Pith. sign in

REVIEW 4 major objections 4 minor 40 references

Synergizing Logical Reasoning, Knowledge Management and Collaboration in Multi-Agent LLM System

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read The paper claims that a hierarchical multi-agent LLM system combining Answer Set Programming, corrective retrieval, and Theory-of-Mind prompting produces deeper, better-justified analysis than single-model chain-of-thought or…

desk verdict A coherent integration of ASP, corrective RAG, and ToM in a hierarchical MAS, but the 'significant enhancement' claim relies on qualitative comparison without metrics. read the letter →

arxiv 2507.02170 v1 pith:AZZKCURW submitted 2025-07-02 cs.MA

Adam Kostka , Jaros{l}aw A. Chudziak This is my paper
classification cs.MA
keywords multi-agentLLMsystemsanswersetprogramminglogicalreasoningretrieval-augmentedgenerationcorrectiveRAGtheoryofmindgraphknowledgebaseproductdevelopmentcasestudy
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 proposes SynergyMAS, a hierarchical multi-agent system in which each LLM agent is equipped with an Answer Set Programming (ASP) logic solver over a graph knowledge base, a corrective retrieval-augmented generation pipeline, and an explicit Theory of Mind belief state. The claim is that uniting these three components with a boss-agent coordination protocol lets a team of specialized agents outperform a single LLM prompted with chain-of-thought or tree-of-thought reasoning on complex, domain-specific problems. The paper defends this claim with a product development case study on a Smart Home Energy Management System, arguing that SynergyMAS delivers the deepest analysis and the most comprehensive, best-justified strategy. A sympathetic reader should care because it is a concrete proposal for overcoming the hallucination, knowledge gaps, and coordination failures that limit single-model LLM deployments.

What carries the argument

The load-bearing mechanism is the agent workflow built around a hierarchical 'boss' agent. Each specialist agent answers in three labelled sections—My Beliefs, Response, and Future Work—where the belief section implements Theory of Mind by having agents state their own and inferred teammates' mental states. When the graph knowledge base returns too little evidence, the question is translated first to a Cypher query and then to an Answer Set Programming (ASP; a logic-programming formalism whose solution is a set of stable models satisfying all rules) program, which the Clingo solver resolves and translates back to natural language. Alongside this, a Corrective RAG layer grades documents retrieved from a Chroma vector store and falls back to Tavily web search when internal evidence is weak. These pieces are coordinated by the boss agent, which decides the next task after every response.

What would settle it

Run the system on a fixed set of natural-language questions with known answers, extract the generated Cypher and ASP code before solving, and compute how often the translated code encodes the intended query; if translation accuracy is low yet output quality stays high, then the logic solver is not what drives the performance.

Watch

Extended reading notes

Core claim

The central discovery is that adding an external logical solver, a self-correcting retrieval system, and belief-state communication to a multi-agent LLM changes the quality of collaborative problem-solving. In the paper's own terms, SynergyMAS 'offers the deepest analysis, exploring factors from multiple perspectives and providing a comprehensive strategy' for the product development task, while single-model baselines remain narrower in scope. The claim is that the combination, not any one component, is what creates the advantage.

Load-bearing premise

The logical reasoning pillar works only if the LLM's translations from natural-language questions into Cypher queries and ASP programs preserve the intended meaning, and the paper does not measure this translation accuracy.

Editorial extensions

If this is right

  • Hybrid systems that interleave neural generation with symbolic solvers will handle longer, multi-step domain conversations more faithfully than prompt-only single models.
  • Specialized agents with personal RAG databases can contribute expert-level knowledge without flooding the conversation with irrelevant retrieved text.
  • Explicit Theory-of-Mind belief reporting lets a coordinator reconcile different specialist opinions and keep the team aligned.
  • Corrective RAG with web-search fallback lets the system answer questions outside its training data, reducing knowledge-gap hallucinations.
  • The architecture should transfer to other domains that need multi-perspective iteration, such as healthcare diagnostics or market analysis, because the reasoning, retrieval, and belief loop is domain-agnostic.

