Debating with More Persuasive LLMs Leads to More Truthful Answers | Paper Citation Record

Observation 97909e86-617b-49ca-b31a-d21dc39c8255 · inbound

Seeing Like an AI: How LLMs Apply (and Misapply) Wikipedia Neutrality Norms cites this paper.

Seeing Like an AI: How LLMs Apply (and Misapply) Wikipedia Neutrality Norms Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-23T23:03:34.372026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 415b8413-65fb-4f51-af35-4fe682407cf0 · inbound

BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games cites this paper.

BALROG: Benchmarking Agentic LLM and VLM Reasoning On Games Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-12T16:21:08.217662Z

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Observation 28284a8d-410a-4e89-b44b-1d1c75aff2f3 · inbound

Engineering AI Judge Systems cites this paper.

Engineering AI Judge Systems Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-12T11:59:18.305344Z

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Observation c842e0d6-dc0b-47fd-909a-d70fc033885e · inbound

LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods cites this paper.

LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 111

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:08:36.962356Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8e6ddbc5-15e5-47e2-8f9d-9e220b70407d · inbound

Defending LVLMs Against Vision Attacks through Partial-Perception Supervision cites this paper.

Defending LVLMs Against Vision Attacks through Partial-Perception Supervision Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T13:52:06.467601Z

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Observation e37400e3-2672-4aef-a372-0f08cfbd9f1c · inbound

Algebraic Evaluation Theorems cites this paper.

Algebraic Evaluation Theorems Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T11:58:27.845342Z

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Observation 9a2b8f84-14af-465c-b360-03b541ea0225 · inbound

The Road to Artificial SuperIntelligence: A Comprehensive Survey of Superalignment cites this paper.

The Road to Artificial SuperIntelligence: A Comprehensive Survey of Superalignment Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T10:36:17.372600Z

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Observation a4e71ad5-91af-49df-b316-1699893a3401 · inbound

Debate Helps Weak-to-Strong Generalization cites this paper.

Debate Helps Weak-to-Strong Generalization Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T17:50:56.406071Z

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Observation bc1c4e70-3e75-4196-8248-3509387f7049 · inbound

Language Games as the Pathway to Artificial Superhuman Intelligence cites this paper.

Language Games as the Pathway to Artificial Superhuman Intelligence Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-09T22:01:12.356340Z

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Observation 5656cac7-558b-48f7-a1e1-d360842bbb59 · inbound

AI Realtor: Towards Grounded Persuasive Language Generation for Automated Copywriting cites this paper.

AI Realtor: Towards Grounded Persuasive Language Generation for Automated Copywriting Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-05-23T02:57:26.373198Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 91eb8325-a0ab-475c-9e23-5b273823587a · inbound

Towards an AI co-scientist cites this paper.

Towards an AI co-scientist Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 275

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T13:02:45.512685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e6b59651-460d-4233-b41f-f1075e6cf072 · inbound

Large Language Model Agent: A Survey on Methodology, Applications and Challenges cites this paper.

Large Language Model Agent: A Survey on Methodology, Applications and Challenges Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 77

Resolution: metadata mismatch; arxiv_id, observed 2026-05-22T21:52:10.426826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1abd2edd-fc0a-4c8b-a32b-b97ce4aef546 · inbound

Fact-Checking with Contextual Narratives: Leveraging Retrieval-Augmented LLMs for Social Media Analysis cites this paper.

Fact-Checking with Contextual Narratives: Leveraging Retrieval-Augmented LLMs for Social Media Analysis Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 32

Resolution: metadata mismatch; arxiv_id, observed 2026-05-22T21:07:07.891689Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 157fea4d-cf51-46c7-84d4-20bdaef78e46 · inbound

Generative AI Act II: Test Time Scaling Drives Cognition Engineering cites this paper.

Generative AI Act II: Test Time Scaling Drives Cognition Engineering Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 154

Resolution: unresolved; no resolver link, observed 2026-08-16T12:02:43.217530Z

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Observation 8ce2fbba-1898-4282-9b13-4a7dff31c2ac · inbound

Leveraging LLMs as Meta-Judges: A Multi-Agent Framework for Evaluating LLM Judgments cites this paper.

