Paper Citation Record · LEDGER

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs

As of 9 August 2026, this Paper Citation Record lists 81 of 81 outbound references and 2 inbound Pith citation observations for arXiv:2507.08960.

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

pith.paper-citation-record.v1
2507.08960 v1

Coverage vector

measured 81 of 81 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:13:00.348063Z

measured 83 of 83 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-09T22:47:51.676289Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T22:56:37.756820Z

Reference resolution

81 of 81 outbound references displayed

verified exact2
verified fuzzy9
unresolved70
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a4260ea4-7abf-4edc-b541-3b2a19422f04 · outbound

This paper cites Graph of thoughts: Solving elaborate problems with large language models.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Graph of thoughts: Solving elaborate problems with large language models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.005763Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bde69cd1-60e0-4620-90ec-6d3d5dcda0ab · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs On the Opportunities and Risks of Foundation Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.029371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4a557c60-7ca1-4d22-b4a3-e3bae4c1f77a · outbound

This paper cites Language Models are Few-Shot Learners.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Language Models are Few-Shot Learners

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.051900Z

Source-reported events for the cited work

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Observation a67cc61c-e0fe-4db0-8a0b-f7eff4fda5f7 · outbound

This paper cites ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.078990Z

Source-reported events for the cited work

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Observation 12af781f-5054-43ed-9f3b-ab547c44287a · outbound

This paper cites SocraSynth: Multi-LLM Reasoning with Conditional Statistics.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs SocraSynth: Multi-LLM Reasoning with Conditional Statistics

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.107269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3fcc3d6-af83-4d07-9272-a7a0c28e18cb · outbound

This paper cites ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.136451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f61326d0-7de9-4c33-887e-afeeaeab4906 · outbound

This paper cites Skill-Based Mixture-of-Experts: Adaptive Routing for Heterogeneous Reasoning via Inferred Skills.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Skill-Based Mixture-of-Experts: Adaptive Routing for Heterogeneous Reasoning via Inferred Skills

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.172268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60908b6d-180d-4b21-a7e5-e772302339b5 · outbound

This paper cites Universal Self-Consistency for Large Language Model Generation.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Universal Self-Consistency for Large Language Model Generation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.200045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ef96167-bebf-4553-b765-e9a53fc887f1 · outbound

This paper cites Cost-Effective Online Multi-LLM Selection with Versatile Reward Models.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Cost-Effective Online Multi-LLM Selection with Versatile Reward Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.230490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4048bd57-f74b-4197-8b87-0f335d1a653b · outbound

This paper cites Improving factuality and reasoning in language models through multiagent debate.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Improving factuality and reasoning in language models through multiagent debate

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.254960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1808ef03-445f-432a-b6b7-ec0674130b18 · outbound

This paper cites Debate Only When Necessary: Adaptive Multiagent Collaboration for Efficient LLM Reasoning.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Debate Only When Necessary: Adaptive Multiagent Collaboration for Efficient LLM Reasoning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.282695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2add6b6c-d084-4bb7-91b5-4ef3434c6652 · outbound

This paper cites Multi-llm debate: Framework, principals, and interventions.Advancesin Neural Information Processing Systems, 37:28938–28964, 2024.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Multi-llm debate: Framework, principals, and interventions.Advancesin Neural Information Processing Systems, 37:28938–28964, 2024

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:13:03.384428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5cb5f25c-4895-4e1c-b7b0-8109771efe23 · outbound

This paper cites ACC-Collab: An Actor-Critic Approach to Multi-Agent LLM Collaboration.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs ACC-Collab: An Actor-Critic Approach to Multi-Agent LLM Collaboration

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.333530Z

Source-reported events for the cited work

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Observation ceb1668a-586b-4453-b857-772546007899 · outbound

This paper cites Acc-collab: An actor-critic approach to multi-agent llm collaboration, 2024.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Acc-collab: An actor-critic approach to multi-agent llm collaboration, 2024

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:13:03.218235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4c63ecf6-b762-4b69-a2a7-99d65e5c23fd · outbound