Reading between the lines

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

  • Editorial inference: the paper's own evidence suggests that translation fidelity from natural language to Cypher and ASP is the true bottleneck; measuring that translation accuracy directly would predict when the logical reasoning component helps.
  • Editorial inference: the 'deepest analysis' comparison could be made quantitative by having blind raters score responses on pre-registered rubrics for completeness, justification, and redundancy, which would test the central claim more directly.
  • Editorial inference: applying the same belief-state and Corrective RAG pattern to a single-model system would isolate whether the multi-agent hierarchy or the tool integration drives the performance gain.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper proposes SynergyMAS, a hierarchical multi-agent LLM system that combines Answer Set Programming (ASP) logical reasoning over a Neo4j graph knowledge base, Corrective RAG (CRAG) for knowledge management, and Theory of Mind (ToM) belief-state prompts, with a 'boss' agent coordinating specialized agents. The system is demonstrated on a Smart Home Energy Management System product-development case study using the Lean Startup methodology. The authors claim that SynergyMAS 'significantly enhances performance and adaptability' compared with ChatGPT-4o, Chain-of-Thought, and Tree-of-Thought baselines, supported in Section 8 by qualitative prose comparisons across structural analysis, content, analytical depth, and unique contributions. The paper also releases code and outlines future scalability work.

Significance. If the central claim were properly supported, the paper would make a useful contribution by showing a concrete integration of symbolic reasoning, retrieval augmentation, and ToM in a multi-agent LLM framework, with potential applicability to collaborative problem-solving tasks. The architecture is described in reasonable detail and the release of evaluation code is a credit to the authors. However, the current evidence does not establish the claimed performance improvement: the evaluation is a single qualitative case study with no metrics, no ablations, and no statistical analysis. The paper is best viewed as a system description with a promising architecture, but the performance claims as stated are not yet verified.

major comments (4)
  1. [§8.1 and Abstract] The central claim that SynergyMAS 'significantly enhances performance and adaptability' is not supported by the reported evidence. Section 8.1 provides only prose characterizations such as 'deepest analysis' and 'most exhaustive competitive position analysis', with no numeric scores, no error bars, no statistical tests, and no repeated runs. Section 8.2 itself concedes redundancy and variability, yet no attempt is made to quantify these against the baselines. To make the central claim checkable, the authors should report quantitative metrics, such as a pre-defined scoring rubric applied by independent raters, response redundancy rates, variance across runs, and, where possible, statistical comparisons with ChatGPT-4o, CoT, and ToT. Without such measures, the claimed superiority is unfalsifiable.
  2. [§4.1–4.2] The logical reasoning component depends on LLM-generated Cypher queries and ASP programs being semantically faithful translations of natural-language questions, but the manuscript reports no accuracy validation for either translation step. If these translations are frequently incorrect, the Clingo solver's conclusions will be logically sound yet irrelevant to the actual question, undermining the reasoning pillar's contribution. The authors should report translation success rates and, ideally, an end-to-end accuracy evaluation on queries with known ground-truth answers.
  3. [§8] No ablation isolates the contributions of the three core components: logical reasoning, CRAG-based knowledge management, and ToM. Since the title and abstract attribute the performance gain to the synergy of these components, the evaluation should include conditions with each component removed or disabled. Without such ablations, the claim that the components work 'in synergy' cannot be distinguished from the possibility that one dominant component, such as CRAG web search, accounts for most of the improvement. In addition, the comparison is limited to single-model baselines; no comparison against another multi-agent framework is provided.
  4. [1 Introduction] The Introduction states that the framework 'was tested on multiple LLMs, including Claude and Gemini' to evaluate its versatility, but Section 8 presents results only for ChatGPT-4o and its prompting variants. No Claude or Gemini results appear anywhere in the evaluation. This claim should be substantiated with actual results or removed from the Introduction.
minor comments (4)
  1. [§3.2] The phrase 'ToM capabilities capabilities' contains a duplicated word; it should read 'ToM capabilities'.
  2. [§3.3] The phrase 'Future Workoutlines' is missing a space; it should read 'Future Work outlines'.
  3. [§4.2] The citation '(McGinness and Baumgartner)' is incomplete: it lacks a year and page range or venue details. Please complete the reference.
  4. [§8] The statement 'The quality of each response was assessed' does not specify who performed the assessment, what rubric was used, or whether the assessment was blinded. This detail should be provided if the qualitative evaluation is retained.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's claims are architectural and qualitative, with no fitted parameters, equations, or self-citation chain forcing the conclusions.