Leveraging LLMs as Meta-Judges: A Multi-Agent Framework for Evaluating LLM Judgments Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-16T10:53:24.844122Z

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Observation 5f10ece9-e073-49d6-98b7-279be554acb2 · inbound

An alignment safety case sketch based on debate cites this paper.

An alignment safety case sketch based on debate Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-15T23:43:46.488293Z

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Observation ac126850-bc50-4040-9bb1-88950aba2dc8 · inbound

Teach2Eval: An Indirect Evaluation Method for LLM by Judging How It Teaches cites this paper.

Teach2Eval: An Indirect Evaluation Method for LLM by Judging How It Teaches Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T20:42:57.968290Z

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Observation 06e93608-3a79-4d69-bf07-c0063b1a6d72 · inbound

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation cites this paper.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T15:44:32.321541Z

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Observation e20efab2-1a1e-4050-935d-c6177babd4b7 · inbound

Collaboration among Multiple Large Language Models for Medical Question Answering cites this paper.

Collaboration among Multiple Large Language Models for Medical Question Answering Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T15:02:08.628751Z

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Observation e2b1b9e5-841f-4eda-8ad3-b211baf2c55e · inbound

OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of Large Language Models cites this paper.

OpenReview Should be Protected and Leveraged as a Community Asset for Research in the Era of Large Language Models Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:38.827612Z

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Observation 5fb2a92a-4a21-4d52-9f93-20718331940e · inbound

Foundation Molecular Grammar: Multi-Modal Foundation Models Induce Interpretable Molecular Graph Languages cites this paper.

Foundation Molecular Grammar: Multi-Modal Foundation Models Induce Interpretable Molecular Graph Languages Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 4848

Resolution: unresolved; no resolver link, observed 2026-08-07T13:03:39.792798Z

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Observation 3f48fab0-9f66-4ec2-b711-8853dc636fbd · inbound

Tournament of Prompts: Evolving LLM Instructions Through Structured Debates and Elo Ratings cites this paper.

Tournament of Prompts: Evolving LLM Instructions Through Structured Debates and Elo Ratings Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T12:15:38.272484Z

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Observation 53b7435d-1187-4845-a777-4cfafb4eb6d1 · inbound

Information Bargaining: Bilateral Commitment in Bayesian Persuasion cites this paper.

Information Bargaining: Bilateral Commitment in Bayesian Persuasion Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T10:18:28.368238Z

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Observation 5125334a-2384-4af3-bc49-d160f8e30bed · inbound

When to Trust Context: Self-Reflective Debates for Context Reliability cites this paper.

When to Trust Context: Self-Reflective Debates for Context Reliability Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T06:07:40.081126Z

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Observation 31613ebc-16b1-42f1-840b-199a7909d5b5 · inbound

Hierarchical Debate-Based Large Language Model (LLM) for Complex Task Planning of 6G Network Management cites this paper.

Hierarchical Debate-Based Large Language Model (LLM) for Complex Task Planning of 6G Network Management Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T05:59:19.759897Z

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

How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs cites this paper.

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

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Observation 184fc43d-3c5d-45d8-b923-cdb7e335967c · inbound

DS@GT at Touch\'e: Large Language Models for Retrieval-Augmented Debate cites this paper.

DS@GT at Touch\'e: Large Language Models for Retrieval-Augmented Debate Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:13.086537Z

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Observation fcb61c41-52ee-4e35-968a-78e3d7c41a14 · inbound

Dynamic Collaboration of Multi-Language Models based on Minimal Complete Semantic Units cites this paper.

Dynamic Collaboration of Multi-Language Models based on Minimal Complete Semantic Units Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T16:18:46.156328Z

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Observation 8c51affd-231e-4bb3-855b-3f53922d37f7 · inbound

Debate2Create: Robot Co-design via Multi-Agent LLM Debate cites this paper.