This paper cites Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Don't Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.377961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62cfc9c8-6a77-45ce-9136-e555f92dca6e · outbound

This paper cites Modular Pluralism: Pluralistic Alignment via Multi-LLM Collaboration.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Modular Pluralism: Pluralistic Alignment via Multi-LLM Collaboration

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.398205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ab1ff3e-dcc9-448b-a654-72d8a3ac5e3a · outbound

This paper cites When One LLM Drools, Multi-LLM Collaboration Rules.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs When One LLM Drools, Multi-LLM Collaboration Rules

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.412984Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98037f10-d385-4b10-96be-593571730124 · outbound

This paper cites Heterogeneous swarms: Jointly optimizing model roles and weights for multi-llm systems.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Heterogeneous swarms: Jointly optimizing model roles and weights for multi-llm systems

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.440669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c44c63f-9bf1-481e-9251-6cf1b33cc292 · outbound

This paper cites The Llama 3 Herd of Models.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs The Llama 3 Herd of Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.461508Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de01ddc7-bbd2-4760-9bb2-e1a489f7016b · outbound

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

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.485112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8bc2de8f-deff-42d0-a3d8-50001e383221 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Measuring Massive Multitask Language Understanding

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.517427Z

Source-reported events for the cited work

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Observation d07b6f32-3df4-4d8e-80bf-56647a1cfb0d · outbound

This paper cites Measuring Mathematical Problem Solving With the MATH Dataset.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Measuring Mathematical Problem Solving With the MATH Dataset

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.548499Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 644f457c-aea3-4539-905c-75dd4b6023b2 · outbound

This paper cites MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.583192Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 597c834d-ee7b-4050-a9dc-87ef7e557153 · outbound

This paper cites A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.605891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99bc94e4-dcf3-486f-a5bd-b69e6a12db7c · outbound

This paper cites Ensemble learning for heterogeneous large language models with deep parallel collaboration.Advancesin Neural Information Processing Systems, 37:119838–119860, 2024.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Ensemble learning for heterogeneous large language models with deep parallel collaboration.Advancesin Neural Information Processing Systems, 37:119838–119860, 2024

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:13:03.050076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f2669eb8-0f89-4653-984a-3a5a2f1c3b18 · outbound

This paper cites OpenAI o1 System Card.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs OpenAI o1 System Card

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.649279Z

Source-reported events for the cited work

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Observation 908b419e-cb3d-45e8-b949-634ed9bfb66c · outbound

This paper cites LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.673023Z

Source-reported events for the cited work

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Observation f49e5ddd-dd47-4ff4-bbfa-4f50ecc3c14d · outbound

This paper cites Two Heads are Better Than One: Test-time Scaling of Multi-agent Collaborative Reasoning.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Two Heads are Better Than One: Test-time Scaling of Multi-agent Collaborative Reasoning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.703046Z

Source-reported events for the cited work

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Observation 12f9681d-b8c2-4344-b6c2-8546819eb797 · outbound

This paper cites Debating with More Persuasive LLMs Leads to More Truthful Answers.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.734477Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af1bd348-84c9-48e1-bb11-b38be3e4ea4f · outbound

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

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Training Language Models to Self-Correct via Reinforcement Learning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.743208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 065edb2c-6337-4c27-b62e-6ffbc0d040f6 · outbound

This paper cites SMoA: Improving Multi-agent Large Language Models with Sparse Mixture-of-Agents.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs SMoA: Improving Multi-agent Large Language Models with Sparse Mixture-of-Agents

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.768620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b06ebd05-8852-4536-be17-d922927703b6 · outbound

This paper cites Two heads are better than one: Dual-model verbal reflection at inference-time.arXiv preprint arXiv:2502.19230, 2025.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Two heads are better than one: Dual-model verbal reflection at inference-time.arXiv preprint arXiv:2502.19230, 2025

Reference 32

Resolution: verified exact; raw_fallback, observed 2026-08-06T18:13:01.369146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7db4046e-e50e-45f8-82f3-bc13de926a5a · outbound

