Pith. sign in

REVIEW 3 major objections 5 minor 11 cited by

G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems

T0 review · 3 major / 5 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 A three-tier graph memory lets LLM-based multi-agent teams learn from past collaborations, lifting success by up to 20.89% on embodied tasks and 10.12% on knowledge QA without touching the base framework.

desk verdict A well-motivated hierarchical memory architecture for MAS with a broad evaluation, but the empirical claims are undermined by missing error bars and test-set hyperparameter selection. read the letter →

arxiv 2506.07398 v2 pith:WZRYOMZY submitted 2025-06-09 cs.MA cs.CLcs.LG

Guibin Zhang , Muxin Fu , Guancheng Wan , Miao Yu , Kun Wang , Shuicheng Yan This is my paper
classification cs.MAcs.CLcs.LG
keywords multi-agentsystemsLLMagentshierarchicalmemorygraph-basedself-evolvingretrieval-augmentedgenerationembodiedactionknowledgequestionanswering
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

The paper argues that LLM-powered multi-agent systems fail to improve across tasks because their memory mechanisms are too simple: they either keep context only inside one trial or pass along only final answers, discarding the inter-agent dialogue that carries the reasoning. To fix this, it proposes G-Memory, a plug-and-play module that organizes a team's history into a three-tier graph hierarchy—high-level insights, a query graph linking related past tasks, and fine-grained interaction logs—and retrieves from all three levels before a new query. This is why it would matter: if the claim holds, any existing multi-agent system can gain self-evolution and better collaboration simply by adding a memory layer, with no changes to its orchestration. The paper's evidence is that G-Memory improves success rates on embodied action and accuracy on knowledge QA by up to 20.89% and 10.12% respectively across five benchmarks, three LLM backbones, and three multi-agent frameworks.

What carries the argument

The load-bearing mechanism is the three-tier graph hierarchy with bi-directional traversal. Coarse similarity search over the query graph returns candidate past tasks; a one-hop expansion widens the candidate set; an upward traversal from query to insight graph collects high-level lessons, and a downward traversal uses an LLM-facilitated sparsifier to condense each relevant interaction graph into the dialogue segments that matter for the current task. An operator then personalizes these memories for each agent role, and after execution the hierarchy is updated: new interaction graphs are stored, new query nodes are linked to the relevant past queries and to the queries supporting the used insights, and new or merged insights are distilled from contrasting successful and failed trajectories. The hierarchy is what lets the system trade off abstraction and detail: general lessons guide strategy, while condensed trajectories provide concrete corrective steps.

What would settle it

Run the identical comparison with a held-out test set: build all G-Memory graphs using only queries from a training split, then evaluate on a disjoint split (or on a second distribution entirely). If the improvements over the no-memory baseline shrink to near zero or reverse, the central claim of cross-trial generalization fails. A cheaper check is to clear memory between trials and confirm success rates fall back to baseline.

Watch

Extended reading notes

Core claim

The central claim is that the bottleneck in multi-agent self-evolution is memory, not orchestration. G-Memory counters the simplistic designs it surveys with a three-tier graph: the insight graph holds distilled, generalizable lessons together with the queries that support them; the query graph stores past queries, their success or failure status, and a semantic network connecting them; the interaction graph records every agent utterance and the influence edges that trace how one utterance inspired another. For a new query, G-Memory retrieves similar past queries, expands to their one-hop neighbors, then traverses upward to pull relevant insights and downward to condense the most useful interaction histories into core subgraphs. Each agent receives memory filtered to its role before the team acts, and after execution the query and insight graphs are extended with the new experience. The paper reports that this cycle consistently improves performance across three task domains, with the largest gains on embodied action tasks.

Load-bearing premise

The reported gains assume that the memory accumulated during earlier evaluation trials does not leak information about later test queries; the experimental setup does not state a train/test split, so the improvements could reflect within-distribution memorization rather than cross-trial generalization.

Editorial extensions

If this is right

  • G-Memory can be retrofitted onto any MAS that accepts extra context: the reported gains are obtained without modifying the frameworks themselves.
  • Memory should be role-specific: giving every agent the same retrieved history underperforms, suggesting that agents with different functions need different cues.
  • High-level insights and fine-grained interaction traces are complementary; removing either one costs roughly 3 to 5 percentage points on the tested frameworks.
  • Teams improve with exposure: on embodied benchmarks, G-Memory reaches higher success ceilings and succeeds with fewer trials as memory accumulates.
  • The performance gain is not bought by excessive prompting: token consumption stays in line with cheaper baselines.

Reading between the lines

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

  • A decisive untested question is generalization beyond the evaluation distribution: the paper does not report a train/test split, and its success curves show memory accumulating during the test run; a held-out evaluation would separate cross-trial learning from memorization.
  • The insight graph's cross-task edges hint at a transfer extension the paper does not run: checking whether lessons learned in one domain (such as verifying web-search facts) improve performance in another (such as embodied object placement).
  • The query graph could support richer retrieval keys than semantic similarity, such as failure type or agent role; the paper only explores embedding similarity plus one-hop expansion.
  • Because insight distillation and trajectory sparsification are done by LLMs, the quality of the memory likely depends on the backbone's summarization reliability; the paper does not isolate this effect.
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

3 major / 5 minor

Summary. The manuscript introduces G-Memory, a hierarchical three-tier graph memory system for LLM-based multi-agent systems (MAS), comprising an insight graph, a query graph, and an interaction graph. The method performs coarse-grained retrieval over the query graph, bi-directional traversal to retrieve high-level insights and condensed interaction trajectories, and then updates all three levels after task execution. The authors claim that G-Memory is plug-and-play and improves embodied action success by up to 20.89% and knowledge QA accuracy by up to 10.12% across five benchmarks, three LLM backbones, and three MAS frameworks. The evaluation is currently reported as single point estimates without error bars, with hyperparameters selected by inspecting the same benchmarks whose results are reported, and without an explicit train/test split in the memory-accumulation protocol.

