Paper Citation Record · LEDGER

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey

As of 17 August 2026, this Paper Citation Record lists 100 of 122 outbound references and 4 inbound Pith citation observations for arXiv:2504.14520.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2504.14520 v1

Coverage vector

measured 100 of 122 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:50:28.171568Z

measured 104 of 104 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T15:50:30.476760Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-10T08:58:13.216074Z

Reference resolution

100 of 122 outbound references displayed

verified exact2
verified fuzzy0
unresolved98
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1e6c8538-f2b3-46ea-9dbb-bbd4b8ec88a8 · outbound

This paper cites Is creativity without intelligence possible? a necessary condition analysis,.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Is creativity without intelligence possible? a necessary condition analysis,

Reference 1

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Observation d882530a-a2bd-4cb8-9c77-a800c9bc2dc6 · outbound

This paper cites Thinking LLMs: General Instruction Following with Thought Generation.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Thinking LLMs: General Instruction Following with Thought Generation

Reference 2

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Observation 096de597-32ce-4b2d-9bc7-38d428171b72 · outbound

This paper cites LLMs for Explainable AI: A Comprehensive Survey.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey LLMs for Explainable AI: A Comprehensive Survey

Reference 3

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Observation 1dd879aa-9cc7-4df7-a203-09ba2a5c835b · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models,.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Chain-of-thought prompting elicits reasoning in large language models,

Reference 4

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Observation 42affbab-670e-46d7-b223-df2d468a7cde · outbound

This paper cites To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning

Reference 5

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Observation 71dc5145-b59d-4802-a989-688a1b3bb8a9 · outbound

This paper cites Retrieval Augmented Generation with Multi-Modal LLM Framework for Wireless Environments.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Retrieval Augmented Generation with Multi-Modal LLM Framework for Wireless Environments

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:27.735660Z

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Observation 154bcae4-8dbc-42c0-a481-b5108176eb91 · outbound

This paper cites A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:27.741423Z

Source-reported events for the cited work

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Observation c9515db6-f872-4c55-b877-87afbf98d45d · outbound

This paper cites Assessing llms for high stakes applications,.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Assessing llms for high stakes applications,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:27.745978Z

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Observation b65b79f8-6cb2-43d9-945e-cac4f7eaac68 · outbound

This paper cites Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

Reference 9

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Observation 44773207-9de3-461f-a6dc-b6efd21e2ec9 · outbound

This paper cites A review of methods for alleviating hallucination issues in large language models,.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey A review of methods for alleviating hallucination issues in large language models,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:27.755117Z

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Observation d7c67a13-6121-40a0-80a4-fe585826d8a8 · outbound

This paper cites A Long Way to Go: Investigating Length Correlations in RLHF.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey A Long Way to Go: Investigating Length Correlations in RLHF

Reference 11

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Observation 1b9787b9-0b1f-4fe6-ab13-7722ba6d720b · outbound

This paper cites Human-level control through deep reinforcement learning,.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Human-level control through deep reinforcement learning,

Reference 12

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Observation 6c73aaa1-87b3-4b79-ab48-28cf63316f5a · outbound

This paper cites Continuous control with deep reinforcement learning.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Continuous control with deep reinforcement learning

Reference 13

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Observation 6e385f08-7479-43b8-a3d1-aed2a29b7ed7 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:27.773074Z

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Observation 427aca7f-f001-4028-8712-34c0f4cc7b30 · outbound

This paper cites Mixcl: Mixed contrastive learning for relation extraction,.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Mixcl: Mixed contrastive learning for relation extraction,

Reference 15

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Observation 051a03b3-4b26-40c7-a85c-9253f5c82c3a · outbound

This paper cites Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding

Reference 16

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Observation abe910a9-7111-4836-a2d8-1d669142b0d8 · outbound

This paper cites Mitigating large language model hallucinations via autonomous knowledge graph- based retrofitting,.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Mitigating large language model hallucinations via autonomous knowledge graph- based retrofitting,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:27.786475Z

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Observation 6cfc1447-a83b-465b-bcfa-f0b303f4ff94 · outbound

This paper cites A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation

Reference 18

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Observation 18d80309-24b0-459c-9331-679058f8a2bd · outbound

This paper cites Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:27.795731Z

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Unavailable: canonical work link unavailable.

