline cases were resolved (see the section describing the corpus construction and the abstract). This information is load-bearing for the reliability of the reported imbalance.
  2. [AOEP-v0 introduction] AOEP-v0 is presented as a pilot protocol that scores state mutation and recovery obligations. No validation, pilot results, or comparison against existing benchmarks is reported, leaving the protocol's practical utility untested (see the section introducing AOEP-v0).
minor comments (2)
  1. [diagnostic axes and lifecycle] The six axes and lifecycle stages are introduced without an explicit justification or comparison to prior state-management taxonomies in the agent or database literature; a short related-work paragraph would clarify novelty.
  2. [corpus description] The paper states the corpus is 'treated as a scoped map rather than an exhaustive census' but does not specify the search strings, date range, or inclusion filters used to arrive at 435 works.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback, which highlights opportunities to improve methodological transparency and to better contextualize the proposed protocol. We address each major comment below, indicating planned revisions where appropriate. The manuscript is framed as a scoped map, which informs our responses.

read point-by-point responses

  1. Referee: The central claim of literature concentration rests on the coding of 435 works using the six axes and lifecycle stages. The manuscript provides no details on coding methodology, inter-rater reliability, exclusion criteria, or how borderline cases were resolved (see the section describing the corpus construction and the abstract). This information is load-bearing for the reliability of the reported imbalance.

    Authors: We agree that additional transparency on the corpus construction process is warranted to support the observational claims. In the revised manuscript we will insert a new subsection detailing the selection criteria for the 435 works, the procedure for applying the six diagnostic axes and nine-stage lifecycle, how borderline cases were handled through discussion among authors, and the steps taken to maintain consistency across codings. Although the work is explicitly positioned as a scoped map rather than a formal systematic review, these additions will allow readers to evaluate the reported patterns more rigorously. revision: yes

  2. Referee: AOEP-v0 is presented as a pilot protocol that scores state mutation and recovery obligations. No validation, pilot results, or comparison against existing benchmarks is reported, leaving the protocol's practical utility untested (see the section introducing AOEP-v0).

    Authors: We acknowledge that the current presentation of AOEP-v0 lacks any illustrative application or comparison to existing benchmarks. In revision we will augment the section with a brief worked example applying the protocol to two publicly described agent systems, thereby demonstrating its scoring mechanics in practice. A comprehensive validation study or head-to-head benchmark comparison lies outside the scope of this survey and is reserved for subsequent work; the protocol is offered as an initial contract rather than a fully validated instrument. revision: partial

Circularity Check

0 steps flagged

No significant circularity in survey and protocol proposal

full rationale

The paper is a literature survey that codes a 435-work corpus using six explicitly defined diagnostic axes and a lifecycle model, then reports an observational concentration on accumulation/retrieval versus governance/recovery. It introduces AOEP-v0 as a pilot evaluation contract motivated by that map. No equations, fitted parameters, predictions, self-definitional reductions, or load-bearing self-citations appear in the argument structure; the central claim is an external coding result treated as a scoped map rather than a derived theorem, rendering the work self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The analysis rests on domain assumptions about the sufficiency of the proposed axes and lifecycle for characterizing state in always-on agents; the AOEP-v0 is introduced as a new evaluation construct without independent evidence of its effectiveness.

axioms (2)
  • domain assumption The six diagnostic axes (authority, scope, mutability, provenance, recoverability, actionability) adequately capture the relevant properties of state items in always-on agents.
    Invoked when applying the axes to code the 435-work corpus.
  • domain assumption The listed lifecycle stages (written, validated, organized, retrieved, acted upon, updated, forgotten, audited, rolled back) represent the key operations on persistent state.
    Used to structure the survey's analysis of state handling.
invented entities (1)
  • Always-On Evaluation Protocol (AOEP-v0) no independent evidence
    purpose: To make governance requirements concrete by scoring state mutation and recovery obligations rather than answer quality alone.
    Introduced as a pilot evaluation contract in the abstract.