This paper cites PRD: Peer Rank and Discussion Improve Large Language Model based Evaluations.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs PRD: Peer Rank and Discussion Improve Large Language Model based Evaluations

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.824124Z

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Observation 700d64f3-8551-4e27-b970-ba6321acecd4 · outbound

This paper cites From Drafts to Answers: Unlocking LLM Potential via Aggregation Fine-Tuning.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs From Drafts to Answers: Unlocking LLM Potential via Aggregation Fine-Tuning

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.843340Z

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Observation 139d4ef8-f847-45a9-8eb3-94ccb2f62650 · outbound

This paper cites Improving Multi-Agent Debate with Sparse Communication Topology.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Improving Multi-Agent Debate with Sparse Communication Topology

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.866755Z

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Observation 51167679-277f-4271-a834-e45d56d82b17 · outbound

This paper cites Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.901843Z

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Unavailable: canonical work link unavailable.

Observation a90916ee-2ddb-465e-bff1-f8c54a8c7fda · outbound

This paper cites MARFT: Multi-Agent Reinforcement Fine-Tuning.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs MARFT: Multi-Agent Reinforcement Fine-Tuning

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.937766Z

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Unavailable: canonical work link unavailable.

Observation ed24df84-fe46-4fae-8838-6e7361642413 · outbound

This paper cites Groupdebate: Enhancing the efficiency of multi-agent debate using group discussion.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Groupdebate: Enhancing the efficiency of multi-agent debate using group discussion

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:58.973810Z

Source-reported events for the cited work

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Observation 341a368d-dece-4147-a542-a14dfe331d4a · outbound

This paper cites Towards Hierarchical Multi-Agent Workflows for Zero-Shot Prompt Optimization.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Towards Hierarchical Multi-Agent Workflows for Zero-Shot Prompt Optimization

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:59.009997Z

Source-reported events for the cited work

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Observation 5dc73893-7337-4db7-ae30-b87f74102874 · outbound

This paper cites Understanding R1-Zero-Like Training: A Critical Perspective.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Understanding R1-Zero-Like Training: A Critical Perspective

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:59.047193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b776a90-25db-4ee1-a943-321d814100c8 · outbound

This paper cites an unresolved cited work.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Unresolved cited work

Reference 41

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:13:02.901335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 88dd62b2-115b-4d69-99f7-df26b77abd66 · outbound

This paper cites Self-refine: Iterative refinement with self-feedback.Advances in Neural Information Processing Systems, 36:46534–46594, 2023.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Self-refine: Iterative refinement with self-feedback.Advances in Neural Information Processing Systems, 36:46534–46594, 2023

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:59.115363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c3f980c-8035-4390-b9e6-2d0ab0c2195e · outbound

This paper cites SelectLLM: Query-Aware Efficient Selection Algorithm for Large Language Models.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs SelectLLM: Query-Aware Efficient Selection Algorithm for Large Language Models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:59.145103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97d93bbb-9545-4b74-b30d-9c455ceb48e4 · outbound

This paper cites Beyond accuracy: Evaluating the reasoning behavior of large language models.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Beyond accuracy: Evaluating the reasoning behavior of large language models

Reference 44

Resolution: verified exact; raw_fallback, observed 2026-08-06T18:13:01.062647Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e7966f11-a285-4ac9-b61d-d8d1ac569a1b · outbound

This paper cites Motwani, Chandler Smith, Rocktim J.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Motwani, Chandler Smith, Rocktim J

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:59.216898Z

Source-reported events for the cited work

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Observation 933c70b5-5273-4ec6-b898-39f5003ff99f · outbound

This paper cites MAPoRL: Multi-Agent Post-Co-Training for Collaborative Large Language Models with Reinforcement Learning.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs MAPoRL: Multi-Agent Post-Co-Training for Collaborative Large Language Models with Reinforcement Learning

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:59.245532Z

Source-reported events for the cited work

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Observation fcab31e2-8508-4fe2-9861-2d58435f8910 · outbound