full rationale

The paper's central claim—that SynergyMAS significantly enhances performance and adaptability—is supported only by qualitative prose in Section 8.1 and Section 8.2. There are no fitted parameters that are subsequently relabeled as predictions, no equations that reduce to their own inputs, and no component that is defined in terms of the outcome it is supposed to explain. The only self-citations (Cinkusz and Chudziak 2024a,b) appear in the Future Work section as pointers to related software-project applications and are not load-bearing for the current evaluation. The evaluation criteria (analysis depth, unique contributions) align with the framework's design goals and are assessed by the authors without external judges or blinded comparisons, which invites confirmation bias, but that is an evidence-quality concern rather than circularity. The absence of quantitative metrics, ablations, and statistical tests makes the performance claim difficult to verify, but the paper does not reduce any derivation to its own inputs by construction. Therefore the circularity score is 0.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The central claims rest on several unquantified domain assumptions: LLM translation to logic code is reliable, ToM prompts help collaboration, and the single case study generalizes. We list two hand-chosen parameters (retrieval threshold and retry limit) and four axioms. No independent invented entities are introduced.

free parameters (2)
  • knowledge base retrieval threshold = not reported
    The logic solver is invoked only when retrieved data falls below this threshold (Section 4.1). The threshold is a hand-chosen design parameter that affects how often the ASP solver is used, and its value is not stated.
  • ASP generation retry limit = 3
    Section 4.2 gives the LLM up to three attempts to produce valid ASP syntax. This choice affects the reliability of the reasoning component and is set by hand.
assumptions (4)
  • domain assumption LLM-generated Cypher queries and ASP code are semantically faithful translations of the natural language questions.
    Sections 4.1 and 4.2 assume the LLM can correctly translate questions into Cypher and ASP with no reported accuracy, and that Clingo's answer corresponds to the intended logical conclusion.
  • domain assumption ToM prompting through 'My Beliefs' sections improves multi-agent collaboration relative to non-ToM prompting.
    Section 6 claims this enhancement without a controlled experiment that isolates ToM; it is treated as established from related work.
  • domain assumption A hierarchical boss-agent structure with control returning after each task prevents redundancy and maintains focus.
    Section 3.3 introduces this as a design objective rather than an empirically tested property.
  • ad hoc to paper The Lean Startup methodology is an appropriate testbed for evaluating the framework's general-purpose capabilities.
    The single case study (Section 7) is selected by the authors and no evidence is provided that performance on this scenario transfers to other domains.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Synergizing Logical Reasoning, Knowledge Management and Collaboration in Multi-Agent LLM System." pith.science (2026). https://pith.science/paper/AZZKCURW

@misc{pith2026250702170,
  author       = {Pith},
  title        = {Pith review of: Synergizing Logical Reasoning, Knowledge Management and Collaboration in Multi-Agent LLM System},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AZZKCURW}},
  note         = {Machine review of arXiv:2507.02170}
}
read the original abstract

This paper explores the integration of advanced Multi-Agent Systems (MAS) techniques to develop a team of agents with enhanced logical reasoning, long-term knowledge retention, and Theory of Mind (ToM) capabilities. By uniting these core components with optimized communication protocols, we create a novel framework called SynergyMAS, which fosters collaborative teamwork and superior problem-solving skills. The system's effectiveness is demonstrated through a product development team case study, where our approach significantly enhances performance and adaptability. These findings highlight SynergyMAS's potential to tackle complex, real-world challenges.