pith-pipeline@v0.9.1-grok · 5741 in / 1649 out tokens · 61301 ms · 2026-06-30T03:41:59.844455+00:00 · methodology

0 comments
read the original abstract

Always-on agents are systems whose future behavior depends on durable state accumulated across earlier interactions. We treat them as persistent-state systems: the operative system includes retrievable memories, but also task ledgers, permissions, credentials, commitments, provenance and audit records, shared state, trigger conditions, and externally committed effects linked to those records. The survey reads the literature through six diagnostic axes for each state item, authority, scope, mutability, provenance, recoverability, and actionability, and through a lifecycle in which state is written, validated, organized, retrieved, acted upon, updated, forgotten, audited, and sometimes rolled back. Across a 435-work coded corpus, treated as a scoped map rather than an exhaustive census, the literature concentrates more heavily on accumulating and retrieving state than on governing, recovering, or relinquishing it. We therefore introduce the Always-On Evaluation Protocol (AOEP-v0), a pilot evaluation contract that makes these governance requirements concrete by scoring state mutation and recovery obligations rather than answer quality alone. The resulting agenda connects always-on agents to databases, distributed systems, formal methods, capability security, and machine unlearning.

Figures

Figures reproduced from arXiv: 2606.30306 by Aditya Nannapaneni, Bingfan Liu, Ling Zhang, Tianyu Ding.

Figure 1
Figure 1. Figure 1: Evolutionary tree of always-on agents. The figure sketches the survey’s organizing claim: [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The survey is organized as a three-regime stack. [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Corpus coverage over time, as the share of each year’s coded works whose lifecycle touches [PITH_FULL_IMAGE:figures/full_fig_p015_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Coverage heatmap: for each taxonomy part (rows), the number of coded works whose [PITH_FULL_IMAGE:figures/full_fig_p033_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The persistent-state lifecycle. State flows along a forward arc that accumulates and uses [PITH_FULL_IMAGE:figures/full_fig_p043_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Stage-proxy opportunity for the five invariants, by taxonomy part. A work counts toward [PITH_FULL_IMAGE:figures/full_fig_p043_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The Always-On Evaluation Protocol (AOEP). Each episode is a typed event stream; [PITH_FULL_IMAGE:figures/full_fig_p088_7.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

57 extracted references · 51 canonical work pages · 26 internal anchors

  1. [1]

    109 Maya Anderson, Guy Amit, and Abigail Goldsteen

    doi: 10.1037/0033-295X.111.4.1036. 109 Maya Anderson, Guy Amit, and Abigail Goldsteen. Is my data in your retrieval database? mem- bership inference attacks against retrieval augmented generation,

    work page doi:10.1037/0033-295x.111.4.1036

  2. [2]

    com/docs/en/memory

    URL https://code.claude. com/docs/en/memory. Accessed 2026-06-28. Mustafa Arslan. Aeon: High-performance neuro-symbolic memory management for long-horizon llm agents,

    2026

  3. [3]

    Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, Yuxiao Dong, Jie Tang, and Juanzi Li

    doi: 10.1016/S1364-6613(00)01538-2. Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, Yuxiao Dong, Jie Tang, and Juanzi Li. Longbench: A bilingual, multitask benchmark for long context understanding,

    work page doi:10.1016/s1364-6613(00)01538-2

  4. [4]

    STALE: Can LLM Agents Know When Their Memories Are No Longer Valid?

    URL https://arxiv.org/abs/2605.06527. Chunliang Chen, Ming Guan, Xiao Lin, Jiaxu Li, Luxi Lin, Qiyi Wang, Xiangyu Chen, Jixiang Luo, Changzhi Sun, Dell Zhang, and Xuelong Li. Telemem: Building long-term and multimodal memory for agentic ai, 2026a. Jiawei Chen, Hongyu Lin, Xianpei Han, and Le Sun. Benchmarking large language models in retrieval-augmented g...

    work page internal anchor Pith review Pith/arXiv arXiv

  5. [5]

    Benchmarking large language models in retrieval- augmented generation.arXiv preprint arXiv:2309.01431, 2023