Significance. If the empirical claims withstand scrutiny, G-Memory addresses a real and currently underserved need: most existing MAS memory mechanisms are limited to inside-trial memory or coarse final artifacts, whereas G-Memory offers role-specific, multi-granularity memory with a principled graph-based organization. The architecture is clear and coherent, the ablations in Figure 4c show that both the insight and interaction components contribute, and the paper covers a wide range of benchmarks, backbones, and frameworks. The public code availability is a further strength. However, the central contribution is empirical, and the current evidence is not yet sufficient to support the headline numbers: there are no variance estimates, hyperparameters are tuned on the same test benchmarks, and the evaluation does not clarify whether memory is built from queries disjoint from those used for final evaluation. The use of LLMs to generate insights is not circular because performance is measured on external benchmarks, but the statistical and protocol issues are load-bearing for the paper's main claim.

major comments (3)
  1. [§5.1, §5.4, Tables 1–3] The hyperparameters k and hop are selected by observing performance on the same five benchmarks whose results are then reported as the headline numbers. Section 5.4 states that 'we employ 1-hop expansion and k∈{1,2} throughout the experiments' after Figure 4 shows performance on ALFWorld, PDDL, and FEVER for different hop counts and k values. This is a form of test-set tuning: the reported 20.89% and 10.12% gains reflect configurations chosen on the benchmark test sets. Please provide an independent validation split for hyperparameter selection, report results for all configurations, or otherwise justify that the choice does not inflate the reported gains.
  2. [§5.1, §A, Tables 1–3] The experiments are reported as single point estimates with no number of runs, random seeds, sampling temperatures, or confidence intervals. Both LLM decoding and MacNet's random graph topology are stochastic, so differences of several percentage points between rows in Tables 1–3 may be consistent with sampling noise. For example, Table 3 shows Voyager on DyLAN/HotpotQA at 30.71 versus no-memory at 30.61, a 0.10-point difference, while other comparisons range from a few points to more than twenty points. Without multiple seeds and variance estimates, the reader cannot determine which improvements are meaningful. Please report means and standard errors over repeated runs, or an equivalent significance analysis.
  3. [§5, §B.1, Figures 6a–c] The evaluation protocol never specifies a train/test split. Figures 6a–c show success curves over trials in which memory is accumulated during the evaluation run, and Section A does not state whether the queries used to build memory are disjoint from the queries whose success is reported. If memory is built from the same distribution being tested, the observed gains could reflect within-distribution memorization rather than cross-trial generalization, which is what the abstract's 'generalizable insights' claims. Please clarify the split between memory-building queries and evaluation queries, or add a held-out evaluation where memory is constructed only on a training set.
minor comments (5)
  1. [Appendix C] The heading 'Inisght Summarization Function' contains a typo; it should be 'Insight Summarization Function'.
  2. [Eq. (9)] The set QR is used in Eq. (9) but is not explicitly named when the top-M relevant queries are introduced in Eq. (7); please define QR there or restate it in the text around Eq. (9).
  3. [§5.4] The parameter M in Eq. (7) is listed as being chosen from {2,3,4,5} in Section 5.1, but Section 5.4 reports sensitivity analysis only for hop and k, not for M. Please state how M was set and whether the results are robust to that choice.
  4. [§5.4] The text says 'the optimal k is among {1,2}' and later 'we employ 1-hop expansion and k∈{1,2} throughout the experiments,' which is ambiguous about whether k=1 or k=2 is selected per benchmark, per framework, or fixed globally. Please state the exact setting for each reported cell.
  5. [Abstract and Introduction] The phrase 'we are alarmed to discover' is editorializing and out of place in a formal research paper; please rephrase.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the reported gains are empirical comparisons on external benchmarks, and the memory mechanism is not defined in terms of the measured outcomes.

full rationale

The paper's central claim is an empirical performance comparison against five external benchmarks (ALFWorld, SciWorld, PDDL, HotpotQA, FEVER) under fixed MAS frameworks and LLM backbones. G-Memory's equations (4)-(11) define a retrieval and update mechanism; they do not encode the evaluation metrics or presuppose the reported success rates. The insight, query, and interaction graphs are constructed from trajectories and updated with environmental feedback, and the headline improvements are measured by external task success/accuracy, not by a quantity defined in terms of G-Memory's own outputs. No parameter is fitted to a target label and then reported as a prediction: the choices of k and hop are selected in Section 5.4 on the same benchmarks, which is a test-set tuning concern, not a reduction of the result to an input. Self-citations ([30], [51], [69], [71]) appear only in related-work discussion and are not used to justify the architecture or the empirical claim. The fact that LLM-generated insights are later consumed by LLM agents is an architectural choice, not a circular definition. Therefore no circular step is exhibited and the score is 0.

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

The method relies on several domain assumptions about the transferability of historical trajectories and the fidelity of LLM-based summarization. The main free parameters are the retrieval hyperparameters (hop, k, M), selected via sensitivity analysis on the test benchmarks. No new physical or logical entities are introduced beyond the three graph structures, which are computational components rather than independently falsifiable entities.

free parameters (3)
  • top-k retrieved queries (k) = 1 or 2
    Selected via sensitivity analysis in Section 5.4 on the same benchmark test sets; no separate validation set mentioned.
  • number of sparsified interaction graphs (M) = 2 to 5
    Set among {2,3,4,5} in Equation (7); the paper does not report the exact value used for each experiment.
  • hop expansion (hop) = 1
    1-hop expansion chosen in Section 5.4 based on best performance on test data.
assumptions (3)
  • domain assumption Historical trajectories of similar queries contain transferable guidance for new queries.
    The entire retrieval mechanism in Sections 4.1-4.2 assumes cross-trial memory improves task solving.
  • domain assumption LLM-based summarization and sparsification preserve critical information without distortion.
    Insight generation and graph sparsification rely on the same LLM family; no verification that insights are factually grounded.
  • domain assumption The evaluation benchmarks' test tasks are independent of the memory-building trials.
    Required for the reported success rates to reflect generalization; not explicitly stated in the paper.

how reviews work

0 comments
Cite this review

Pith. "Pith review of G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems." pith.science (2026). https://pith.science/paper/WZRYOMZY

@misc{pith2026250607398,
  author       = {Pith},
  title        = {Pith review of: G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WZRYOMZY}},
  note         = {Machine review of arXiv:2506.07398}
}
abstract