Figures

Figures reproduced from arXiv: 2507.02170 by the authors.

Figure 1
Figure 1. SynergyMAS: Integrating Logical Reasoning, [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. , is one such structure utilized in this article [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Agents architecture: illustrates the Synergy [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Logical Reasoning Functions: Shows the core [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: Corrective-RAG flowchart: Document re￾trieval, grading, and adaptive question-answering pro￾cess. this and infer: completed(ImplementFeatureX) Safety mechanisms validate the ASP syntax to ensure logical consistency and correctness, allow￾ing the LLM up to three attempt…
Figure 6
Figure 6. Figure 6: Belief Prompts for Collaboration: Demon [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: Diagram illustrating the Lean Startup method [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 8
Figure 8. Figure 8: Multi-Agent Task Progression: Illustrates the [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

40 extracted references · 13 canonical work pages

  1. [1]

    Maciej Besta, Robert Gerstenberger, Emanuel Peter, Marc Fischer, Michał Podstawski, Claude Barthels, Gustavo Alonso, and Torsten Hoefler. 2023. https://arxiv.org/abs/1910.09017 Demystifying graph databases: Analysis and taxonomy of data organization, system designs, and graph queries . Preprint, arXiv:1910.09017

    work page Pith review arXiv 2023

  2. [2]

    Roberta Calegari, Giovanni Ciatto, Viviana Mascardi, and Andrea Omicini. 2020. https://link.springer.com/article/10.1007/s10458-020-09478-3#citeas Logic-based technologies for multi-agent systems: A systematic literature review - autonomous agents and multi-agent systems

    doi:10.1007/s10458-020-09478-3 2020

  3. [4]

    Meiqi Chen, Yubo Ma, Kaitao Song, Yixin Cao, Yan Zhang, and Dongsheng Li. 2024 a . https://arxiv.org/abs/2310.09158 Improving large language models in event relation logical prediction . Preprint, arXiv:2310.09158

    work page Pith review arXiv 2024

  4. [5]

    Minyu Chen, Guoqiang Li, Ling-I Wu, Ruibang Liu, Yuxin Su, Xi Chang, and Jianxin Xue. 2024 b . https://arxiv.org/abs/2403.16097 Can language models pretend solvers? logic code simulation with llms . Preprint, arXiv:2403.16097

    work page Pith review arXiv 2024

  5. [6]

    Pei Chen, Boran Han, and Shuai Zhang. 2024 c . https://arxiv.org/abs/2404.17729 Comm: Collaborative multi-agent, multi-reasoning-path prompting for complex problem solving . Preprint, arXiv:2404.17729

    arXiv 2024

  6. [7]

    Chudziak

    Konrad Cinkusz and Jaroslaw A. Chudziak. 2024 a . Communicative agents for software project management and system development. In Proceedings of the 21st International Conference on Modeling Decisions for Artificial Intelligence (MDAI 2024)

    work page 2024

  7. [8]

    Chudziak

    Konrad Cinkusz and Jaroslaw A. Chudziak. 2024 b . https://doi.org/10.1145/3691620.3695336 Towards llm-augmented multiagent systems for agile software engineering . In Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering, ASE '24, page 2476–2477, New York, NY, USA. Association for Computing Machinery

    arXiv 2024

  8. [9]

    Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, and Jonathan Larson. 2024. https://arxiv.org/abs/2404.16130 From local to global: A graph rag approach to query-focused summarization . Preprint, arXiv:2404.16130

    arXiv 2024

Show all 40 references
  1. [10]

    Michael Gelfond and Yulia Kahl. 2014. Knowledge representation, reasoning, and the design of Intelligent Agents: The answer-set programming approach. Cambridge University Press

