Paper Citation Record · LEDGER

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

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2305.11595.

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

pith.paper-citation-record.v1
2305.11595 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 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 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T22:33:09.670060Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T20:13:22.968014Z

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 e735533d-388b-4c7c-b160-bf11718f877f · inbound

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

Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate

Reference 78

Resolution: verified exact; arxiv_id, observed 2026-05-14T00:00:15.890391Z

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 3a1b127a-b6ab-49e9-b162-de8c53869399 · inbound

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

When One LLM Drools, Multi-LLM Collaboration Rules Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate

Reference 120

Resolution: unresolved; no resolver link, observed 2026-08-08T22:33:09.670060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d4c4cff-e61d-460f-be5d-af3b7cfde930 · inbound

S$^2$-MAD: Breaking the Token Barrier to Enhance Multi-Agent Debate Efficiency cites this paper.

S$^2$-MAD: Breaking the Token Barrier to Enhance Multi-Agent Debate Efficiency Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-08T21:33:37.693898Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7dd22778-e937-45f8-8b8c-25ec876de622 · inbound

Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent Prompting Strategy for Text Classification cites this paper.

Don't Just Demo, Teach Me the Principles: A Principle-Based Multi-Agent Prompting Strategy for Text Classification Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-08T13:39:04.490242Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e7de63f-51e4-4eb2-8727-63c504aff7b6 · inbound

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories cites this paper.

Agentic Feature Augmentation: Unifying Selection and Generation with Teaming, Planning, and Memories Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T15:29:06.100869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need cites this paper.

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 e6a5dd2f-d44b-49b2-acf4-a03b35312ccd · inbound

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences cites this paper.

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T18:24:52.916540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a60e3e36-e01e-43d2-b319-9f9fcb5d416a · 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 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 aafacac0-6936-4c84-8114-78ace9123618 · inbound

DRF: LLM-AGENT Dynamic Reputation Filtering Framework cites this paper.

DRF: LLM-AGENT Dynamic Reputation Filtering Framework Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T05:04:52.739488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a131de4-6bea-4263-a03f-353cc8d4c126 · inbound

Debate-Enhanced Pseudo Labeling and Frequency-Aware Progressive Debiasing for Weakly-Supervised Camouflaged Object Detection with Scribble Annotations cites this paper.

Debate-Enhanced Pseudo Labeling and Frequency-Aware Progressive Debiasing for Weakly-Supervised Camouflaged Object Detection with Scribble Annotations Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-16T20:13:22.971012Z

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.