    URL https://arxiv.org/abs/2309.01431. Kai Chen, Yan Pang, and Tianhao Wang. Mrmmia: Membership inference attacks on memory in chat agents, 2026b. 111 Lei (Rachel) Chen, Guilin Zhang, Kai Zhao, Dalmo Cirne, Andy Olsen, Xu Chu, Zeke Miller, Alet Blanken, Amine Anoun, and Jerry Ting. Deployment-time memorization in foundation-model agents, 2026c. URL https:/...

    work page arXiv

  6. [6]

    Available: https://arxiv.org/abs/2407.12784

    URL https://arxiv.org/abs/2407.12784. Zheng Chen, Hanqing Liu, Duling Xu, Dong Dong, Jialin Li, Bangzheng Pu, and Jidong Zhai. Cordon: Semantic transactions for tool-using llm agents, 2026h. Zhibao Chen and Qian Cheng. Learning what to remember: A cognitively grounded multi-factor value model for agentic memory,

    work page arXiv

  7. [7]

    Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory

    URL https://arxiv.org/abs/ 2504.19413. Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, Anastasios Nikolas Angelopoulos, Tianle Li, Dacheng Li, Hao Zhang, Banghua Zhu, Michael Jordan, Joseph E. Gonzalez, and Ion Stoica. Chatbot arena: An open platform for evaluating llms by human preference,

    work page internal anchor Pith review Pith/arXiv arXiv

  8. [8]

    A Systematic Survey of Security Threats and Defenses in LLM-Based AI Agents: A Layered Attack Surface Framework

    URL https://arxiv.org/abs/2604.23338. 112 Zheng Chu, Jingchang Chen, Qianglong Chen, Weijiang Yu, Haotian Wang, Ming Liu, and Bing Qin. Timebench: A comprehensive evaluation of temporal reasoning abilities in large language models,

    work page internal anchor Pith review Pith/arXiv arXiv

  9. [9]

    AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents

    URL https://arxiv.org/abs/2406.13352. Edoardo Debenedetti, Ilia Shumailov, Tianqi Fan, Jamie Hayes, Nicholas Carlini, Daniel Fabian, Christoph Kern, Chongyang Shi, Andreas Terzis, and Florian Tram` er. Defeating prompt injec- tions by design,

    work page internal anchor Pith review Pith/arXiv arXiv

  10. [10]

    WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?

    URL https://arxiv.org/abs/2403.07718. Pengfei Du. Memory for autonomous llm agents: Mechanisms, evaluation, and emerging frontiers,

    work page internal anchor Pith review Pith/arXiv arXiv

  11. [11]

    Memp: Exploring Agent Procedural Memory

    URL https://arxiv.org/abs/2508.06433. Bahare Fatemi, Mehran Kazemi, Anton Tsitsulin, Karishma Malkan, Jinyeong Yim, John Palow- itch, Sungyong Seo, Jonathan Halcrow, and Bryan Perozzi. Test of time: A benchmark for evaluating llms on temporal reasoning,

    work page internal anchor Pith review Pith/arXiv arXiv

  12. [12]

    RAGBench: Explainable bench- mark for retrieval-augmented generation systems,

    URL https://arxiv.org/abs/2407.11005. Yuhang Gan, Yiwei Yang, Yuyi Li, Xiangyu Gao, Yichen Wang, Rain Jiang, Xiaoning Ding, Andi Quinn, and Chen Qian. Concordia: Jit-compiled persistent-kernel checkpointing for fault-tolerant llm inference,

    work page arXiv

  13. [13]

    Accessed 2026-06-28

    URL https://cloud.google.com/gemini/enterprise/docs/configure- personalization. Accessed 2026-06-28. Jiawei Gu, Xuhui Jiang, Zhichao Shi, Hexiang Tan, Xuehao Zhai, Chengjin Xu, Wei Li, Yinghan Shen, Shengjie Ma, Honghao Liu, Saizhuo Wang, Kun Zhang, Yuanzhuo Wang, Wen Gao, Lionel Ni, and Jian Guo. A survey on llm-as-a-judge,

    2026

  14. [14]

    RULER: What's the Real Context Size of Your Long-Context Language Models?