Large language model (LLM)-powered multi-agent systems (MAS) have demonstrated cognitive and execution capabilities that far exceed those of single LLM agents, yet their capacity for self-evolution remains hampered by underdeveloped memory architectures. Upon close inspection, we are alarmed to discover that prevailing MAS memory mechanisms (1) are overly simplistic, completely disregarding the nuanced inter-agent collaboration trajectories, and (2) lack cross-trial and agent-specific customization, in stark contrast to the expressive memory developed for single agents. To bridge this gap, we introduce G-Memory, a hierarchical, agentic memory system for MAS inspired by organizational memory theory, which manages the lengthy MAS interaction via a three-tier graph hierarchy: insight, query, and interaction graphs. Upon receiving a new user query, G-Memory performs bi-directional memory traversal to retrieve both $\textit{high-level, generalizable insights}$ that enable the system to leverage cross-trial knowledge, and $\textit{fine-grained, condensed interaction trajectories}$ that compactly encode prior collaboration experiences. Upon task execution, the entire hierarchy evolves by assimilating new collaborative trajectories, nurturing the progressive evolution of agent teams. Extensive experiments across five benchmarks, three LLM backbones, and three popular MAS frameworks demonstrate that G-Memory improves success rates in embodied action and accuracy in knowledge QA by up to $20.89\%$ and $10.12\%$, respectively, without any modifications to the original frameworks. Our codes are available at https://github.com/bingreeky/GMemory.

Figures

Figures reproduced from arXiv: 2506.07398 by the authors.

Figure 1
Figure 1. (Left) We report the token cost of several single-agent and MAS baselines on ALFWorld benchmark; (Right) The overview of G-Memory’s three-tier hierarchical memory architecture, en￾compassing the insight graph, query graph and interaction (utterance) graph. retrieve relevant information. Previous successful memory mechanism designs, including both inside-trial memory (i.e., context retained within solving one single … view at source ↗
Figure 2
Figure 2. The overview of our proposed G-Memory. 4.1 Coarse-grained Memory Retrieval As a plug-in designed for seamless integration into mainstream MAS, G-Memory is triggered when the MAS G encounters a new user query Q. As emphasized in organizational memory theory [1], efficient knowledge retrieval typically begins with broadly relevant schemas prior to more fine-grained access. Following this principle, G-Memory first perf… view at source ↗
Figure 3
Figure 3. Cost analysis of G-Memory. We showcase the performance versus the overall system token cost when combined with different memory architectures. Takeaway ➊: G-Memory consistently improves performance across all task domains and MAS frameworks. As shown in [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (7 more)
Figure 4
Figure 4. Figure 4: (a) Sensitivity analysis of the hop expansion in Equation ( [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Case study of G-Memory. compared to the full method. Conversely, enabling only insights leads to smaller drops of 3.95% and 3.39%. This indicates that while both components are contributive, interactions offer a slightly greater impact, likely due to their preserving m…
Figure 7
Figure 7. Figure 7: Cost analysis of G-Memory. We showcase the performance versus the overall system token cost when combined with different memory architectures. - Keep each insight concise and impactful . Output Format : - Start immediately with a numbered list . - No introduction or ex…
Figure 8
Figure 8. Figure 8: Visualizations of insight graphs across different LLM backbones, MAS, and benchmarks. [PITH_FULL_IMAGE:figures/full_fig_p021_8.png]
Figure 9
Figure 9. Figure 9: Query graph optimized from ALFWorld dataset. [PITH_FULL_IMAGE:figures/full_fig_p022_9.png]
Figure 10
Figure 10. Figure 10: Query graph optimized from SciWorld dataset. [PITH_FULL_IMAGE:figures/full_fig_p022_10.png]
Figure 11
Figure 11. Figure 11: Query graph optimized from PDDL dataset. [PITH_FULL_IMAGE:figures/full_fig_p023_11.png]

Discussion (0). Sign in to comment.

Forward citations

Cited by 11 Pith papers

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

  1. Knowledge-Centric Self-Improvement

    cs.AI 2026-07 conditional novelty 6.0 of 10

    A protocol that keeps agents generic and disposable while improving a persistent curated knowledge base outperforms agent-centric self-improvement baselines on ARC, coding, and terminal benchmarks.

  2. Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents

    cs.CL 2026-07 conditional novelty 6.0 of 10

    A tabular UCB controller trained on task success improves LLM-agent memory use over fixed heuristics, without extra LLM calls.

  3. Mosaic: Runtime-Efficient Multi-Agent Embodied Planning

    cs.MA 2026-07 accept novelty 6.0 of 10

    Mosaic delivers 27-32% faster multi-agent embodied execution and 4-10 point higher success via agent-centric relative memory plus per-step ILP action allocation.

  4. What to Keep, What to Forget: A Rate--Distortion View of Memory Compaction in LLMs and Agents

    cs.LG 2026-07 conditional novelty 6.0 of 10

    KV-cache eviction, prompt compression, recurrent state bounding, and agent memory consolidation are unified as one rate-distortion problem with a shared lower bound, shared failure mode, and transferable mechanisms.

  5. AgentKVShift: Efficient KV Cache Reuse for Agentic Memory Systems

    cs.AI 2026-05 conditional novelty 6.0 of 10

    AgentKVShift corrects all reused KV cache entries in agentic memory by estimating a chunk-level residual offset from a small probe set, recovering near-fresh accuracy at 10–30% recompute ratios.

  6. ActMem: Bridging the Gap Between Memory Retrieval and Reasoning in LLM Agents

    cs.CL 2026-02 conditional novelty 6.0 of 10

    ActMem stores agent dialogue as a causal and semantic knowledge graph and uses LLM counterfactual reasoning to retrieve implicit constraints, outperforming RAG-style baselines on a new reasoning benchmark.

  7. Cost and Accuracy of Long-Term Memory in Distributed Multi-Agent Systems Based on Large Language Models

    cs.IR 2026-01 reject novelty 5.0 of 10

    A two-framework testbed comparison claims mem0 is Pareto-optimal over Graphiti for distributed LLM agents because its lower cost is paired with accuracy that is not significantly different.

  8. RGMem: Renormalization Group-inspired Memory Evolution for Language Agents

    cs.AI 2025-10 conditional novelty 4.0 of 10

    RGMem, a hierarchical memory framework with thresholded updates inspired by renormalization group ideas, reports state-of-the-art scores on the LOCOMO long-term conversational memory benchmark.