This paper cites O1 Replication Journey: A Strategic Progress Report -- Part 1.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs O1 Replication Journey: A Strategic Progress Report -- Part 1

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:59.282290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f94231f-a55b-4841-ab74-3b74cc6f3f99 · outbound

This paper cites Towards Collaborative Intelligence: Propagating Intentions and Reasoning for Multi-Agent Coordination with Large Language Models.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Towards Collaborative Intelligence: Propagating Intentions and Reasoning for Multi-Agent Coordination with Large Language Models

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:59.318146Z

Source-reported events for the cited work

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Observation 290f5497-d606-43da-af7d-05ffa0e50a00 · outbound

This paper cites Qwen2.5 Technical Report.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Qwen2.5 Technical Report

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:59.353123Z

Source-reported events for the cited work

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Observation 35070d5d-222d-4fbd-9321-603e556d56d2 · outbound

This paper cites Proximal Policy Optimization Algorithms.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Proximal Policy Optimization Algorithms

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:59.389070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f8fdf02-dab1-4ce9-8149-e33179bf2602 · outbound

This paper cites MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulation.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulation

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:59.444849Z

Source-reported events for the cited work

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Observation afbc721e-c949-4498-8155-5eb6cdfc1040 · outbound

This paper cites Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Should we be going MAD? A Look at Multi-Agent Debate Strategies for LLMs

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:59.473520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b08e34d0-5cb3-47a4-a98d-741b4b28bd2a · outbound

This paper cites Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:59.492882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42978e99-da8c-48d2-b500-364fcb55df68 · outbound

This paper cites an unresolved cited work.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:13:02.819843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ec833267-3708-47c0-93a1-ed34501fb33a · outbound

This paper cites Kimi k1.5: Scaling Reinforcement Learning with LLMs.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Kimi k1.5: Scaling Reinforcement Learning with LLMs

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:59.592171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bee4dd27-919b-47ab-a771-c8f20354d44b · outbound

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

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement Learning

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:59.617501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69d857ca-a367-4744-b812-1b5a3b746474 · outbound

This paper cites Mixture-of-Agents Enhances Large Language Model Capabilities.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Mixture-of-Agents Enhances Large Language Model Capabilities

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:59.646978Z

Source-reported events for the cited work

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Observation 456c1972-61bd-4e5d-8cb0-60ac7f7abce5 · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:59.678125Z

Source-reported events for the cited work

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Observation c6f7f492-6fe2-4edf-b028-048728c7dd10 · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:59.703873Z

Source-reported events for the cited work

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Observation 7ba63848-f0ec-402a-82db-1d4aac63c743 · outbound

This paper cites AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:59.735760Z

Source-reported events for the cited work

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Observation a60e3e36-e01e-43d2-b319-9f9fcb5d416a · outbound

This paper cites Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:59.763216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e1bdd83-af97-4041-b0e1-521d2b1b7820 · outbound

This paper cites Qwen3 Technical Report.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Qwen3 Technical Report

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:59.793527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 608823d2-2b24-4911-9c2b-a6368629f91a · outbound

This paper cites Multi-LLM Collaborative Search for Complex Problem Solving.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Multi-LLM Collaborative Search for Complex Problem Solving

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:59.822314Z

Source-reported events for the cited work

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Observation eca49996-f6e0-415e-9532-a107d19292dd · outbound

This paper cites AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:59.855011Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2873cd1d-8021-4c09-a3b6-8f98b5d851d0 · outbound

This paper cites R1-ShareVL: Incentivizing Reasoning Capability of Multimodal Large Language Models via Share-GRPO.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs R1-ShareVL: Incentivizing Reasoning Capability of Multimodal Large Language Models via Share-GRPO

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:59.892203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1dd632c-f6eb-455b-9ab8-cdd6c3d1d633 · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models.Advances in neural information processing systems, 36:11809–11822, 2023.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Tree of thoughts: Deliberate problem solving with large language models.Advances in neural information processing systems, 36:11809–11822, 2023