Debate2Create: Robot Co-design via Multi-Agent LLM Debate Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T07:26:34.581245Z

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Observation 8a60fc48-dd3e-4e3c-80bd-7c0d3763df62 · inbound

Human-AI Complementarity: A Goal for Amplified Oversight cites this paper.

Human-AI Complementarity: A Goal for Amplified Oversight Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-04T07:21:22.491486Z

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Observation 8c6cfe20-8fb3-4412-a860-031f77075284 · inbound

Representing expertise accelerates learning from pedagogical interaction data cites this paper.

Representing expertise accelerates learning from pedagogical interaction data Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T09:05:59.346245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2512a937-8d49-40b6-ba12-7544d42a081a · inbound

Spontaneous Persuasion: An Audit of Model Persuasiveness in Everyday Conversations cites this paper.

Spontaneous Persuasion: An Audit of Model Persuasiveness in Everyday Conversations Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T15:16:07.922055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 25cb3376-3eab-433b-ab3f-b252dbe8d50c · inbound

Interactive Critique-Revision Training for Reliable Structured LLM Generation cites this paper.

Interactive Critique-Revision Training for Reliable Structured LLM Generation Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:36:24.526604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3b802bcb-4ddf-4ab4-8c53-fa6cd628b030 · inbound

EquiMem: Calibrating Shared Memory in Multi-Agent Debate via Game-Theoretic Equilibrium cites this paper.

EquiMem: Calibrating Shared Memory in Multi-Agent Debate via Game-Theoretic Equilibrium Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 28

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T05:56:26.014438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bf1196db-f02d-482f-ad0b-eb1b9950a225 · inbound

LLM-Based Persuasion Enables Guardrail Override in Frontier LLMs cites this paper.

LLM-Based Persuasion Enables Guardrail Override in Frontier LLMs Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:22:55.759999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a12758ac-885c-4431-b5f8-7cd7c387f85c · inbound

Towards Robust Argumentative Essay Understanding via TIDE: An Interactive Framework with Trial and Debate cites this paper.

Towards Robust Argumentative Essay Understanding via TIDE: An Interactive Framework with Trial and Debate Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 78

Resolution: verified exact; arxiv_id, observed 2026-05-20T13:48:19.801086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a19ca39c-4588-478b-b056-f639635aae6d · inbound

Emergent Collaborative Deliberation in Multi-Model AI Systems: A BFT-Derived Protocol for Epistemic Synthesis cites this paper.

Emergent Collaborative Deliberation in Multi-Model AI Systems: A BFT-Derived Protocol for Epistemic Synthesis Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-13T17:56:39.720418Z

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Observation 1df3a52f-03c0-481d-9aeb-7d1d57839ff5 · inbound

Weak Critics Make Strong Learners: On-Policy Critique Distillation for Scalable Oversight cites this paper.

Weak Critics Make Strong Learners: On-Policy Critique Distillation for Scalable Oversight Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-01T19:56:10.964106Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b1c5589b-51b2-4b91-826a-8d550f71da58 · inbound

DeliChess: A Multi-party Dialogue Dataset for Deliberation in Chess Puzzle Solving cites this paper.

DeliChess: A Multi-party Dialogue Dataset for Deliberation in Chess Puzzle Solving Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 28

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T08:26:48.170398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 34f9960e-f256-4d6c-9853-3088184e4ca5 · inbound

DeliChess: A Multi-party Dialogue Dataset for Deliberation in Chess Puzzle Solving cites this paper.

DeliChess: A Multi-party Dialogue Dataset for Deliberation in Chess Puzzle Solving Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-04T04:55:03.848231Z

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Observation ace6d158-8316-4b11-a378-bdb743bd65eb · inbound

A Model of Multi-turn Human Persuadability Using Probabilistic Belief Tracing cites this paper.