  9. Meta-Policy Reflexion: Reusable Reflective Memory and Rule Admissibility for Resource-Efficient LLM Agent

    cs.AI 2025-09 conditional novelty 4.0 of 10

    Storing LLM reflections as predicate-like rules and applying them via prompt guidance plus hard validity filtering is reported to improve AlfWorld task accuracy over Reflexion, without weight updates.

  10. Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives

    cs.AI 2025-06 conditional novelty 4.0 of 10

    A survey proposes a macro-meso-micro value framework for agentic AI alignment and maps applications, methods, and benchmarks onto it.

  11. Token-Operations-Oriented Inference Optimization Techniques for Large Models

    cs.SE 2026-06 unverdicted novelty 3.0 of 10

    The paper introduces a four-layer technical architecture for token-operations-oriented inference optimization in large models and reviews key technologies and industry status at each layer.

Reference graph

Works this paper leans on

85 extracted references · 18 canonical work pages · cited by 11 Pith papers

  1. [1]

    Organizational memory.Academy of management review, 16(1):57–91, 1991

    James P Walsh and Gerardo Rivera Ungson. Organizational memory.Academy of management review, 16(1):57–91, 1991

    1991

  2. [2]

    Palm-e: An embodied multimodal language model

    Danny Driess, Fei Xia, Mehdi SM Sajjadi, Corey Lynch, Aakanksha Chowdhery, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, Wenlong Huang, et al. Palm-e: An embodied multimodal language model. 2023

    2023

  3. [3]

    Omnidrive: A holistic llm-agent framework for autonomous driving with 3d perception, reasoning and planning

    Shihao Wang, Zhiding Yu, Xiaohui Jiang, Shiyi Lan, Min Shi, Nadine Chang, Jan Kautz, Ying Li, and Jose M Alvarez. Omnidrive: A holistic llm-agent framework for autonomous driving with 3d perception, reasoning and planning. arXiv preprint arXiv:2405.01533, 2024

    arXiv 2024

  4. [4]

    Steve-eye: Equipping llm-based embodied agents with visual perception in open worlds

    Sipeng Zheng, Jiazheng Liu, Yicheng Feng, and Zongqing Lu. Steve-eye: Equipping llm-based embodied agents with visual perception in open worlds. arXiv preprint arXiv:2310.13255, 2023

    arXiv 2023

  5. [5]

    Editable scene simulation for autonomous driving via collaborative llm-agents

    Yuxi Wei, Zi Wang, Yifan Lu, Chenxin Xu, Changxing Liu, Hao Zhao, Siheng Chen, and Yanfeng Wang. Editable scene simulation for autonomous driving via collaborative llm-agents. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15077–15087, 2024

    2024

  6. [6]

    Knowagent: Knowledge-augmented planning for llm-based agents

    Yuqi Zhu, Shuofei Qiao, Yixin Ou, Shumin Deng, Shiwei Lyu, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen, and Ningyu Zhang. Knowagent: Knowledge-augmented planning for llm-based agents. arXiv preprint arXiv:2403.03101, 2024

    arXiv 2024

  7. [7]

    Plan-and-act: Improving planning of agents for long-horizon tasks

    Lutfi Eren Erdogan, Nicholas Lee, Sehoon Kim, Suhong Moon, Hiroki Furuta, Gopala Anu- manchipalli, Kurt Keutzer, and Amir Gholami. Plan-and-act: Improving planning of agents for long-horizon tasks. arXiv preprint arXiv:2503.09572, 2025

    arXiv 2025

  8. [8]

    Understanding the planning of llm agents: A survey

    Xu Huang, Weiwen Liu, Xiaolong Chen, Xingmei Wang, Hao Wang, Defu Lian, Yasheng Wang, Ruiming Tang, and Enhong Chen. Understanding the planning of llm agents: A survey. arXiv preprint arXiv:2402.02716, 2024

    arXiv 2024

Show all 85 references
  1. [9]

    Agent q: Advanced reasoning and learning for autonomous ai agents

    Pranav Putta, Edmund Mills, Naman Garg, Sumeet Motwani, Chelsea Finn, Divyansh Garg, and Rafael Rafailov. Agent q: Advanced reasoning and learning for autonomous ai agents. arXiv preprint arXiv:2408.07199, 2024

    2024 arXiv

  2. [10]

    The landscape of emerging ai agent architectures for reasoning, planning, and tool calling: A survey

    Tula Masterman, Sandi Besen, Mason Sawtell, and Alex Chao. The landscape of emerging ai agent architectures for reasoning, planning, and tool calling: A survey. arXiv preprint arXiv:2404.11584, 2024

    2024 arXiv

  3. [11]

    Embodied agent interface: Benchmarking llms for embodied decision making

    Manling Li, Shiyu Zhao, Qineng Wang, Kangrui Wang, Yu Zhou, Sanjana Srivastava, Cem Gokmen, Tony Lee, Erran Li Li, Ruohan Zhang, et al. Embodied agent interface: Benchmarking llms for embodied decision making. Advances in Neural Information Processing Systems, 37:100428–100534, 2024

    2024

  4. [12]

    Embodied multi-modal agent trained by an llm from a parallel textworld

    Yijun Yang, Tianyi Zhou, Kanxue Li, Dapeng Tao, Lusong Li, Li Shen, Xiaodong He, Jing Jiang, and Yuhui Shi. Embodied multi-modal agent trained by an llm from a parallel textworld. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 26275...