    2014

  2. [11]

    Chawla, Olaf Wiest, and Xiangliang Zhang

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

    2024 arXiv

  3. [12]

    Shanshan Han, Qifan Zhang, Yuhang Yao, Weizhao Jin, Zhaozhuo Xu, and Chaoyang He. 2024. https://arxiv.org/abs/2402.03578 Llm multi-agent systems: Challenges and open problems . Preprint, arXiv:2402.03578

    2024 arXiv

  4. [13]

    Michal Kosinski. 2024. https://arxiv.org/abs/2302.02083 Evaluating large language models in theory of mind tasks . Preprint, arXiv:2302.02083

    2024 arXiv

  5. [14]

    Hobson Lane, Hannes Hapke, and Cole Howard. 2019. Natural language processing in action understanding, analyzing, and generating text with python. Manning Publications: distributed by Skillsoft Books

    2019

  6. [15]

    Bin Lei. 2024. https://arxiv.org/abs/2404.04735 Macm: Utilizing a multi-agent system for condition mining in solving complex mathematical problems . Preprint, arXiv:2404.04735

    2024 arXiv

  7. [16]

    Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2021. https://arxiv.org/abs/2005.11401 Retrieval-augmented generation for knowledge-int...

    2021 arXiv

  8. [17]

    Huao Li, Yu Chong, Simon Stepputtis, Joseph Campbell, Dana Hughes, Charles Lewis, and Katia Sycara. 2023. https://doi.org/10.18653/v1/2023.emnlp-main.13 Theory of mind for multi-agent collaboration via large language models . In Proceedings of the 2023 Conference on Empirical ...

    2023 doi

  9. [18]

    Yuanyuan Liang, Keren Tan, Tingyu Xie, Wenbiao Tao, Siyuan Wang, Yunshi Lan, and Weining Qian. 2024. https://arxiv.org/abs/2402.16567 Aligning large language models to a domain-specific graph database . Preprint, arXiv:2402.16567

    2024 arXiv

  10. [19]

    https://doi.org/10.29007/2n9m Automated theorem provers help improve large language model reasoning

    Lachlan McGinness and Peter Baumgartner. https://doi.org/10.29007/2n9m Automated theorem provers help improve large language model reasoning . In EPiC Series in Computing. EasyChair

    doi

  11. [20]

    McLaughlin, Ansgar Beckermann, and Sven Walter

    Brian P. McLaughlin, Ansgar Beckermann, and Sven Walter. 2011. The Oxford Handbook of Philosophy of Mind. Oxford University Press

    2011

  12. [21]

    Humza Naveed, Asad Ullah Khan, Shi Qiu, Muhammad Saqib, Saeed Anwar, Muhammad Usman, Naveed Akhtar, Nick Barnes, and Ajmal Mian. 2024. https://arxiv.org/abs/2307.06435 A comprehensive overview of large language models . Preprint, arXiv:2307.06435

    2024 arXiv

  13. [22]

    Liangming Pan, Alon Albalak, Xinyi Wang, and William Wang. 2023. https://doi.org/10.18653/v1/2023.findings-emnlp.248 Logic- LM : Empowering large language models with symbolic solvers for faithful logical reasoning . In Findings of the Association for Computational Linguistics...

    2023 doi

  14. [23]

    Sebastian Raschka. 2024. Build a large language model. Manning Publications

    2024

  15. [24]

    Eric Ries. 2017. The Lean Startup: How Today’s entrepreneurs use continuous innovation to create radically successful businesses. Currency

    2017

  16. [25]

    Russell and Peter Norvig

    Stuart J. Russell and Peter Norvig. 2016. Artificial Intelligence: A modern approach. Pearson

    2016

  17. [26]

    Yoav Shoham and Kevin Leyton-Brown. 2012. Multiagent Systems Algorithmic, game-theoretic, and logical foundations Yoav Shoham; Kevin Leyton-Brown. Cambridge Univ. Press

