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REVIEW 4 major objections 6 minor 28 references

Can Memory-Augmented LLM Agents Aid Journalism in Interpreting and Framing News for Diverse Audiences?

T0 review · 4 major / 6 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read An LLM-agent framework that stages discussions among simulated audience members can locate each reader group's comprehension gaps and generate targeted explainers that outperform generic rewrites, with human quiz accuracy rising to 85.7%.

desk verdict Interesting application idea with a promising pilot human result, but the automated comprehension metric is confounded and the statistical claims outrun the evidence; worth a serious referee, not a desk reject. read the letter →

arxiv 2507.21055 v2 pith:ZVGT7CR2 submitted 2025-04-30 cs.CY cs.AIcs.SI

Leyi Ouyang This is my paper · ORCID
classification cs.CYcs.AIcs.SI
keywords NewsInterpretationInformationGapAudienceDiversityCommunicationBehaviorLargeLanguageAgentDesignMulti-AgentDiscussionMemory-AugmentedAgentsSupplementaryMaterialGeneration
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 claims that a simulated audience of memory-augmented LLM agents, each playing a role such as finance expert or child reader, can expose where a complex news article will confuse real people. The framework, MADES (Mnemonic Agent Debate Engine), runs these agents through iterative discussions, logs their questions and misunderstandings, and converts the record into supplementary explanations tailored to each audience profile. In the paper's tests, agents receiving this discussion-derived supplement improved their comprehension scores across all four expert domains and all age groups, while a vanilla-LLM supplement sometimes made comprehension worse. A human quiz backs the claim: readers given the MADES supplement scored 85.7% accuracy, versus 64.5% for news-only readers and 69.2% for readers given a generic LLM supplement. If this holds, news outlets could publish audience-specific explainers at release time instead of waiting for feedback.

What carries the argument

The machinery is MADES, a multi-agent simulation with three memory layers—semantic memory for domain knowledge, episodic memory for past news events, and procedural memory for analytical how-to instructions—plus an attention mechanism. Agents with distinct expertise (finance, law, agriculture, technology) and age profiles first read the article alone, then enter iterative discussion rounds in which they ask cross-domain questions and expert agents answer from their knowledge bases. Each round is summarized and the accumulated record of gaps, questions, and clarifications becomes the basis for targeted supplementary material that is given to a fresh control group of identical agents. The framework draws on the social-construction-of-reality idea that interaction surfaces and corrects misunderstandings, and on the cognitive-communication idea that questioning and clarifying deepen comprehension. The supplement is the key artifact that converts discussion diagnostics into a testable intervention.

What would settle it

Give an agent only the supplementary material—without the original article—and ask it to summarize the news; if its summary still scores about as high against the article's text as summaries from agents that actually read the article, then the similarity metric cannot distinguish comprehension from overlap with the supplement, and the automated evidence collapses, leaving the human quiz as the only remaining support.

Watch

Extended reading notes

Core claim

The central claim is that the supplementary material generated through the framework's iterative agent discussion process consistently and significantly increases agents' news understanding across all expert domains, and that the same material measurably improves human comprehension. The paper positions this as evidence that explicitly identifying an audience's comprehension gaps—rather than asking a single LLM to rewrite or expand the article—is what makes supplemental explanation effective. The automated comparison shows discussion-informed supplements raising cosine-similarity comprehension scores for every agent type, with the technology expert jumping from a baseline of 0.1351 to 0.7988; the vanilla-LLM supplement, by contrast, lowered the finance expert's score. The human evaluation reports 85.7% mean quiz accuracy for the MADES-supplement group versus 64.5% for the control and 69.2% for the vanilla-LLM group, and higher human ratings on summarization, faithfulness, completeness, and coherence. These results are presented as answering three research questions: that discussion identifies gaps, that the identified gaps generate effective supplements, and that the framework adapts to user-defined audience profiles.

Load-bearing premise

The automated part of the argument rests on one premise: that a high semantic-similarity score between an agent's comprehension response and the original news text, once a word-overlap check rules out copying, is the same thing as the agent understanding the news.