Observation f1e05a96-e1a7-42d2-a8b3-725ffe62fe8a · outbound

This paper cites Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 20

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Observation b177ac6c-635b-4070-9139-0957ba51eeec · outbound

This paper cites SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models

Reference 21

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Observation 03780251-b86b-445b-b0fd-433f624b25a3 · outbound

This paper cites Chain-of-Verification Reduces Hallucination in Large Language Models.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Chain-of-Verification Reduces Hallucination in Large Language Models

Reference 22

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Observation eb624d59-d1c8-426d-8410-550fd622c07e · outbound

This paper cites Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation

Reference 23

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Observation fa6d9464-ecb5-4ab5-8836-a1589b16fbc5 · outbound

This paper cites Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation

Reference 24

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Observation 394cbf54-979d-498f-b796-80434bc5d06a · outbound

This paper cites KCTS: Knowledge-Constrained Tree Search Decoding with Token-Level Hallucination Detection.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey KCTS: Knowledge-Constrained Tree Search Decoding with Token-Level Hallucination Detection

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:27.823504Z

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Observation f8a06022-c61e-49da-b9bb-3b649acac5ac · outbound

This paper cites Inference- time intervention: Eliciting truthful answers from a language model,.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Inference- time intervention: Eliciting truthful answers from a language model,

Reference 26

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Observation 66daf7b3-f725-4b40-81f1-135f56b87328 · outbound

This paper cites DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:27.832665Z

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Observation 11b83328-ce46-42b9-bb2b-30733779bead · outbound

This paper cites Self-refine: Iter- ative refinement with self-feedback,.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Self-refine: Iter- ative refinement with self-feedback,

Reference 28

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Observation 5e1110ee-275e-4c93-8f5c-966c12c56193 · outbound

This paper cites Can Large Language Models Really Improve by Self-critiquing Their Own Plans?.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Can Large Language Models Really Improve by Self-critiquing Their Own Plans?

Reference 29

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Observation 49e02cfd-ec9c-4204-917e-a3e98acae603 · outbound

This paper cites Large language models lack essential metacognition for reliable medical reasoning,.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Large language models lack essential metacognition for reliable medical reasoning,

Reference 30

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Observation 42b023a8-cbf4-4719-901a-76c742b31881 · outbound

This paper cites Large Language Models Cannot Self-Correct Reasoning Yet.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Large Language Models Cannot Self-Correct Reasoning Yet

Reference 31

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Observation 1be6a8c3-4756-4598-ad56-93b346f8fc36 · outbound

This paper cites LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey LLM-based Multi-Agent Reinforcement Learning: Current and Future Directions

Reference 32

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Observation e3a1c42c-2c14-46da-a5a2-310886184be1 · outbound

This paper cites A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration

Reference 33

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Observation 698330db-ad30-4eb7-8bbd-fab455087f69 · outbound

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Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Leveraging large language models for optimised coordination in textual multi-agent reinforcement learning,

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Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework

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Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Building Cooperative Embodied Agents Modularly with Large Language Models

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Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Smart-llm: Smart multi-agent robot task planning using large language models,

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Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Roco: Dialectic multi-robot col- laboration with large language models,

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Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Co-NavGPT: Multi-Robot Cooperative Visual Semantic Navigation Using Vision Language Models

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Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Embodied LLM Agents Learn to Cooperate in Organized Teams

Reference 40

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Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Multi-Agent Consensus Seeking via Large Language Models

Reference 41

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Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Learning Natural Language Constraints for Safe Reinforcement Learning of Language Agents

Reference 42

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Observation 352db76e-7349-40df-9c3b-bbb078ccf2b1 · outbound

This paper cites ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement Learning.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement Learning

Reference 43

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Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Theory of Mind for Multi-Agent Collaboration via Large Language Models

Reference 44

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Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Buffer of thoughts: Thought-augmented reasoning with large language models,

Reference 45

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Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Meta Learning for Natural Language Processing: A Survey

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Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Continual Learning for Large Language Models: A Survey

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Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Continual Learning of Large Language Models: A Comprehensive Survey