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:59.917749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a0deef6-b5a5-46c0-893e-2f70b92aef8b · outbound

This paper cites X-MAS: Towards Building Multi-Agent Systems with Heterogeneous LLMs.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs X-MAS: Towards Building Multi-Agent Systems with Heterogeneous LLMs

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:59.939425Z

Source-reported events for the cited work

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Observation 98fead6a-7a30-415d-8930-bc0ea65e6463 · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:59.958609Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3afbd129-6a71-4eba-ac6e-3003fa257a68 · outbound

This paper cites Chain of agents: Large language models collaborating on long-context tasks.Advances in Neural Information Processing Systems, 37: 132208–132237, 2024.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Chain of agents: Large language models collaborating on long-context tasks.Advances in Neural Information Processing Systems, 37: 132208–132237, 2024

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:13:02.727265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 633bb555-fc2c-4003-9188-8f313ed67ce6 · outbound

This paper cites SWEET-RL: Training Multi-Turn LLM Agents on Collaborative Reasoning Tasks.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs SWEET-RL: Training Multi-Turn LLM Agents on Collaborative Reasoning Tasks

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-06T18:13:00.021058Z

Source-reported events for the cited work

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Observation 062c7dff-3ce0-47f1-a21d-290ef93ee002 · outbound

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

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-06T18:13:00.055272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a111599-c2f5-4e98-98f3-4d3ed6bff955 · outbound

This paper cites an unresolved cited work.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Unresolved cited work

Reference 74

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:13:02.606895Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 1cbd8334-b5ec-46dd-9f99-393fcc307ac6 · outbound

This paper cites Address each question raised where relevant.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Address each question raised where relevant

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:13:02.462769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 671f858a-d84e-4cca-bd6e-490bd5d1cb56 · outbound

This paper cites Regardless of the approach, always conclude with: 25 Therefore, the final answer is: $\boxed{[answer]}$.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Regardless of the approach, always conclude with: 25 Therefore, the final answer is: $\boxed{[answer]}$

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:13:02.402514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation add2d73d-c7af-4113-8882-af81644c6a15 · outbound

This paper cites - End the answer with: Therefore, the final answer is: $\boxed{[answer]}$.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs - End the answer with: Therefore, the final answer is: $\boxed{[answer]}$

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:13:02.222163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 5d7ebf72-172c-4525-afe0-5910b2999433 · outbound

This paper cites an unresolved cited work.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Unresolved cited work

Reference 79

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:13:02.140011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 53a71cad-ead2-4e6d-871b-a79d06efe8c2 · outbound

This paper cites These should be carefully reviewed for mistakes.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs These should be carefully reviewed for mistakes

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:13:02.058682Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 26db6557-6743-4503-9159-44a94183d0c3 · outbound

This paper cites Wait, that doesn’t seem right.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Wait, that doesn’t seem right

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:13:01.989643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f1997a2b-2b83-42f9-9fda-666bbccea7ed · outbound

This paper cites an unresolved cited work.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Unresolved cited work

Reference 82

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:13:02.326585Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3cb29c6f-d52a-47f0-903e-2ddab44a79c9 · outbound

This paper cites an unresolved cited work.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Unresolved cited work

Reference 83

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:13:01.896301Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 20202767-143f-4d82-9975-74f515d63946 · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs Gemma 2: Improving Open Language Models at a Practical Size

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T18:12:59.556457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 6fddc6f9-9dac-44d6-9fe6-1bb5a442007d · inbound

Plan First, Judge Later, Run Better: A DMAIC-Inspired Agentic System for Industrial Anomaly Detection cites this paper.

Plan First, Judge Later, Run Better: A DMAIC-Inspired Agentic System for Industrial Anomaly Detection How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T07:56:47.554324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 4b4c0f79-3c6a-4168-bb90-8a1d61ec4fc3 · inbound

Mathematical methods of reinforcement learning cites this paper.

Mathematical methods of reinforcement learning How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs

Reference 116

Resolution: verified exact; local_arxiv, observed 2026-07-09T22:56:37.758040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.