Paper Citation Record · LEDGER

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need

As of 18 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 6 inbound Pith citation observations for arXiv:2506.15451.

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

pith.paper-citation-record.v1
2506.15451 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:59:48.236412Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T18:11:51.416335Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T00:49:18.501603Z

Reference resolution

26 of 26 outbound references displayed

verified exact3
verified fuzzy0
unresolved23
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dc8811af-c606-475a-a86f-704a73a1b8b3 · outbound

This paper cites AgentGroupChat: An Interactive Group Chat Simulacra For Better Eliciting Emergent Behavior.

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need AgentGroupChat: An Interactive Group Chat Simulacra For Better Eliciting Emergent Behavior

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:59:49.441491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation feb19d4a-b71c-4dd0-94b7-ce86b09c38e3 · outbound

This paper cites Large Language Model based Multi-Agents: A Survey of Progress and Challenges.

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need Large Language Model based Multi-Agents: A Survey of Progress and Challenges

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:46.484134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d26ece33-d25a-4277-9904-91bf347a5b23 · outbound

This paper cites "Guinea Pig Trials" Utilizing GPT: A Novel Smart Agent-Based Modeling Approach for Studying Firm Competition and Collusion.

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need "Guinea Pig Trials" Utilizing GPT: A Novel Smart Agent-Based Modeling Approach for Studying Firm Competition and Collusion

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:59:49.284762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d4f3087e-dfc0-4981-829c-702afd545ecf · outbound

This paper cites Understanding the planning of LLM agents: A survey.

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need Understanding the planning of LLM agents: A survey

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:46.694902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 015e86a7-122b-4ce2-b356-4d2d82cb62ea · outbound

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

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:46.850820Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b39749b1-2f7a-4872-a0d5-e86ed5ddc5e0 · outbound

This paper cites From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake News.

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake News

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:46.910398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0944bd3-bc8f-4a4d-9e39-bcfd1e08d8ff · outbound

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

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need Large Language Model Agent: A Survey on Methodology, Applications and Challenges

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:46.981426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a7be85b-39d6-4178-98c4-f0b396ff4d59 · outbound

This paper cites ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs.

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:47.077700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62c66233-0d4f-49a2-ad43-6cd37f7f4da0 · outbound

This paper cites Voyager: An Open-Ended Embodied Agent with Large Language Models.

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need Voyager: An Open-Ended Embodied Agent with Large Language Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:47.212837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32b48444-95f4-4cf8-9396-a8acd0a6f87a · outbound

This paper cites The Rise and Potential of Large Language Model Based Agents: A Survey.

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need The Rise and Potential of Large Language Model Based Agents: A Survey

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:47.372916Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2be25b62-e498-4b09-9229-b6e89587b49f · outbound

This paper cites Can Large Language Model Agents Simulate Human Trust Behavior?.

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need Can Large Language Model Agents Simulate Human Trust Behavior?

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:47.456409Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6a50189-1a32-489d-ae44-88ba8cef310a · outbound

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

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:47.596398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0ee0688-8c94-463c-9ea4-2ffbe4efc7bf · outbound

This paper cites ReAct: Synergizing Reasoning and Acting in Language Models.

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need ReAct: Synergizing Reasoning and Acting in Language Models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:47.737056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29efbb57-8e7c-47be-b7f1-8ad43678b341 · outbound

This paper cites ElectionSim: Massive Population Election Simulation Powered by Large Language Model Driven Agents.

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need ElectionSim: Massive Population Election Simulation Powered by Large Language Model Driven Agents

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:47.875189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 876472e3-9c87-4479-b2e4-b8f56a2be4f3 · outbound

This paper cites Multi-agent Reinforcement Learning: A Comprehensive Survey.

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need Multi-agent Reinforcement Learning: A Comprehensive Survey

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:47.982531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bae085ed-78fd-4f07-891c-c5e2b9fdef77 · outbound

This paper cites SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents.

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:48.116476Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b0256e2-0ddb-4667-8a33-594dcb285da3 · outbound

This paper cites Multi-Agent Reinforcement Learning in Wireless Distributed Networks for 6G.