    2024

  5. [13]

    Autogen: Enabling next-gen llm applications via multi-agent conversation framework, August 01, 2023 2023

    Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang. Autogen: Enabling next-gen llm applications via multi-agent conversation framework, August 01, 2023 2023. 10

    2023

  6. [14]

    Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong, Wentao Zhang, Guanting Chen, Xiao Bi, Y . Wu, Y . K. Li, Fuli Luo, Yingfei Xiong, and Wenfeng Liang. Deepseek-coder: When the large language model meets programming – the rise of code intelligence, 2024

    2024

  7. [15]

    Data interpreter: An llm agent for data science

    Sirui Hong, Yizhang Lin, Bang Liu, Bangbang Liu, Binhao Wu, Ceyao Zhang, Chenxing Wei, Danyang Li, Jiaqi Chen, Jiayi Zhang, et al. Data interpreter: An llm agent for data science. arXiv preprint arXiv:2402.18679, 2024

    2024 arXiv

  8. [16]

    V oyager: 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. V oyager: An Open-Ended Embodied Agent with Large Language Models. arXiv e-prints, page arXiv:2305.16291, May 2023

    2023 arXiv

  9. [17]

    Driving with llms: Fusing object-level vector modality for explainable autonomous driving

    Long Chen, Oleg Sinavski, Jan Hünermann, Alice Karnsund, Andrew James Willmott, Danny Birch, Daniel Maund, and Jamie Shotton. Driving with llms: Fusing object-level vector modality for explainable autonomous driving. In 2024 IEEE International Conference on Robotics and Automa...

    2024

  10. [18]

    Optimizing autonomous driving for safety: A human-centric approach with llm-enhanced rlhf

    Yuan Sun, Navid Salami Pargoo, Peter Jin, and Jorge Ortiz. Optimizing autonomous driving for safety: A human-centric approach with llm-enhanced rlhf. In Companion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing, pages 76–80, 2024

    2024

  11. [19]

    O’Brien, Carrie J

    Joon Sung Park, Joseph C. O’Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein. Generative agents: Interactive simulacra of human behavior, April 01, 2023 2023

    2023

  12. [20]

    Tenenbaum, and Igor Mordatch

    Yilun Du, Shuang Li, Antonio Torralba, Joshua B. Tenenbaum, and Igor Mordatch. Improving factuality and reasoning in language models through multiagent debate. CoRR, abs/2305.14325, 2023

    2023 arXiv

  13. [21]

    Metagpt: Meta programming for multi-agent collaborative framework, August 01, 2023 2023

    Sirui Hong, Xiawu Zheng, Jonathan Chen, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, Chenyu Ran, Lingfeng Xiao, and Chenglin Wu. Metagpt: Meta programming for multi-agent collaborative framework, August 01, 2023 2023

    2023

  14. [22]

    Society of mind

    Marvin Minsky. Society of mind. Simon and Schuster, 1988

    1988

  15. [23]

    Examining the society of mind

    Push Singh. Examining the society of mind. Comput. Artif. Intell., 22(6):521–543, 2003

    2003

  16. [24]

    CAMEL: communicative agents for "mind" exploration of large language model society

    Guohao Li, Hasan Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. CAMEL: communicative agents for "mind" exploration of large language model society. In NeurIPS, 2023

    2023

  17. [25]

    Unleashing cognitive synergy in large language models: A task-solving agent through multi-persona self- collaboration, July 01, 2023 2023

    Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, and Heng Ji. Unleashing cognitive synergy in large language models: A task-solving agent through multi-persona self- collaboration, July 01, 2023 2023. work in progress

    2023

  18. [26]

    Chawla, Olaf Wiest, and Xiangliang Zhang

    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. CoRR, abs/2402.01680, 2024

    2024 arXiv

  19. [27]

    Reasoning capacity in multi-agent systems: Limitations, challenges and human-centered solutions

    Pouya Pezeshkpour, Eser Kandogan, Nikita Bhutani, Sajjadur Rahman, Tom Mitchell, and Estevam Hruschka. Reasoning capacity in multi-agent systems: Limitations, challenges and human-centered solutions. CoRR, abs/2402.01108, 2024

    2024 arXiv

  20. [28]

    Cooperate or collapse: Emergence of sustainability behaviors in a society of llm agents

    Giorgio Piatti, Zhijing Jin, Max Kleiman-Weiner, Bernhard Schölkopf, Mrinmaya Sachan, and Rada Mihalcea. Cooperate or collapse: Emergence of sustainability behaviors in a society of llm agents. arXiv preprint arXiv:2404.16698, 2024

    2024 arXiv

  21. [29]

    Discovering causality for efficient coopera- tion in multi-agent environments

    Rafael Pina, Varuna De Silva, and Corentin Artaud. Discovering causality for efficient coopera- tion in multi-agent environments. CoRR, abs/2306.11846, 2023

    2023 arXiv

  22. [30]

    Cut the crap: An economical communication pipeline for llm-based multi-agent systems

    Guibin Zhang, Yanwei Yue, Zhixun Li, Sukwon Yun, Guancheng Wan, Kun Wang, Dawei Cheng, Jeffrey Xu Yu, and Tianlong Chen. Cut the crap: An economical communication pipeline for llm-based multi-agent systems. arXiv preprint arXiv:2410.02506, 2024. 11

    arXiv

  23. [31]

    Masrouter: Learning to route llms for multi-agent systems

    Yanwei Yue, Guibin Zhang, Boyang Liu, Guancheng Wan, Kun Wang, Dawei Cheng, and Yiyan Qi. Masrouter: Learning to route llms for multi-agent systems. arXiv preprint arXiv:2502.11133, 2025

    2025 arXiv

  24. [32]

    Competeai: Understanding the competition behaviors in large language model-based agents

    Qinlin Zhao, Jindong Wang, Yixuan Zhang, Yiqiao Jin, Kaijie Zhu, Hao Chen, and Xing Xie. Competeai: Understanding the competition behaviors in large language model-based agents. arXiv preprint arXiv:2310.17512, 2023

    2023 arXiv

  25. [33]

    Encouraging divergent thinking in large language models through multi-agent debate

    Tian Liang, Zhiwei He, Wenxiang Jiao, Xing Wang, Yan Wang, Rui Wang, Yujiu Yang, Zhaopeng Tu, and Shuming Shi. Encouraging divergent thinking in large language models through multi-agent debate. CoRR, abs/2305.19118, 2023

    2023 arXiv

  26. [34]

    Battleagentbench: A benchmark for evaluating cooperation and competition capabilities of language models in multi-agent systems

    Wei Wang, Dan Zhang, Tao Feng, Boyan Wang, and Jie Tang. Battleagentbench: A benchmark for evaluating cooperation and competition capabilities of language models in multi-agent systems. arXiv preprint arXiv:2408.15971, 2024