    URL https://arxiv.org/abs/2404.06654. He Hu, Chiyuan Ma, Qianning Wang, Lin Liu, Yucheng Zhou, Laizhong Cui, Fei Ma, and Qi Tian. Theramind: A strategic and adaptive agent for longitudinal psychological counseling, 2025a. Hebin Hu, Renke Dai, Ah-Hwee Tan, and Yilin Kang. Synthesis and evaluation of long-term history-aware medical dialogue,

    work page internal anchor Pith review Pith/arXiv arXiv

  15. [15]

    Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions

    Yuanzhe Hu, Yu Wang, and Julian McAuley. Evaluating memory in llm agents via incremental multi-turn interactions, 2025b. URL https://arxiv.org/abs/2507.05257. Yuyang Hu, Shichun Liu, Yanwei Yue, Guibin Zhang, Boyang Liu, Fangyi Zhu, Jiahang Lin, Honglin Guo, Shihan Dou, Zhiheng Xi, Senjie Jin, Jiejun Tan, Yanbin Yin, Jiongnan Liu, Zeyu Zhang, Zhongxiang S...

    work page internal anchor Pith review Pith/arXiv arXiv

  16. [16]

    Rethinking memory mechanisms of foundation agents in the second half: A survey.arXiv preprint arXiv:2602.06052, 2026

    URL https://arxiv.org/abs/ 2602.06052. Zhaopei Huang, Qifeng Dai, Guozheng Wu, Xiaopeng Wu, Kehan Chen, Chuan Yu, Xubin Li, Tiezheng Ge, Wenxuan Wang, and Qin Jin. Mem-pal: Towards memory-based personalized dialogue assistants for long-term user-agent interaction, 2025b. Ziheng Huang, Sebastian Gutierrez, Hemanth Kamana, and Stephen MacNeil. Memory sandbo...

    work page arXiv

  17. [17]

    MemoryOS : A Memory Operating System for AI System

    URL https: //arxiv.org/abs/2506.06326. Qingcan Kang, Liu Mingyang, Shixiong Kai, Kaichao Liang, Tao Zhong, and Mingxuan Yuan. Learning what to remember: Observability-safe memory retention via constrained optimization for long-horizon language agents,

    work page arXiv

  18. [18]

    Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al

    doi: 10.1016/j.tics.2016.05.004. Maximilian Kuschewski, Lam-Duy Nguyen, Matthias Jasny, Tobias Ziegler, Viktor Leis, and Muhammad El-Hindi. Btrlog: Low-latency logging for cloud database systems,

    work page doi:10.1016/j.tics.2016.05.004 2016

  19. [19]

    001.0001

    doi: 10.7551/mitpress/7688. 001.0001. Tu Lan and Chaowei Xiao. Runtime skill audit: Targeted runtime probing for agent skill security,

    work page doi:10.7551/mitpress/7688

  20. [20]

    URL https://arxiv.org/abs/2604.16548. Adam Liska, Tomas Kocisky, Elena Gribovskaya, Tayfun Terzi, Eren Sezener, Devang Agrawal, Cyprien de Masson d’Autume, Tim Scholtes, Manzil Zaheer, Susannah Young, Ellen Gilsenan- McMahon, Sophia Austin, Phil Blunsom, and Angeliki Lazaridou. Streamingqa: A benchmark for adaptation to new knowledge over time in question...

    work page internal anchor Pith review Pith/arXiv arXiv

  21. [21]

    Lost in the Middle: How Language Models Use Long Contexts

    URL https://arxiv.org/abs/2307.03172. Ruiping Liu, Junwei Zheng, Yufan Chen, Di Wen, Shaofang Quan, Chengzhi Wu, Jiaming Zhang, Kailun Yang, Kunyu Peng, and Rainer Stiefelhagen. Egoexomem: Cross-view memory reasoning over synchronized egocentric and exocentric videos, 2026d. Simiao Liu, Li Zhang, Fang Liu, Xiaoli Lian, Yang Liu, and Yinghao Zhu. Memrepair...