Reference 48

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Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Towards Incremental Learning in Large Language Models: A Critical Review

Reference 49

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Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Learning from models beyond fine-tuning

Reference 50

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Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Meta Reasoning for Large Language Models

Reference 51

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Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Reasoning with large language models, a survey,

Reference 52

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Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Gizaml: A collaborative meta-learning based framework using llm for automated time-series forecasting

Reference 53

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This paper cites Towards lifelong learning of large language models: A survey,.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Towards lifelong learning of large language models: A survey,

Reference 54

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Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Thinking Machines: A Survey of LLM based Reasoning Strategies

Reference 55

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Observation 7cb7659b-e411-4e9a-a034-897ef2f3b2a9 · outbound

This paper cites MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs

Reference 56

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Observation 46850007-b9cd-4d20-b553-92a5208124ac · outbound

This paper cites Multi-LogiEval: Towards Evaluating Multi-Step Logical Reasoning Ability of Large Language Models.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Multi-LogiEval: Towards Evaluating Multi-Step Logical Reasoning Ability of Large Language Models

Reference 57

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Observation ebcbbd79-9a65-47e0-9a1f-9bff521d5042 · outbound

This paper cites Evaluating the Meta- and Object-Level Reasoning of Large Language Models for Question Answering.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Evaluating the Meta- and Object-Level Reasoning of Large Language Models for Question Answering

Reference 58

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:50:28.936737Z

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Observation 431dabcf-b987-43c7-a8e3-e51b2555374d · outbound

This paper cites Malalgoqa: Pedagogical evaluation of counterfactual reasoning in large language models and implications for ai in education,.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Malalgoqa: Pedagogical evaluation of counterfactual reasoning in large language models and implications for ai in education,

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:27.982603Z

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Observation 377461d5-1bdc-4e12-9799-a738d93b855c · outbound

This paper cites METAL: Towards Multilingual Meta-Evaluation.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey METAL: Towards Multilingual Meta-Evaluation

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:27.987474Z

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Observation bd47ff9c-9ebf-4db1-a278-a2dc24002790 · outbound

This paper cites MetaICL: Learning to Learn In Context.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey MetaICL: Learning to Learn In Context

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:27.992183Z

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Observation 4e4b7159-712f-4537-ba66-a7ee4299d250 · outbound

This paper cites Towards metacognitive clinical reasoning: Benchmarking md-pie against state-of-the-art llms in medical decision-making,.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Towards metacognitive clinical reasoning: Benchmarking md-pie against state-of-the-art llms in medical decision-making,

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:27.997065Z

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Observation dc0c558f-ff7e-4608-b522-ec34abe6f280 · outbound

This paper cites Do Large Language Models Know What They Don't Know?.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Do Large Language Models Know What They Don't Know?

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.001877Z

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Observation d9ae61b9-778e-49eb-9f06-cb89bc74c7c0 · outbound

This paper cites Language grounded multi- agent reinforcement learning with human-interpretable communica- tion,.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Language grounded multi- agent reinforcement learning with human-interpretable communica- tion,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.006674Z

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Observation 67fcd80a-e994-4467-af7e-ddc518990c26 · outbound

This paper cites Hypothetical Minds: Scaffolding Theory of Mind for Multi-Agent Tasks with Large Language Models.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Hypothetical Minds: Scaffolding Theory of Mind for Multi-Agent Tasks with Large Language Models

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.011118Z

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Observation ebbc836d-4e0a-40dc-aacb-1fc3ce636dcd · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Distilling the Knowledge in a Neural Network

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.015604Z

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Observation 92a9ccc0-e665-4136-b76a-460f8febf5ee · outbound

This paper cites Knowledge distillation: A survey,.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Knowledge distillation: A survey,

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.020070Z

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Observation e13eeaa1-c6fa-4272-84fa-c432c205caa2 · outbound

This paper cites Large Language Models have Intrinsic Self-Correction Ability.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Large Language Models have Intrinsic Self-Correction Ability

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.024588Z

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Observation 8e8ead3d-6cf2-4ef4-9157-ea712b0ae964 · outbound

This paper cites Can Rationalization Improve Robustness?.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Can Rationalization Improve Robustness?