    2024 arXiv

  27. [35]

    Progressive-hint prompt- ing improves reasoning in large language models, April 01, 2023 2023

    Chuanyang Zheng, Zhengying Liu, Enze Xie, Zhenguo Li, and Yu Li. Progressive-hint prompt- ing improves reasoning in large language models, April 01, 2023 2023. Tech Report

    2023

  28. [36]

    Memorybank: Enhancing large language models with long-term memory

    Wanjun Zhong, Lianghong Guo, Qiqi Gao, He Ye, and Yanlin Wang. Memorybank: Enhancing large language models with long-term memory. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 19724–19731, 2024

    2024

  29. [37]

    Memgpt: Towards llms as operating systems

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

    2023

  30. [38]

    Mem- llm: Finetuning llms to use an explicit read-write memory

    Ali Modarressi, Abdullatif Köksal, Ayyoob Imani, Mohsen Fayyaz, and Hinrich Schütze. Mem- llm: Finetuning llms to use an explicit read-write memory. arXiv preprint arXiv:2404.11672, 2024

    2024 arXiv

  31. [39]

    A survey on the memory mechanism of large language model based agents

    Zeyu Zhang, Xiaohe Bo, Chen Ma, Rui Li, Xu Chen, Quanyu Dai, Jieming Zhu, Zhenhua Dong, and Ji-Rong Wen. A survey on the memory mechanism of large language model based agents. arXiv preprint arXiv:2404.13501, 2024

    2024 arXiv

  32. [40]

    Chatdb: Augmenting llms with databases as their symbolic memory

    Chenxu Hu, Jie Fu, Chenzhuang Du, Simian Luo, Junbo Zhao, and Hang Zhao. Chatdb: Augmenting llms with databases as their symbolic memory. arXiv preprint arXiv:2306.03901, 2023

    2023 arXiv

  33. [41]

    Memochat: Tuning llms to use memos for consistent long-range open-domain conversation

    Junru Lu, Siyu An, Mingbao Lin, Gabriele Pergola, Yulan He, Di Yin, Xing Sun, and Yunsheng Wu. Memochat: Tuning llms to use memos for consistent long-range open-domain conversation. arXiv preprint arXiv:2308.08239, 2023

    2023 arXiv

  34. [42]

    Recmind: Large language model powered agent for recommendation

    Yancheng Wang, Ziyan Jiang, Zheng Chen, Fan Yang, Yingxue Zhou, Eunah Cho, Xing Fan, Xiaojiang Huang, Yanbin Lu, and Yingzhen Yang. Recmind: Large language model powered agent for recommendation. arXiv preprint arXiv:2308.14296, 2023

    2023 arXiv

  35. [43]

    Expel: Llm agents are experiential learners

    Andrew Zhao, Daniel Huang, Quentin Xu, Matthieu Lin, Yong-Jin Liu, and Gao Huang. Expel: Llm agents are experiential learners. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 19632–19642, 2024

    2024

  36. [44]

    Metaagents: Simulating interactions of human behaviors for llm-based task-oriented coordination via collaborative generative agents

    Yuan Li, Yixuan Zhang, and Lichao Sun. Metaagents: Simulating interactions of human behaviors for llm-based task-oriented coordination via collaborative generative agents. arXiv preprint arXiv:2310.06500, 2023

    2023 arXiv

  37. [45]

    S3: Social-network simulation system with large language model-empowered agents

    Chen Gao, Xiaochong Lan, Zhihong Lu, Jinzhu Mao, Jinghua Piao, Huandong Wang, Depeng Jin, and Yong Li. S3: Social-network simulation system with large language model-empowered agents. arXiv preprint arXiv:2307.14984, 2023

    2023 arXiv

  38. [46]

    Communicative agents for software development, July 01, 2023 2023

    Chen Qian, Xin Cong, Cheng Yang, Weize Chen, Yusheng Su, Juyuan Xu, Zhiyuan Liu, and Maosong Sun. Communicative agents for software development, July 01, 2023 2023. 25 pages, 9 figures, 2 tables

    2023

  39. [47]

    Scaling large-language-model-based multi-agent collab- oration

    Chen Qian, Zihao Xie, Yifei Wang, Wei Liu, Yufan Dang, Zhuoyun Du, Weize Chen, Cheng Yang, Zhiyuan Liu, and Maosong Sun. Scaling large-language-model-based multi-agent collab- oration. arXiv preprint arXiv:2406.07155, 2024. 12

    2024 arXiv

  40. [48]

    Gptswarm: Language agents as optimizable graphs

    Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, and Jürgen Schmidhuber. Gptswarm: Language agents as optimizable graphs. In Forty-first International Conference on Machine Learning, 2024

    2024

  41. [49]

    Automated design of agentic systems

    Shengran Hu, Cong Lu, and Jeff Clune. Automated design of agentic systems. arXiv preprint arXiv:2408.08435, 2024

    2024 arXiv

  42. [50]

    AFlow: Automating Agentic Workflow Generation, October 2024

    Jiayi Zhang, Jinyu Xiang, Zhaoyang Yu, Fengwei Teng, Xionghui Chen, Jiaqi Chen, Mingchen Zhuge, Xin Cheng, Sirui Hong, Jinlin Wang, Bingnan Zheng, Bang Liu, Yuyu Luo, and Chenglin Wu. AFlow: Automating Agentic Workflow Generation, October 2024. arXiv:2410.10762

    2024 arXiv

  43. [51]

    Multi-agent architecture search via agentic supernet

    Guibin Zhang, Luyang Niu, Junfeng Fang, Kun Wang, Lei Bai, and Xiang Wang. Multi-agent architecture search via agentic supernet. arXiv preprint arXiv:2502.04180, 2025

    2025 arXiv

  44. [52]

    Exchange-of-thought: Enhancing large language model capabilities through cross-model communication

    Zhangyue Yin, Qiushi Sun, Cheng Chang, Qipeng Guo, Junqi Dai, Xuan-Jing Huang, and Xipeng Qiu. Exchange-of-thought: Enhancing large language model capabilities through cross-model communication. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Pro...