    2012

  18. [27]

    Jiankai Sun, Chuanyang Zheng, Enze Xie, Zhengying Liu, Ruihang Chu, Jianing Qiu, Jiaqi Xu, Mingyu Ding, Hongyang Li, Mengzhe Geng, Yue Wu, Wenhai Wang, Junsong Chen, Zhangyue Yin, Xiaozhe Ren, Jie Fu, Junxian He, Wu Yuan, Qi Liu, Xihui Liu, Yu Li, Hao Dong, Yu Cheng, Ming Zhan...

    2024 arXiv

  19. [28]

    Oyvind Tafjord, Bhavana Dalvi Mishra, and Peter Clark. 2021. https://arxiv.org/abs/2012.13048 Proofwriter: Generating implications, proofs, and abductive statements over natural language . Preprint, arXiv:2012.13048

    2021 arXiv

  20. [29]

    Qian Wang, Tianyu Wang, Qinbin Li, Jingsheng Liang, and Bingsheng He. 2024 a . https://arxiv.org/abs/2408.09955 Megaagent: A practical framework for autonomous cooperation in large-scale llm agent systems . Preprint, arXiv:2408.09955

    2024 arXiv

  21. [30]

    Yulong Wang, Tianhao Shen, Lifeng Liu, and Jian Xie. 2024 b . https://arxiv.org/abs/2407.10718 Sibyl: Simple yet effective agent framework for complex real-world reasoning . Preprint, arXiv:2407.10718

    2024 arXiv

  22. [31]

    Zhongsheng Wang, Jiamou Liu, Qiming Bao, Hongfei Rong, and Jingfeng Zhang. 2024 c . https://arxiv.org/abs/2407.10162 Chatlogic: Integrating logic programming with large language models for multi-step reasoning . Preprint, arXiv:2407.10162

    2024 arXiv

  23. [32]

    Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. 2023. https://arxiv.org/abs/2201.11903 Chain-of-thought prompting elicits reasoning in large language models . Preprint, arXiv:2201.11903

    2023 arXiv

  24. [33]

    Lilian Weng. 2023. https://lilianweng.github.io/posts/2023-06-23-agent/ Llm-powered autonomous agents . lilianweng.github.io

    2023

  25. [34]

    Wooldridge

    Michael J. Wooldridge. 2009. An introduction to multiagent systems. John Wiley & Sons

    2009

  26. [35]

    Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, Ahmed Hassan Awadallah, Ryen W White, Doug Burger, and Chi Wang. 2023. https://arxiv.org/abs/2308.08155 Autogen: Enabling next-gen llm applications via mu...

    2023 arXiv

  27. [36]

    Weijian Xie, Xuefeng Liang, Yuhui Liu, Kaihua Ni, Hong Cheng, and Zetian Hu. 2024. https://arxiv.org/abs/2408.07611 Weknow-rag: An adaptive approach for retrieval-augmented generation integrating web search and knowledge graphs . Preprint, arXiv:2408.07611

    2024 arXiv

  28. [37]

    Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, and Zhen-Hua Ling. 2024. https://arxiv.org/abs/2401.15884 Corrective retrieval augmented generation . Preprint, arXiv:2401.15884

    2024 arXiv

  29. [38]

    Zhun Yang, Adam Ishay, and Joohyung Lee. 2023. https://arxiv.org/abs/2307.07696 Coupling large language models with logic programming for robust and general reasoning from text . Preprint, arXiv:2307.07696

    2023 arXiv

  30. [39]

    Griffiths, Yuan Cao, and Karthik Narasimhan

    Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan. 2023. https://arxiv.org/abs/2305.10601 Tree of thoughts: Deliberate problem solving with large language models . Preprint, arXiv:2305.10601

    2023 arXiv

  31. [40]

    online" 'onlinestring :=

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list...

  32. [41]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.