    work page internal anchor Pith review Pith/arXiv arXiv

  22. [22]

    Evaluating Very Long-Term Conversational Memory of LLM Agents

    URL https://arxiv. org/abs/2402.17753. Bodhisattwa Prasad Majumder, Bhavana Dalvi Mishra, Peter Jansen, Oyvind Tafjord, Niket Tan- don, Li Zhang, Chris Callison-Burch, and Peter Clark. Clin: A continually learning language agent for rapid task adaptation and generalization,

    work page internal anchor Pith review Pith/arXiv arXiv

  23. [23]

    arXiv preprint arXiv:2310.10134 , year=

    URL https://arxiv.org/abs/2310.10134. Ripon Chandra Malo and Tong Qiu. Projectmem: A local-first, event-sourced memory and judg- ment layer for ai coding agents,

    work page arXiv

  24. [24]

    URL https://arxiv.org/abs/2606.24535. 121 Samuele Marro, Alan Chan, Xinxing Ren, Lewis Hammond, Jesse Wright, Gurjyot Wanga, Tiziano Piccardi, Nuno Campos, Tobin South, Jialin Yu, Sunando Sengupta, Eric Sommerlade, Alex Pentland, Philip Torr, and Jiaxin Pei. Permission manifests for web agents,

    work page internal anchor Pith review Pith/arXiv arXiv

  25. [25]

    Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov

    doi: 10.1037/0033-295X.102.3.419. Michael McCloskey and Neal J. Cohen. Catastrophic interference in connectionist networks: The sequential learning problem. InPsychology of Learning and Motivation, volume 24, pages 109–

    work page doi:10.1037/0033-295x.102.3.419

  26. [26]

    McCloskey, N

    doi: 10.1016/S0079-7421(08)60536-8. Kai Mei, Xi Zhu, Wujiang Xu, Wenyue Hua, Mingyu Jin, Zelong Li, Shuyuan Xu, Ruosong Ye, Yingqiang Ge, and Yongfeng Zhang. Aios: Llm agent operating system,

    work page doi:10.1016/s0079-7421(08)60536-8

  27. [27]

    A Survey of Context Engineering for Large Language Models

    URL https://arxiv.org/ abs/2507.13334. Huichao Men, Yizhen Hu, Yu Gao, Xiaofeng Mou, Yi Xu, and Xinhua Xiao. An autonomous agent framework for feature-label extraction from device dialogues and automatic multi-dimensional device hosting planning based on large language models,

    work page internal anchor Pith review Pith/arXiv arXiv

  28. [28]

    Accessed 2026-06-28

    URL https://support.microsoft.com/en-us/microsoft-365-copilot/manage-copilot-memory-in- microsoft-365-copilot. Accessed 2026-06-28. Evan Miller. Adding error bars to evals: A statistical approach to language model evaluations,

    2026

  29. [29]

    Praveen Kumar Myakala, Manan Agrawal, and Rahul Manche

    doi: 10.1371/journal.pone.0120644. Praveen Kumar Myakala, Manan Agrawal, and Rahul Manche. Beliefshift: Benchmarking temporal belief consistency and opinion drift in llm agents,

    work page doi:10.1371/journal.pone.0120644

  30. [30]

    Yohei Nakajima

    URL https://arxiv.org/abs/2603.23848. Yohei Nakajima. Regimes: An auditable, held-out-gated improvement loop demonstrated on long- memeval with activegraph,

    work page arXiv

  31. [31]

    Preprint, arXiv:2401.00396

    URL https://arxiv.org/abs/2401.00396. Yasmine Omri, Ziyu Gan, Zachary Broveak, Robin Geens, Zexue He, Alex Pentland, Marian Ver- helst, Tsachy Weissman, and Thierry Tambe. Agent memory: Characterization and system implications of stateful long-horizon workloads,

    work page arXiv

  32. [32]