Editorial extensions

If this is right

  • Newsrooms could attach audience-specific explainer boxes to an article at publication, covering the legal and technical dimensions that each reader group is likely to miss.
  • Generic LLM rewriting is not a safe default: the paper finds it can lower comprehension for some domains, so gap identification should precede any automated supplement generation.
  • The framework's three-iteration design gives a practical stopping point: most comprehension gains appear by the third discussion round, limiting computational cost.
  • Because users can define their own agent profiles, the same mechanism could explain policy documents, health guidance, or product instructions to custom audiences, not just news.
  • The combination of high semantic similarity and low lexical overlap in agent responses suggests the supplement is being understood and re-expressed, not copied, which is the paper's evidence for genuine comprehension.

Reading between the lines

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

  • If the similarity metric is accepted, the same agent-discussion record could also be used as a diagnostic report for writers, telling them precisely which terms and cross-domain links to rephrase for a given audience.
  • The loop could be run before publication as an internal review step, where each domain agent checks a draft for missing context or likely misunderstandings rather than explaining an already published article.
  • A lower-cost variant worth testing is whether a single agent prompted to generate the list of cross-domain questions can reproduce the discussion's gap diagnosis; if so, the debate rounds may matter less for diagnosis than for producing the language used in the supplement.
  • If the age-group pattern generalizes, the largest practical gains would come from prioritizing supplements for the youngest and oldest readers, who showed the biggest relative improvements in the paper's simulation results.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 6 minor

Summary. The paper proposes MADES, a memory-augmented multi-agent LLM framework that simulates discussions among audience agents with different domain expertise and age profiles to identify comprehension gaps in news articles and generate targeted supplementary material. The authors report that the discussion-informed supplements significantly improve agent understanding, measured by cosine similarity between agent responses and the original news text, with ROUGE as a lexical-overlap control. They also report a 60-participant human quiz in which the MADES supplement group achieved 85.7% accuracy versus 64.5% for the control and 69.2% for a vanilla-LLM supplement group. The paper claims to answer RQ1–RQ3, including an analysis of optimal discussion iterations and age-group effects.

Significance. If the central evidential claims were sound, MADES would offer a practical tool for journalists to tailor news supplements to diverse audiences, and the comparison against a vanilla LLM baseline would be a useful design principle. The paper's conceptual contribution is interesting, and the inclusion of a 5,000-article corpus, a human comprehension study, and a detailed case study is commendable. However, the automated understanding metric is self-referential, the reported automatic scores contain numerical inconsistencies, and the human study is too thinly documented to support the stated conclusions. As submitted, the paper does not establish its central claims.

