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Paper Citation Record · LEDGER

SocraSynth: Multi-LLM Reasoning with Conditional Statistics

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2402.06634.

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

pith.paper-citation-record.v1
2402.06634 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:16:14.724750Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

6
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation f3405865-6500-47e3-b95e-57cf8d3f92ea · inbound

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration cites this paper.

Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration SocraSynth: Multi-LLM Reasoning with Conditional Statistics

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-06T21:16:14.724750Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:16:14.724750Z digest=sha256:6644d82c273ccc6882ed3204347956f9d04b4a82cfd87ee05517d8f11c2c184a

Observation fc1a0a7b-6320-4a0d-a643-1bcc4a1c019c · 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 SocraSynth: Multi-LLM Reasoning with Conditional Statistics

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-06T18:24:53.489055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:24:53.489055Z digest=sha256:25ed1f99c148c72d20a6b58ccaff162123e2e780de77d3a2c36b15f984e2bcb6

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

source=pdf_text observed=2026-08-06T18:12:58.107269Z digest=sha256:518e86c6e56313579b3cd5d6f2432a9089511c7d4fa95a05feb4a19ab0c09a1f

Observation 5f9ad3fa-402b-4ca1-a0de-5f84950cb475 · 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 SocraSynth: Multi-LLM Reasoning with Conditional Statistics

Reference 12

Resolution
verified exact
arxiv_id, observed 2026-05-15T23:21:42.305365Z

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.

source=pdf_text observed=2026-05-15T23:21:42.029285Z digest=sha256:d1d6afc8f58e648d3d7616c7cb8ef47c5d2d1ce27f463f46d68bcf89405d8602

Observation 278b5cc0-5ca8-4116-a279-3bc6df9edfea · inbound

The Reasoning Trap: An Information-Theoretic Bound on Closed-System Multi-Step LLM Reasoning cites this paper.

The Reasoning Trap: An Information-Theoretic Bound on Closed-System Multi-Step LLM Reasoning SocraSynth: Multi-LLM Reasoning with Conditional Statistics

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-11T09:41:04.036345Z

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.

source=arxiv_source observed=2026-05-10T15:52:43.274993Z digest=sha256:da2093601cf553fee0f283be9a1165c741ee383e481db7503f22136787826a1b

Observation 007bbe2d-6043-4d16-9cf6-55002e95228b · inbound

AI at the Front Lines of Platform Governance: Using LLMs to Support Illegal Content Reporting under the Digital Services Act cites this paper.

AI at the Front Lines of Platform Governance: Using LLMs to Support Illegal Content Reporting under the Digital Services Act SocraSynth: Multi-LLM Reasoning with Conditional Statistics

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-05-25T03:25:18.151284Z

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.

source=pdf_text observed=2026-05-25T03:23:29.493915Z digest=sha256:8d584ca60fc962ca7b0587fb3cd6e219dad12fced8578afa60352ff71fab62e3

Observation d585f8ef-5e49-4aa2-a2f4-e384ab3e6aec · inbound

AI at the Front Lines of Platform Governance: Using LLMs to Support Illegal Content Reporting under the Digital Services Act cites this paper.

AI at the Front Lines of Platform Governance: Using LLMs to Support Illegal Content Reporting under the Digital Services Act SocraSynth: Multi-LLM Reasoning with Conditional Statistics

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-25T03:26:35.800851Z

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.

source=pdf_text observed=2026-05-25T03:23:29.493915Z digest=sha256:9f2db442be95c077c44e2b573509ed90dcdbffb0e85f3dad8d2a98ad78faaa0b