    Accessed 2026-06-28

    URL https://help.openai.com/en/articles/ 8590148-memory-faq. Accessed 2026-06-28. 123 Barak Or. Mttr-a: Measuring cognitive recovery latency in multi-agent systems,

    2026

  33. [33]

    Generative Agents: Interactive Simulacra of Human Behavior

    URL https://arxiv.org/abs/2304.03442. Yein Park, Chanwoong Yoon, Jungwoo Park, Donghyeon Lee, Minbyul Jeong, and Jaewoo Kang. Chroknowledge: Unveiling chronological knowledge of language models in multiple domains,

    work page internal anchor Pith review Pith/arXiv arXiv

  34. [34]

    EMNLP 2024 (ACL Anthology 2024.emnlp-main)

    URL https://aclanthology.org/2024.emnlp-main.394/. EMNLP 2024 (ACL Anthology 2024.emnlp-main). Suliu Qin, Haomin Zhuang, Yujun Zhou, Yufei Han, and Xiangliang Zhang. Airguard: Guarding agent actions with runtime authority control,

    2024

  35. [35]

    Collaborativememory: Multi- user memory sharing in LLM agents with dynamic ac- cess control

    URL https://arxiv.org/abs/2505.18279. Andoni Rodriguez, Alberto Pozanco, and Daniel Borrajo. Is your agent playing dead? deployed llm agents exhibit constraint-evasive fabrication and thanatosis,

    work page arXiv

  36. [36]

    L a MP : When Large Language Models Meet Personalization

    doi: 10.18653/v1/2024.acl-long.399. URL https: //aclanthology.org/2024.acl-long.399/. 125 Klaus-Dieter Schewe, Andreas Prinz, and Egon B¨ orger. Concurrent computing with shared repli- cated memory,

    work page doi:10.18653/v1/2024.acl-long.399 2024

  37. [37]

    Reflexion: Language Agents with Verbal Reinforcement Learning

    URL https://arxiv.org/abs/2303.11366. Yiheng Shu, Bernal Jimenez Gutierrez, Saisri Padmaja Jonnalagedda, Yuguang Yao, Huan Sun, and Yu Su. Agentcl: Toward rigorous evaluation of continual learning in language agents,

    work page internal anchor Pith review Pith/arXiv arXiv

  38. [38]

    Cognitive Architectures for Language Agents

    URL https://arxiv.org/abs/2309.02427. Haoran Tan, Zeyu Zhang, Zhicheng Cao, Rui Li, and Xu Chen. Deltamem: Incremental experience memory for llm agents via residual trees,

    work page internal anchor Pith review Pith/arXiv arXiv

  39. [39]

    URL https://aclanthology.org/2024.acl-long.850/

    doi: 10.18653/v1/2024.acl-long.850. URL https://aclanthology.org/2024.acl-long.850/. 127 Endel Tulving. Episodic and semantic memory. In Endel Tulving and Wayne Donaldson, editors, Organization of Memory, pages 381–403. Academic Press,

    work page doi:10.18653/v1/2024.acl-long.850 2024

  40. [40]

    From Recall to Forgetting: Benchmarking Long-Term Memory for Personalized Agents

    URL https: //arxiv.org/abs/2604.20006. Findings of ACL

    work page internal anchor Pith review Pith/arXiv arXiv

  41. [41]

    Voyager: An Open-Ended Embodied Agent with Large Language Models

    Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, and Anima Anandkumar. Voyager: An open-ended embodied agent with large language models, 2023a. URL https://arxiv.org/abs/2305.16291. Haoyu Wang, Christopher M. Poskitt, Jiali Wei, and Jun Sun. Probguard: Probabilistic runtime monitoring for llm agent safety, 2025a. Ji...

    work page internal anchor Pith review Pith/arXiv arXiv

  42. [42]

    Contextualized privacy defense for llm agents.arXiv preprint arXiv:2603.02983, 2026