major comments (4)
  1. [Section 3.2] The primary automated metric is confounded and cannot support RQ1 and RQ2 as stated. Cosine similarity is computed between the agent's comprehension response and the original news article, but the supplementary material is generated from that same article and necessarily contains its key facts in paraphrased form. An agent that reads the supplement can therefore produce a response that is semantically close to the news embedding without independent comprehension. The ROUGE control only excludes verbatim n-gram overlap; it does not control for semantic paraphrase, so the sentence in Section 3.2 claiming that the dual-metric approach ensures high cosine similarity can be 'confidently interpreted as a valid indicator of enhanced understanding' is not justified. This concern is amplified by Appendix C, which reports that agents operated 'more in serial monologue than true dialogue' and that the process was better at identifying integration needs than performing integration dynamically.
  2. [Tables 1-3] The numeric evidence underpinning the agent experiments is internally inconsistent. Table 2's improvements do not match Table 1's cross-condition scores: for Finance, Table 1 gives a vanilla-supplement change of 0.6128 - 0.7022 = -0.0894, while Table 2 reports +0.0206; for Technology with the discussion supplement, Table 1 implies +0.6637, while Table 2 reports +0.3220. Table 3 contains duplicated entries under the vanilla condition: Law and Technical both show 0.0488 on the Finance part, both show 0.0748 on the Agriculture part, and Finance and Law both show 0.1028 on the Technical part. These are not cosmetic issues; they indicate that the reported averages cannot be verified from the underlying tables.
  3. [Section 3.3 and Tables 4-5] The human evaluation is reported as statistically significant without providing any test statistic, p-value, confidence interval, or effect size. The quiz has 20 participants per group, and no quiz items, scoring rubric, or randomization details are provided. The qualitative survey uses only three raters, and no inter-rater reliability measure is reported. The human evaluation is the only independent instrument in the paper, and as documented it cannot bear the weight of the paper's main claim that the MADES supplement produced 'tangible and superior learning gains.'
  4. [Appendix D, Tables 6-7] The age-group results are also internally inconsistent. Table 6 implies an improvement for the 6-12 group of 0.7995 - 0.6793 = 0.1202 for the discussion supplement, while Table 7 reports 0.1381; for the 18-35 group Table 6 implies 0.0216, while Table 7 reports 0.0183; for the above-35 group Table 6 implies 0.0269, while Table 7 reports 0.0423. The claimed statistical significance for all age groups is asserted without any test statistic. These discrepancies further undermine the reliability of the automated evaluation.
minor comments (6)
  1. [Title and tables] There are several typos and inconsistencies in presentation: the title has an unwanted space in 'A GENTS'; Table 7 uses 'Vanillar' instead of 'Vanilla'; and the affiliation 'South China Agriculture University' is likely intended to be 'South China Agricultural University.'
  2. [Section 3.1] The description of the 5,000-article corpus lacks source details, article identifiers, date ranges, and any access information, which prevents reproducibility of the sampling procedure.
  3. [Figure 4] Figure 4 is referenced to justify the claim that the optimal number of discussion iterations is around three, but the figure is not shown and the axes and legend are not described, so the claim cannot be checked.
  4. [Table 4] The asterisk next to 85.7% in Table 4 is never explained in the caption or the text.
  5. [Section 3.5 and RQ3] RQ3 concerns adaptability to user-defined target audiences, but the reported analysis addresses the optimal number of discussion iterations rather than testing the framework with custom audience profiles; no evidence is provided for the claimed adaptability.
  6. [References and Impact Statement] Several references are incomplete or not clearly relevant, such as [9] and [10], and the Impact Statement claims steps taken to ensure unbiased training data even though no model training is described in the paper.

Circularity Check

1 steps flagged · score 6.0 of 10

Agent-level 'understanding' gains are partially self-referential: the metric is cosine similarity to the original news, while the supplement is constructed from that same news; ROUGE only excludes verbatim overlap, not paraphrase.

  1. self definitional [Section 3.2 (Evaluation Metric), with supplement construction in Section 2.3; results in Tables 1–3]
    "To objectively quantify the degree of understanding, which we define as the semantic relevance between the news content and the agents’ expressed comprehension, we utilize text embedding techniques. These techniques transform both the original news text and the agents’ comprehension responses into high-dimensional vector representations. [...] Subsequently, we calculate the cosine similarity between these vectors, which provides a direct, numerical measure of their semantic similarity and serves as our primary metric for evaluating comprehension accuracy."

    The target quantity is cosine similarity between the agent's comprehension response and the original news embedding. The supplement is, by the paper's own description, built from the same original news and from the agents' discussion of that news. Therefore an agent that reads the supplement and restates its content in its own words will produce a response that is semantically close to the original article; this raises the metric regardless of whether the agent has any independent comprehension. ROUGE is used as the only control, but ROUGE measures lexical n-gram overlap, not semantic paraphrase, so it cannot distinguish genuine understanding from a paraphrased summary of the supplement.

full rationale

The primary automated derivation chain for RQ1 and RQ2 reduces, in part, to the paper's own metric: understanding is defined as cosine similarity between the agent's response and the original news text, and the supplementary material is generated from that same news plus the discussion of it. Any agent that faithfully incorporates the supplement into its response should therefore score higher on the metric, independent of whether it truly understands the content. The paper's ROUGE control addresses only verbatim lexical overlap, not semantic paraphrase, so it does not break the circularity. This makes the agent-level 'statistically significant' improvement claims partially self-referential. The duplicated numeric entries in Table 3 (e.g., Law and Technical agents both at 0.0488 on the Finance part, and both at 0.0748 on the Agriculture part, under the vanilla condition) further undermine confidence in the automated scores, though they are data-quality issues rather than additional circular steps. Appendix C also concedes that agents 'operated more in serial monologue than true dialogue' and that the process 'was more effective at identifying areas requiring integration than at performing the integration dynamically,' which weakens the causal mechanism behind the automated gains. The paper does, however, include an independent human quiz in which the MADES supplement group scored 85.7% versus 64.5% for control, and that result does not reduce to the cosine-similarity definition. Although the human study is thinly documented, it provides non-circular support for the central claim, so the circularity is partial rather than total. No load-bearing self-citation was found: the author's own prior work appears only as a general citation, and no uniqueness theorem or same-author external validation is imported to force the framework's design. The appropriate score is therefore 6, reflecting one construction-driven automated evaluation while acknowledging the independent human evidence.