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need Multi-Agent Reinforcement Learning in Wireless Distributed Networks for 6G

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:48.236412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d643e24-23f0-4a49-a6d2-bd19713a361e · outbound

This paper cites Why Do Multi-Agent LLM Systems Fail?.

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need Why Do Multi-Agent LLM Systems Fail?

Reference 1994

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:46.134886Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc7212c7-a4b7-4ace-a7f6-082d26bbfae2 · outbound

This paper cites S$^3$: Social-network Simulation System with Large Language Model-Empowered Agents.

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need S$^3$: Social-network Simulation System with Large Language Model-Empowered Agents

Reference 1999

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:46.331250Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6149761d-f1eb-47ba-87a1-02e4a9b14614 · outbound

This paper cites LawLuo: A Multi-Agent Collaborative Framework for Multi-Round Chinese Legal Consultation.

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need LawLuo: A Multi-Agent Collaborative Framework for Multi-Round Chinese Legal Consultation

Reference 2008

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:47.142490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1aa3418-c9f3-42cb-88e7-1447aefbd44c · outbound

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

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:47.287255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7aa50b7-30f5-48be-b49e-6a074ebab29c · outbound

This paper cites Improving Factuality and Reasoning in Language Models through Multiagent Debate.

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need Improving Factuality and Reasoning in Language Models through Multiagent Debate

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:46.260809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 471768b2-9b57-427c-97f0-8a48be7bab65 · outbound

This paper cites From MOOC to MAIC: Reshaping Online Teaching and Learning through LLM-driven Agents.

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need From MOOC to MAIC: Reshaping Online Teaching and Learning through LLM-driven Agents

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:47.796261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 203e8601-dc32-4bac-a355-6c3df0d43339 · outbound

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

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:46.617116Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f350c010-8613-40d6-840e-f25b5cfd02ee · outbound

This paper cites TravelAgent: An AI Assistant for Personalized Travel Planning.

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need TravelAgent: An AI Assistant for Personalized Travel Planning

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T23:59:46.195529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07657e3e-f2d1-4499-9eb9-d99ac80b378d · outbound

This paper cites Can Large Language Models Capture Public Opinion about Global Warming? An Empirical Assessment of Algorithmic Fidelity and Bias.

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need Can Large Language Models Capture Public Opinion about Global Warming? An Empirical Assessment of Algorithmic Fidelity and Bias

Reference 2025

Resolution: verified exact; local_arxiv, observed 2026-08-06T23:59:49.040748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation ef30748f-887c-4ce1-a4e1-1824632b542f · inbound

A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems cites this paper.

A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-15T23:21:42.409897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9e5f7387-b329-499c-ad33-3605788a0fdb · inbound

Decoupled Travel Planning with Behavior Forest cites this paper.

Decoupled Travel Planning with Behavior Forest AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need

Reference 12

Resolution: metadata mismatch; arxiv_id, observed 2026-05-09T22:54:16.214576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 55c4f99c-4b7e-46de-a9bd-87e17b2d9090 · inbound

Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems cites this paper.

Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need

Reference 276

Resolution: verified exact; arxiv_id, observed 2026-05-15T03:08:57.768766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bdd6f43d-dc08-488f-900a-fd85f9a9b7f7 · inbound

Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems cites this paper.

Reference 277

Resolution: verified exact; arxiv_id, observed 2026-05-19T16:52:39.989946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a4171943-5c38-41d1-a4a5-2360f9d2869c · inbound

Scaling Behavior of Single LLM-Driven Multi-Agent Systems cites this paper.

Scaling Behavior of Single LLM-Driven Multi-Agent Systems AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-07-01T20:36:12.454969Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1019b223-2a50-4803-881d-2df820e0ddc9 · inbound

Enhancing Decision-Making with Large Language Models through Multi-Agent Fictitious Play cites this paper.

Enhancing Decision-Making with Large Language Models through Multi-Agent Fictitious Play AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:49:18.504105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.