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.029078Z

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Observation 41c59bf3-1b97-4413-8d7a-e4df063ee9e4 · outbound

This paper cites Measuring Compositionality in Representation Learning.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Measuring Compositionality in Representation Learning

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.033705Z

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Observation 6affd4e1-55c9-4651-bf20-c75c71cdc5e3 · outbound

This paper cites Multi-agent Reinforcement Learning in Sequential Social Dilemmas.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Multi-agent Reinforcement Learning in Sequential Social Dilemmas

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.038753Z

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Observation 4045999f-60d3-410e-8cd0-9c3db45adbaf · outbound

This paper cites Towards Effective GenAI Multi-Agent Collaboration: Design and Evaluation for Enterprise Applications.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Towards Effective GenAI Multi-Agent Collaboration: Design and Evaluation for Enterprise Applications

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.043495Z

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Observation 27c89c8d-3764-4f1c-aae9-f40bda59ecfa · outbound

This paper cites AI safety via debate.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey AI safety via debate

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.048060Z

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Observation 5941701a-ef45-4456-82a8-9b9cbb91d422 · outbound

This paper cites Adversarial training for high-stakes reliability,.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Adversarial training for high-stakes reliability,

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.052767Z

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Observation 6612d0f1-855f-48ff-8898-5be4bf4fe428 · outbound

This paper cites AD-AutoGPT: An Autonomous GPT for Alzheimer's Disease Infodemiology.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey AD-AutoGPT: An Autonomous GPT for Alzheimer's Disease Infodemiology

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.057392Z

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Observation d9024881-cdfb-4788-91f7-2dbf9b92107c · outbound

This paper cites Deep reinforcement learning from human preferences,.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Deep reinforcement learning from human preferences,

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.062051Z

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Observation 2b65ccd2-3f01-410c-a3de-2de81a16e07d · outbound

This paper cites Training language models to follow instructions with human feedback,.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Training language models to follow instructions with human feedback,

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.066324Z

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Observation 961f0250-0a70-4bf2-b01b-fdfd2a0a40fa · outbound

This paper cites Training Language Models to Self-Correct via Reinforcement Learning.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Training Language Models to Self-Correct via Reinforcement Learning

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.070616Z

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Observation ce8f0fa8-a1ad-4dbf-8c38-a30ac0bcf42d · outbound

This paper cites A Tutorial on Meta-Reinforcement Learning.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey A Tutorial on Meta-Reinforcement Learning

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.075344Z

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Observation 85be3b41-3738-43e8-9fcb-d5c5900d92ad · outbound

This paper cites Eureka: Human-Level Reward Design via Coding Large Language Models.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Eureka: Human-Level Reward Design via Coding Large Language Models

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.079809Z

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Observation 7e57c22b-0edb-45fc-b1af-92cf449c920b · outbound

This paper cites Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.084177Z

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Observation 93397ad0-a6c0-4119-b5ef-f58d4b6bbc0c · outbound

This paper cites Learning to summarize with human feedback,.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Learning to summarize with human feedback,

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.088784Z

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Observation 0a0131c6-e27b-4d0c-ba80-8fb8cc73294d · outbound

This paper cites Confidence Matters: Revisiting Intrinsic Self-Correction Capabilities of Large Language Models.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Confidence Matters: Revisiting Intrinsic Self-Correction Capabilities of Large Language Models

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.092823Z

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Observation c1f33125-130b-4524-aa1c-2a2cadc76e4a · outbound

This paper cites Curiosity-Driven Reinforcement Learning from Human Feedback.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Curiosity-Driven Reinforcement Learning from Human Feedback

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.097413Z

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Observation 4e39e6a5-dffc-4217-b1c4-ae0d3fd4e97a · outbound

This paper cites Online intrinsic rewards for decision making agents from large language model feedback,.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Online intrinsic rewards for decision making agents from large language model feedback,

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.101951Z

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Observation 45c41508-2731-41d1-9af1-c041ccf42002 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.106527Z

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Observation 4290a4a5-6c3b-4d78-b8e2-93a8058b923c · outbound

This paper cites Grandmaster level in starcraft ii using multi-agent reinforcement learning,.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Grandmaster level in starcraft ii using multi-agent reinforcement learning,