Assumptions & free parameters 1 free parameters · 4 assumptions · 1 invented entities

The framework's claimed success rests on the validity of embedding-based comprehension measurement, the faithfulness of LLM agents as proxies for real audiences, and the choice of three discussion iterations as optimal. None of these is validated against an external benchmark; the paper's evidence is internal to its own pipeline.

free parameters (1)
  • optimal_discussion_iterations = 3
    The paper states the number of discussion iterations was 'determined to be optimal in our analysis' (Section 2.3) and Figure 4 shows comprehension flattening after the third iteration. Selecting this on the same evaluation data can inflate reported gains.
assumptions (4)
  • domain assumption Cosine similarity between news text and agent response measures comprehension.
    Central evaluation metric in Section 3.2; unvalidated and confounded by supplement content overlap.
  • domain assumption Low ROUGE combined with high cosine similarity proves genuine understanding rather than paraphrase.
    Section 3.2; low ROUGE does not rule out semantic copying or generic topical language.
  • domain assumption LLM agents with configured memories and prompts faithfully simulate diverse human audiences.
    Framework design Sections 2.1 and 2.2; no validation against real audience comprehension data beyond a small quiz.
  • domain assumption Iterative multi-agent discussion surfaces the same comprehension gaps a real audience would have.
    Core mechanism of the framework; not independently validated against external audience data.
invented entities (1)
  • MADES memory-augmented agents
    purpose: Simulate heterogeneous audience members (occupations, age groups) to surface comprehension gaps through iterative discussion.
    No external benchmark validates that agent-identified gaps match real reader gaps; the only evidence is internal consistency in the paper's own evaluation.

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Cite this review

Pith. "Pith review of Can Memory-Augmented LLM Agents Aid Journalism in Interpreting and Framing News for Diverse Audiences?." pith.science (2026). https://pith.science/paper/ZVGT7CR2

@misc{pith2026250721055,
  author       = {Pith},
  title        = {Pith review of: Can Memory-Augmented LLM Agents Aid Journalism in Interpreting and Framing News for Diverse Audiences?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ZVGT7CR2}},
  note         = {Machine review of arXiv:2507.21055}
}
read the original abstract

Modern news is often comprehensive, weaving together information from diverse domains, including technology, finance, and agriculture. This very comprehensiveness creates a challenge for interpretation, as audiences typically possess specialized knowledge related to their expertise, age, or standpoint. Consequently, a reader might fully understand the financial implications of a story but fail to grasp or even actively misunderstand its legal or technological dimensions, resulting in critical comprehension gaps. In this work, we investigate how to identify these comprehension gaps and provide solutions to improve audiences' understanding of news content, particularly in the aspects of articles outside their primary domains of knowledge. We propose MADES, an agent-based framework designed to simulate societal communication. The framework utilizes diverse agents, each configured to represent a specific occupation or age group. Each agent is equipped with a memory system. These agents are then simulated to discuss the news. This process enables us to monitor and analyze their behavior and cognitive processes. Our findings indicate that the framework can identify confusions and misunderstandings within news content through its iterative discussion process. Based on these accurate identifications, the framework then designs supplementary material. We validated these outcomes using both statistical analysis and human evaluation, and the results show that agents exhibit significantly improved news understanding after receiving this supplementary material.

Figures

Figures reproduced from arXiv: 2507.21055 by the authors.

