Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need
As of 1 August 2026, this Paper Citation Record lists 0 of 0 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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-07-31T06:34:12.847434+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-28T18:11:51.416335Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T00:49:18.501603Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
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 2026-07-31T06:34:12.847434+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 2026-07-31T06:34:12.847434+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 2026-07-31T06:34:12.847434+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.
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 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 2026-07-31T06:34:12.847434+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 2026-07-31T06:34:12.847434+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 2026-07-31T06:34:12.847434+00:00.