    2023

  45. [53]

    A survey on large language model based autonomous agents

    Lei Wang, Chen Ma, Xueyang Feng, Zeyu Zhang, Hao Yang, Jingsen Zhang, Zhiyuan Chen, Jiakai Tang, Xu Chen, Yankai Lin, Wayne Xin Zhao, Zhewei Wei, and Ji-Rong Wen. A survey on large language model based autonomous agents. Front. Comput. Sci., 18, 2024

    2024

  46. [54]

    The rise and potential of large language model based agents: A survey

    Zhiheng Xi, Wenxiang Chen, Xin Guo, Wei He, Yiwen Ding, Boyang Hong, Ming Zhang, Junzhe Wang, Senjie Jin, Enyu Zhou, Rui Zheng, Xiaoran Fan, Xiao Wang, Limao Xiong, Yuhao Zhou, Weiran Wang, Changhao Jiang, Yicheng Zou, Xiangyang Liu, Zhangyue Yin, Shihan Dou, Rongxiang Weng, W...

    2023 arXiv

  47. [55]

    Large language models empowered agent-based modeling and simulation: A survey and perspectives

    Chen Gao, Xiaochong Lan, Nian Li, Yuan Yuan, Jingtao Ding, Zhilun Zhou, Fengli Xu, and Yong Li. Large language models empowered agent-based modeling and simulation: A survey and perspectives. CoRR, abs/2312.11970, 2023

    2023 arXiv

  48. [56]

    A survey on llm-based multi-agent systems: workflow, infrastructure, and challenges

    Xinyi Li, Sai Wang, Siqi Zeng, Yu Wu, and Yi Yang. A survey on llm-based multi-agent systems: workflow, infrastructure, and challenges. Vicinagearth, 1(1):9, 2024

    2024

  49. [57]

    Synapse: Trajectory-as-exemplar prompting with memory for computer control

    Longtao Zheng, Rundong Wang, Xinrun Wang, and Bo An. Synapse: Trajectory-as-exemplar prompting with memory for computer control. arXiv preprint arXiv:2306.07863, 2023

    2023 arXiv

  50. [58]

    Ghost in the minecraft: Generally capable agents for open-world environments via large language models with text-based knowledge and memory

    Xizhou Zhu, Yuntao Chen, Hao Tian, Chenxin Tao, Weijie Su, Chenyu Yang, Gao Huang, Bin Li, Lewei Lu, Xiaogang Wang, et al. Ghost in the minecraft: Generally capable agents for open-world environments via large language models with text-based knowledge and memory. arXiv preprin...

    2023 arXiv

  51. [59]

    Chemagent: Self-updating library in large language models improves chemical reasoning

    Xiangru Tang, Tianyu Hu, Muyang Ye, Yanjun Shao, Xunjian Yin, Siru Ouyang, Wangchunshu Zhou, Pan Lu, Zhuosheng Zhang, Yilun Zhao, et al. Chemagent: Self-updating library in large language models improves chemical reasoning. arXiv preprint arXiv:2501.06590, 2025

    2025 arXiv

  52. [60]

    Reflexion: an autonomous agent with dynamic memory and self-reflection

    Noah Shinn, Beck Labash, and Ashwin Gopinath. Reflexion: an autonomous agent with dynamic memory and self-reflection. arXiv preprint, abs/2303.11366, 2023

    2023 arXiv

  53. [61]

    A-mem: Agentic memory for llm agents

    Wujiang Xu, Kai Mei, Hang Gao, Juntao Tan, Zujie Liang, and Yongfeng Zhang. A-mem: Agentic memory for llm agents. arXiv preprint arXiv:2502.12110, 2025

    2025 arXiv

  54. [62]

    Mem0: Building production-ready ai agents with scalable long-term memory

    Prateek Chhikara, Dev Khant, Saket Aryan, Taranjeet Singh, and Deshraj Yadav. Mem0: Building production-ready ai agents with scalable long-term memory. arXiv preprint arXiv:2504.19413, 2025

    2025 arXiv

  55. [63]

    Meminsight: Autonomous memory augmentation for llm agents

    Rana Salama, Jason Cai, Michelle Yuan, Anna Currey, Monica Sunkara, Yi Zhang, and Yassine Benajiba. Meminsight: Autonomous memory augmentation for llm agents. arXiv preprint arXiv:2503.21760, 2025. 13

    2025 arXiv

  56. [64]

    Mixture-of-agents enhances large language model capabilities

    Junlin Wang, Jue Wang, Ben Athiwaratkun, Ce Zhang, and James Zou. Mixture-of-agents enhances large language model capabilities. arXiv preprint arXiv:2406.04692, 2024

    2024 arXiv

  57. [65]

    Symbolic learning enables self-evolving agents

    Wangchunshu Zhou, Yixin Ou, Shengwei Ding, Long Li, Jialong Wu, Tiannan Wang, Jiamin Chen, Shuai Wang, Xiaohua Xu, Ningyu Zhang, et al. Symbolic learning enables self-evolving agents. arXiv preprint arXiv:2406.18532, 2024

    2024 arXiv

  58. [66]

    Self-evolving agents with reflective and memory-augmented abilities

    Xuechen Liang, Meiling Tao, Yinghui Xia, Tianyu Shi, Jun Wang, and JingSong Yang. Self-evolving agents with reflective and memory-augmented abilities. arXiv preprint arXiv:2409.00872, 2024

    2024 arXiv

  59. [67]

    Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors in agents, 2023

    Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chen Qian, Chi-Min Chan, Yujia Qin, Yaxi Lu, Ruobing Xie, Zhiyuan Liu, Maosong Sun, and Jie Zhou. Agentverse: Facilitating multi-agent collaboration and exploring emergent behaviors in agents, 2023

    2023

  60. [68]

    Self-evolving multi-agent collaboration networks for software development

    Yue Hu, Yuzhu Cai, Yaxin Du, Xinyu Zhu, Xiangrui Liu, Zijie Yu, Yuchen Hou, Shuo Tang, and Siheng Chen. Self-evolving multi-agent collaboration networks for software development. arXiv preprint arXiv:2410.16946, 2024

    2024 arXiv

  61. [69]

    G-designer: Architecting multi-agent communication topologies via graph neural networks