Figure 2
Figure 2. Hierarchical Structure of Memory Shall we have a discussion? End Yes Discussion No News Discussion of text Stores general knowledge and facts, separate from personal experiences. Example: "...The World Bank Group will double its annual agribusiness financing to $12 billion by 2030...." Analysis: This is a general fact (not tied to personal experience) that could become part of someone’s semantic memory after reading… view at source ↗
Figure 3
Figure 3. Workflow of The Agent Discussion Process [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Determine Optimal Iteration Round A Related Work The dissemination and interpretation of news are foundational to informed public discourse and civic engagement. However, the complexity of modern information, spanning diverse domains such as technology, finance, and agriculture, often leads to significant variations in comprehension across different audience segments. This review examines existing literature at the … view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

28 extracted references · 24 canonical work pages

  1. [1]

    Are news audiences increasingly fragmented? a cross-national comparative analysis of cross-platform news audience fragmentation and duplication

    Richard Fletcher and Rasmus Kleis Nielsen. Are news audiences increasingly fragmented? a cross-national comparative analysis of cross-platform news audience fragmentation and duplication. Journal of communication, 67(4):476–498, 2017

    work page 2017

  2. [2]

    News dissemination: a semantic approach to barrier classification

    Abdul Sittar, Dunja Mladeni ´c, and Marko Grobelnik. News dissemination: a semantic approach to barrier classification. Journal of Intelligent Information Systems, pages 1–31, 2024

    work page 2024

  3. [3]

    Media Bias: Examining the Facts

    Thomas Arndt. Media Bias: Examining the Facts. Bloomsbury Publishing USA, 2025

    work page 2025

  4. [4]

    Making sense of policy practices: Interpretation and meaning

    Dvora Yanow. Making sense of policy practices: Interpretation and meaning. In Handbook of critical policy studies, pages 401–421. Edward Elgar Publishing, 2015. 19 Can Memory-Augmented LLM Agents Aid Journalism in Interpreting and Framing News for Diverse Audiences?

    work page 2015

  5. [5]

    Interpreting public sentiment in diplomacy events: A counterfactual analysis framework using large language models

    Leyi OuYang. Interpreting public sentiment in diplomacy events: A counterfactual analysis framework using large language models. Available at SSRN 5286005, 2025

    work page 2025

  6. [6]

    glorified minute takers

    Kelsey Mesmer, M Rosie Jahng, Jill Wurm, and Najma Akther. “glorified minute takers”: Journalists’(mis) handling of scientific uncertainty during the covid-19 pandemic. Science Communication, 46(6):790–815, 2024

    work page 2024

  7. [7]

    Large language models (LLMs)

    Maria Johnsen. Large language models (LLMs). Maria Johnsen, 2024

    work page 2024

  8. [8]

    Large language model (llm) ai text generation detection based on transformer deep learning algorithm

    Yuhong Mo, Hao Qin, Yushan Dong, Ziyi Zhu, and Zhenglin Li. Large language model (llm) ai text generation detection based on transformer deep learning algorithm. arXiv preprint arXiv:2405.06652, 2024

    arXiv 2024

Show all 28 references
  1. [9]

    Cognitive communication theory, 2022

    Yoesoep Edhie Rachmad. Cognitive communication theory, 2022

    2022

  2. [10]

    A sparsity algorithm for finding optimal counterfactual explanations: Application to corporate credit rating

    Dan Wang, Zhi Chen, Ionu¸ t Florescu, and Bingyang Wen. A sparsity algorithm for finding optimal counterfactual explanations: Application to corporate credit rating. Research in International Business and Finance, 64:101869, 2023

    2023

  3. [11]

    The social construction of reality

    Peter Berger and Thomas Luckmann. The social construction of reality. In Social theory re-wired, pages 110–122. Routledge, 2016

    2016

  4. [12]

    Episodic and semantic memory

    Endel Tulving et al. Episodic and semantic memory. Organization of memory, 1(381-403):1, 1972

    1972

  5. [13]

    Memory systems of the brain: a brief history and current perspective

    Larry R Squire. Memory systems of the brain: a brief history and current perspective. Neurobiology of learning and memory, 82(3):171–177, 2004

    2004

  6. [14]

    Social cognition 2.0: An interactive memory systems account

    David M Amodio. Social cognition 2.0: An interactive memory systems account. Trends in Cognitive Sciences, 23(1):21–33, 2019

    2019

  7. [15]

    Semantic memory and stored knowledge

    Michael W Eysenck. Semantic memory and stored knowledge. In Memory, pages 207–235. Routledge, 2020

    2020

  8. [16]