A Model of Multi-turn Human Persuadability Using Probabilistic Belief Tracing Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 67

Resolution: verified exact; arxiv_id, observed 2026-07-02T08:16:47.592494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 40ee94f4-9748-4907-b76d-a59d323676a0 · inbound

When Does Delegation Beat Majority? A Delegation-Based Aggregator for Multi-Sample LLM Inference cites this paper.

When Does Delegation Beat Majority? A Delegation-Based Aggregator for Multi-Sample LLM Inference Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 39

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T21:07:23.632874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3d943da3-dc20-4ace-88b1-3317f209769c · inbound

Using Cognitive Models to Improve Language Model Simulation of Human Persuasion Games cites this paper.

Using Cognitive Models to Improve Language Model Simulation of Human Persuasion Games Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-07-03T20:58:57.649393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0eacd0b7-d6a3-473e-94f2-1f2013cecaeb · inbound

Heterogeneous LLM Debate Under Adversarial Peers: Honest Gains, Replacement Costs, and Resilience cites this paper.

Heterogeneous LLM Debate Under Adversarial Peers: Honest Gains, Replacement Costs, and Resilience Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T03:59:34.283597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 78b21b99-94ed-4883-bf5f-a6e458bdf65f · inbound

The Warrant Gap: Claim-Conditioned Re-scoring for Fact-Checking cites this paper.

The Warrant Gap: Claim-Conditioned Re-scoring for Fact-Checking Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 75

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T17:29:59.929156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2751c60e-e9a9-4fad-83d4-c488c32e4924 · inbound

When the Judge Changes, So Does the Measurement: Auditing LLM-as-Judge Reliability cites this paper.

When the Judge Changes, So Does the Measurement: Auditing LLM-as-Judge Reliability Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-07-10T05:56:50.520266Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 66d103b0-1b6a-4538-bade-b29960acb9c3 · inbound

Faster AI, Uneven Frontier: Rapid Crossings, a Jagged Frontier, and the Repositioning of Human Judgment cites this paper.

Faster AI, Uneven Frontier: Rapid Crossings, a Jagged Frontier, and the Repositioning of Human Judgment Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-15T07:32:36.632338Z

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Observation c60f123d-e571-4d5a-ac53-8206d7af8108 · inbound

Does Multi-Agent Debate Improve AI Feedback on Research Papers? cites this paper.

Does Multi-Agent Debate Improve AI Feedback on Research Papers? Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-02T01:21:37.321251Z

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Observation 373cc95f-3183-40de-bbc1-2f9e1e0da72a · inbound

It Matters How You Say It: Exploring Rhetorical Patterns for AI-Assisted Information Evaluation cites this paper.

It Matters How You Say It: Exploring Rhetorical Patterns for AI-Assisted Information Evaluation Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T17:29:58.536136Z

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Observation a82384ce-550b-46b9-a827-e94b0dbfe05a · inbound

More Is Not More: What Matters for Diversity in LLM Opinions? cites this paper.

More Is Not More: What Matters for Diversity in LLM Opinions? Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-02T14:33:33.888256Z

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Observation c385dd0a-2686-4048-9058-2e9813ebfa64 · inbound

$\Sigma$-Mem: An Online Reliability Memory for LLM-based Multi-Agent Systems cites this paper.

$\Sigma$-Mem: An Online Reliability Memory for LLM-based Multi-Agent Systems Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-31T22:14:36.595358Z

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Observation b2270bab-ea4b-43be-9f46-b378b411103c · inbound

DS@GT ARC at Touch\'e: Large Language Models for Retrieval-Augmented Debate cites this paper.

DS@GT ARC at Touch\'e: Large Language Models for Retrieval-Augmented Debate Debating with More Persuasive LLMs Leads to More Truthful Answers

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T00:26:13.652993Z

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Observation 57b8941d-f579-45f8-b942-1d24589114e3 · inbound

ForestBench: A Unified Graph Framework for Evaluating Multi-Agent Collaboration cites this paper.

ForestBench: A Unified Graph Framework for Evaluating Multi-Agent Collaboration Debating with More Persuasive LLMs Leads to More Truthful Answers

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