    Guibin Zhang, Yanwei Yue, Xiangguo Sun, Guancheng Wan, Miao Yu, Junfeng Fang, Kun Wang, Tianlong Chen, and Dawei Cheng. G-designer: Architecting multi-agent communication topologies via graph neural networks. arXiv preprint arXiv:2410.11782, 2024

    arXiv

  62. [70]

    Evoa- gent: Towards automatic multi-agent generation via evolutionary algorithms

    Siyu Yuan, Kaitao Song, Jiangjie Chen, Xu Tan, Dongsheng Li, and Deqing Yang. Evoa- gent: Towards automatic multi-agent generation via evolutionary algorithms. arXiv preprint arXiv:2406.14228, 2024

    2024 arXiv

  63. [71]

    Evoflow: Evolving diverse agentic workflows on the fly

    Guibin Zhang, Kaijie Chen, Guancheng Wan, Heng Chang, Hong Cheng, Kun Wang, Shuyue Hu, and Lei Bai. Evoflow: Evolving diverse agentic workflows on the fly. arXiv preprint arXiv:2502.07373, 2025

    2025 arXiv

  64. [72]

    Dynamic llm-agent network: An llm-agent collaboration framework with agent team optimization

    Zijun Liu, Yanzhe Zhang, Peng Li, Yang Liu, and Diyi Yang. Dynamic llm-agent network: An llm-agent collaboration framework with agent team optimization. CoRR, abs/2310.02170, 2023

    2023 arXiv

  65. [73]

    Microsoft academic graph: When experts are not enough

    Kuansan Wang, Zhihong Shen, Chiyuan Huang, Chieh-Han Wu, Yuxiao Dong, and Anshul Kanakia. Microsoft academic graph: When experts are not enough. Quantitative Science Studies, 1(1):396–413, 2020

    2020

  66. [74]

    Sirius: Self-improving multi- agent systems via bootstrapped reasoning

    Wanjia Zhao, Mert Yuksekgonul, Shirley Wu, and James Zou. Sirius: Self-improving multi- agent systems via bootstrapped reasoning. arXiv preprint arXiv:2502.04780, 2025

    2025 arXiv

  67. [75]

    Reso: A reward-driven self- organizing llm-based multi-agent system for reasoning tasks

    Heng Zhou, Hejia Geng, Xiangyuan Xue, Zhenfei Yin, and Lei Bai. Reso: A reward-driven self- organizing llm-based multi-agent system for reasoning tasks. arXiv preprint arXiv:2503.02390, 2025

    2025 arXiv

  68. [76]

    Hotpotqa: A dataset for diverse, explainable multi-hop question answering

    Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W Cohen, Ruslan Salakhut- dinov, and Christopher D Manning. Hotpotqa: A dataset for diverse, explainable multi-hop question answering. arXiv preprint arXiv:1809.09600, 2018

    2018 arXiv

  69. [77]

    Fever: a large-scale dataset for fact extraction and verification

    James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. Fever: a large-scale dataset for fact extraction and verification. arXiv preprint arXiv:1803.05355, 2018

    2018 arXiv

  70. [78]

    Alfworld: Aligning text and embodied environments for interactive learning

    Mohit Shridhar, Xingdi Yuan, Marc-Alexandre Côté, Yonatan Bisk, Adam Trischler, and Matthew Hausknecht. Alfworld: Aligning text and embodied environments for interactive learning. arXiv preprint arXiv:2010.03768, 2020

    2010 arXiv

  71. [79]

    Scienceworld: Is your agent smarter than a 5th grader? arXiv preprint arXiv:2203.07540, 2022

    Ruoyao Wang, Peter Jansen, Marc-Alexandre Côté, and Prithviraj Ammanabrolu. Scienceworld: Is your agent smarter than a 5th grader? arXiv preprint arXiv:2203.07540, 2022

    2022 arXiv

  72. [80]

    Agentboard: An analytical evaluation board of multi-turn llm agents

    Chang Ma, Junlei Zhang, Zhihao Zhu, Cheng Yang, Yujiu Yang, Yaohui Jin, Zhenzhong Lan, Lingpeng Kong, and Junxian He. Agentboard: An analytical evaluation board of multi-turn llm agents. arXiv preprint arXiv:2401.13178, 2024. 14

    2024 arXiv

  73. [81]

    You are a smart agent designed to solve problems

    Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, and Ming Zhou. Minilm: Deep self-attention distillation for task-agnostic compression of pre-trained transformers. Advances in Neural Information Processing Systems, 33:5776–5788, 2020. Impact Statement G-Memory introduces ...

    2020

  74. [82]

    "" l e a r n _ l e s s o n s _ u s e r _ p r o m p t _ c o m p a r e =

    Insight 3 ... """ l e a r n _ l e s s o n s _ u s e r _ p r o m p t _ c o m p a r e = """ ## S u c c e s s f u l t r a j e c t o r y { t r u e _ t r a j } ## Failed t r a j e c t o r y ### t r a j e c t o r y { f a l s e _ t r a j } Your output : """ l e a r n _ l e s s o n s ...

    1920

  75. [83]

    "" l e a r n _ l e s s o n s _ u s e r _ p r o m p t _ a l l _ s u c c =

    Insight 3 ... """ l e a r n _ l e s s o n s _ u s e r _ p r o m p t _ a l l _ s u c c = """ ## S u c c e s s f u l t r a j e c t o r y s { t r u e _ t r a j s } 22 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1920 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 4...

    1920

  76. [84]

    "" m e r g e _ r u l e s _ u s e r _ p r o m p t =

    Insight 3 ... """ m e r g e _ r u l e s _ u s e r _ p r o m p t = """ ## Here are the current i ns igh ts that need to be merged : { c u r r e n t _ r u l e s } ## Please c o n s o l i d a t e and rewrite them into ** no more than { l i m i t e d _ n u m b e r } refined ins ig...

  77. [85]

    "" p r o j e c t _ i n s i g h t s _ u s e r _ p r o m p t : str =

    Insight 3 ... """ p r o j e c t _ i n s i g h t s _ u s e r _ p r o m p t : str = """ ### T r a j e c t o r y { t r a j e c t o r y } ### Agent ’ s Role : { role } ### General In si gh ts : { ins ig ht s } ### Your Output ( P e r s o n a l i z e d In si ght s for This Role ) :...

Pith tools

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