    A-mem: Agentic memory for llm agents

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

    2025 arXiv

  9. [17]

    What about “space” is important for episodic memory? Wiley Interdisciplinary Reviews: Cognitive Science, 14(3):e1645, 2023

    Carina L Fan, H Moriah Sokolowski, R Shayna Rosenbaum, and Brian Levine. What about “space” is important for episodic memory? Wiley Interdisciplinary Reviews: Cognitive Science, 14(3):e1645, 2023

    2023

  10. [18]

    Theories of episodic memory

    Andrew R Mayes and Neil Roberts. Theories of episodic memory. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 356(1413):1395–1408, 2001

    2001

  11. [19]

    Procedural memory

    Joshua Ewen and Stewart Mostofsky. Procedural memory. In Encyclopedia of Autism Spectrum Disorders, pages 3678–3684. Springer, 2021

    2021

  12. [20]

    The development of memory and reasoning skill in argumentative contexts: Evaluating, explaining, and generating evidence

    Nancy L Stein and Christopher A Miller. The development of memory and reasoning skill in argumentative contexts: Evaluating, explaining, and generating evidence. In Advances in instructional psychology , pages 285–335. Routledge, 2019

    2019

  13. [21]

    News classification and its techniques: a review

    Gurmeet Kaur and Karan Bajaj. News classification and its techniques: a review. IOSR Journal of Computer Engineering, 18(1):22–26, 2016

    2016

  14. [22]

    Human conflict: Disagreement, misunderstanding, and problematic talk

    C David Mortensen. Human conflict: Disagreement, misunderstanding, and problematic talk . Rowman & Littlefield, 2006

    2006

  15. [23]

    Ecc analyzer: Extracting trading signal from earnings conference calls using large language model for stock volatility prediction

    Yupeng Cao, Zhi Chen, Qingyun Pei, Nathan Lee, KP Subbalakshmi, and Papa Momar Ndiaye. Ecc analyzer: Extracting trading signal from earnings conference calls using large language model for stock volatility prediction. In Proceedings of the 5th ACM International Conference on A...

    2024

  16. [24]

    Risklabs: Predicting financial risk using large language model based on multi-sources data

    Yupeng Cao, Zhi Chen, Qingyun Pei, Fabrizio Dimino, Lorenzo Ausiello, Prashant Kumar, KP Subbalakshmi, and Papa Momar Ndiaye. Risklabs: Predicting financial risk using large language model based on multi-sources data. Technical report, 2024

    2024

  17. [25]

    Catmemo@ ijcai 2024 finllm challenge: Fine-tuning large language models using data fusion in financial applications

    Yupeng Cao, Zhiyuan Yao, Zhi Chen, and Zhiyang Deng. Catmemo@ ijcai 2024 finllm challenge: Fine-tuning large language models using data fusion in financial applications. In Proceedings of the Eighth Financial Technology and Natural Language Processing and the 1st Agent AI for ...

    2024

  18. [26]

    Finmem: A performance-enhanced llm trading agent with layered memory and character design

    Yangyang Yu, Haohang Li, Zhi Chen, Yuechen Jiang, Yang Li, Denghui Zhang, Rong Liu, Jordan W Suchow, and Khaldoun Khashanah. Finmem: A performance-enhanced llm trading agent with layered memory and character design. In Proceedings of the AAAI Symposium Series, volume 3, pages ...

    2024

  19. [27]

    Fincon: A synthesized llm multi-agent system with conceptual verbal reinforcement for enhanced financial decision making

    Yangyang Yu, Zhiyuan Yao, Haohang Li, Zhiyang Deng, Yuechen Jiang, Yupeng Cao, Zhi Chen, Jordan Suchow, Zhenyu Cui, Rong Liu, et al. Fincon: A synthesized llm multi-agent system with conceptual verbal reinforcement for enhanced financial decision making. Advances in Neural Inf...

    2024

  20. [28]

    Generative agents: Interactive simulacra of human behavior

    Joon Sung Park, Joseph O’Brien, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, and Michael S Bernstein. Generative agents: Interactive simulacra of human behavior. In Proceedings of the 36th annual acm symposium on user interface software and technology, pages 1–22, 2023. 20

    2023

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Reviewed August 16, 2026 · model on record in the stance chip above.