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.111357Z

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Observation 961f9518-3979-41e8-95cb-bd8151638423 · outbound

This paper cites Human-level play in the game of diplomacy by combining language models with strategic reasoning,.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Human-level play in the game of diplomacy by combining language models with strategic reasoning,

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.115812Z

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Observation 77bbbd09-8f67-474a-8f63-6f5ed77d072c · outbound

This paper cites Shall We Team Up: Exploring Spontaneous Cooperation of Competing LLM Agents.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Shall We Team Up: Exploring Spontaneous Cooperation of Competing LLM Agents

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.120233Z

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Observation d6b594b8-4cf9-4770-9318-2f3ae99696b8 · outbound

This paper cites Self- playing adversarial language game enhances llm reasoning,.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Self- playing adversarial language game enhances llm reasoning,

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.124703Z

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Observation b50ec277-b147-423a-8e93-544c3680f91e · outbound

This paper cites Logicattack: Adversarial attacks for evaluating logical consistency of natural language inference,.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Logicattack: Adversarial attacks for evaluating logical consistency of natural language inference,

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.129150Z

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Observation 4d0e1425-408a-4eee-8266-419855a42db0 · outbound

This paper cites Model-agnostic meta-learning for fast adaptation of deep networks,.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Model-agnostic meta-learning for fast adaptation of deep networks,

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.134129Z

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Observation 54c4ffe3-a2f7-474c-ba71-1c2918a801bc · outbound

This paper cites Meta-learning for Few-shot Natural Language Processing: A Survey.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Meta-learning for Few-shot Natural Language Processing: A Survey

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.138491Z

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Observation b4a7406c-bca5-4b9d-b880-910dfc948c4c · outbound

This paper cites Meta In-Context Learning Makes Large Language Models Better Zero and Few-Shot Relation Extractors.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Meta In-Context Learning Makes Large Language Models Better Zero and Few-Shot Relation Extractors

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.143364Z

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Observation df4e08ac-3fce-4d24-877c-f38553618c9b · outbound

This paper cites Improving Consistency in Large Language Models through Chain of Guidance.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Improving Consistency in Large Language Models through Chain of Guidance

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.148408Z

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Observation 3633bd04-b82f-4a56-8a7a-09f3aeff3b4f · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Training Verifiers to Solve Math Word Problems

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.153101Z

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Observation f1d73e9d-158b-46df-adae-97258db6d2ae · outbound

This paper cites Red teaming language models for contradictory dialogues,.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Red teaming language models for contradictory dialogues,

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.158013Z

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Observation 104758c2-e148-4106-b2f1-69ceb8b023b1 · outbound

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Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey OpenAI o1 System Card

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.162336Z

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Observation b0de2eb8-aa5b-43e9-b20b-4346b35a212a · outbound

This paper cites Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies,.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies,

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-16T11:50:28.167260Z

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Observation 219f7fc9-5318-4c73-bf61-8388a04b39f8 · outbound

This paper cites Can LLMs Beat Humans in Debating? A Dynamic Multi-agent Framework for Competitive Debate.

Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey Can LLMs Beat Humans in Debating? A Dynamic Multi-agent Framework for Competitive Debate

Reference 100

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Pith citing papers

Observation d795cdaf-31c7-4e31-b172-fad1c89479b0 · inbound

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Verifying Meta-Awareness via Predictive Rewards in Reasoning Models Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-15T15:50:30.476760Z

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Observation 594d600c-b10e-46ab-b09a-d5a8219ea514 · inbound

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Weak-Link Optimization for Multi-Agent Reasoning and Collaboration Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-10T08:58:13.218271Z

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Observation 0b30f91a-1da6-43b0-b8d6-2a8131d40932 · inbound

Human Cognition in Machines: A Unified Perspective of World Models cites this paper.

Human Cognition in Machines: A Unified Perspective of World Models Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey

Reference 18

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Observation c3a4fb96-ec94-4311-8ce7-2106133bb173 · inbound

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HiPO: Hierarchical Preference Optimization for Adaptive Reasoning in LLMs Meta-Thinking in LLMs via Multi-Agent Reinforcement Learning: A Survey

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-10T00:54:48.570592Z

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