    Fukang Wen, Zhijie Wang, and Ruilin Xu. Openrath: Session-centered runtime state for agent systems, 2026a. Yule Wen, Yanzhe Zhang, Jianxun Lian, Xiaoyuan Yi, Xing Xie, and Diyi Yang. Contextualized privacy defense for LLM agents, 2026b. URL https://arxiv.org/abs/2603.02983. Yixuan Weng, Minjun Zhu, Qiujie Xie, Qiyao Sun, Zhen Lin, Sifan Liu, and Yue Zhang...

    work page arXiv

  43. [43]

    Cailin Winston, Claris Winston, and Ren´ e Just

    doi: 10.1126/science.8036517. Cailin Winston, Claris Winston, and Ren´ e Just. Solver-aided verification of policy compliance in tool-augmented llm agents,

    work page doi:10.1126/science.8036517

  44. [44]

    LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory

    Di Wu, Hongwei Wang, Wenhao Yu, Yuwei Zhang, Kai-Wei Chang, and Dong Yu. Longmemeval: Benchmarking chat assistants on long-term interactive memory, 2025a. URL https://arxiv.org/ abs/2410.10813. Junde Wu, Minhao Hu, Jiayuan Zhu, Jiazhen Pan, Yuyuan Liu, Min Xu, and Yueming Jin. Git context controller: Manage the context of llm-based agents like git, 2025b....

    work page internal anchor Pith review Pith/arXiv arXiv

  45. [45]

    A-MEM: Agentic Memory for LLM Agents

    Ruyi Xu, Guangxuan Xiao, Yukang Chen, Liuning He, Yao Lu, and Song Han. Streamingvlm: Real-time understanding for infinite video streams, 2025b. Wujiang Xu, Zujie Liang, Kai Mei, Hang Gao, Juntao Tan, and Yongfeng Zhang. A-mem: Agentic memory for llm agents, 2025c. URL https://arxiv.org/abs/2502.12110. Xiaoyu Xu, Minxin Du, Qipeng Xie, Haobin Ke, Qingqing...

    work page internal anchor Pith review Pith/arXiv arXiv

  46. [46]

    GroupMemBench: Benchmarking LLM Agent Memory in Multi-Party Conversations

    Jingbo Yang, Kwei-Herng Lai, Xiaowen Wang, Shiyu Chang, Yaar Harari, and Evgeniy Gabrilovich. Groupmembench: Benchmarking llm agent memory in multi-party conversations, 2026b. URL https://arxiv.org/abs/2605.14498. Jingkang Yang, Shuai Liu, Hongming Guo, Yuhao Dong, Xiamengwei Zhang, Sicheng Zhang, Pengyun Wang, Zitang Zhou, Binzhu Xie, Ziyue Wang, Bei Ouy...

    work page internal anchor Pith review Pith/arXiv arXiv

  47. [47]

    Wentao Wu, Hongsong Li, Haixun Wang, and Kenny Q

    URL https://arxiv.org/abs/2406.04744. Yutao Yang, Junsong Li, Qianjun Pan, Jie Zhou, Kai Chen, Qin Chen, Jingyuan Zhao, Ningning Zhou, Xin Li, and Liang He. Psychagent: An experience-driven lifelong learning agent for self- evolving psychological counselor, 2026c. Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov,...

    work page arXiv 2018

  48. [48]

    , booktitle =

    doi: 10.18653/v1/D18-1259. URL https://aclanthology.org/D18- 1259/. Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models,

    work page doi:10.18653/v1/d18-1259

  49. [49]

    ReAct: Synergizing Reasoning and Acting in Language Models

    URL https://arxiv.org/abs/ 2210.03629. Shunyu Yao, Noah Shinn, Pedram Razavi, and Karthik Narasimhan.τ-bench: A benchmark for tool-agent-user interaction in real-world domains,

    work page internal anchor Pith review Pith/arXiv arXiv

  50. [50]

    $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains

    URL https://arxiv.org/abs/2406.12045. Yanyu Yao, Shangze Li, Zhi Zheng, Hui Zheng, Qi Liu, Tong Xu, and Enhong Chen. Atommem: Building simple and effective memory system for llm agents via atomic facts,

    work page internal anchor Pith review Pith/arXiv arXiv

  51. [51]

    Survey on Evaluation of LLM-based Agents

    URL https://arxiv. org/abs/2503.16416. Yejin Yoon, Minseo Kim, and Taeuk Kim. Latent preference modeling for cross-session personalized tool calling,

    work page internal anchor Pith review Pith/arXiv arXiv

  52. [52]

    A survey on agent-as-a-judge.arXiv preprint arXiv:2601.05111, 2026

    URL https://arxiv.org/abs/2601.05111. Chaerin Yu, Chihun Choi, Sunjae Lee, Hyosu Kim, Steven Y. Ko, Young-Bae Ko, and Sangeun Oh. Leveraging llms for efficient and personalized smart home automation, 2026a. Jiatong Yu, Yinghui He, Anirudh Goyal, and Sanjeev Arora. On the impossibility of retrain equiv- alence in machine unlearning,

    work page arXiv

  53. [53]

    PatchBoard: Schema-Grounded State Mutation for Reliable and Auditable LLM Multi-Agent Collaboration

    Zhang, Ma, Ma, Li, Xu, Yi, and Li. Understanding users’ privacy perceptions towards llm’s rag- based memory, 2025a. Binchi Zhang, Zihan Chen, Cong Shen, and Jundong Li. Verification of machine unlearning is fragile, 2024a. Chaoyun Zhang, Shilin He, Jiaxu Qian, Bowen Li, Liqun Li, Si Qin, Yu Kang, Minghua Ma, Guyue Liu, Qingwei Lin, Saravan Rajmohan, Dongm...

    work page internal anchor Pith review Pith/arXiv arXiv

  54. [54]

    ExpeL: LLM Agents Are Experiential Learners, 2023

    URL https://arxiv.org/abs/2308.10144. Bingxi Zhao, Jiahao Zhang, Xubin Ren, Zirui Guo, Tianzhe Chu, Yi Ma, and Chao Huang. Deep- tutor: Towards agentic personalized tutoring, 2026a. Siyan Zhao, Mingyi Hong, Yang Liu, Devamanyu Hazarika, and Kaixiang Lin. Do llms recognize your preferences? evaluating personalized preference following in llms,

    work page arXiv

  55. [55]

    Lifelongagentbench: Evaluating llm agents as lifelong learners.arXiv preprint arXiv:2505.11942, 2025

    URL https: //arxiv.org/abs/2505.11942. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. Judging LLM-as-a-judge with MT-Bench and chatbot arena,

    work page arXiv

  56. [56]

    Yusheng Zheng, Tianyuan Wu, Quanzhi Fu, Tong Yu, Wenan Mao, Wei Wang, Dan Williams, and Andi Quinn

    NeurIPS 2023 Datasets and Benchmarks. Yusheng Zheng, Tianyuan Wu, Quanzhi Fu, Tong Yu, Wenan Mao, Wei Wang, Dan Williams, and Andi Quinn. Actplane: Programmable os-level policy enforcement for agent harnesses, 2026c. Yusheng Zheng, Yiwei Yang, Wei Zhang, and Andi Quinn. Acrfence: Preventing semantic rollback attacks in agent checkpoint-restore, 2026d. Wan...

    2023

  57. [57]

    URL https://arxiv.org/abs/2305.10250. 135 Chenyu Zhou, Huacan Chai, Wenteng Chen, Zihan Guo, Rong Shan, Yuanyi Song, Tianyi Xu, Yingxuan Yang, Aofan Yu, Weiming Zhang, Congming Zheng, Jiachen Zhu, Zeyu Zheng, Zhu- osheng Zhang, Xingyu Lou, Changwang Zhang, Zhihui Fu, Jun Wang, Weiwen Liu, Jianghao Lin, and Weinan Zhang. Externalization in llm agents: A un...

    work page internal anchor Pith review Pith/arXiv arXiv