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Paper Citation Record · LEDGER

The Fellowship of the LLMs: Multi-Model Workflows for Synthetic Preference Optimization Dataset Generation

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2408.08688.

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

pith.paper-citation-record.v1
2408.08688 v7

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:25:19.073588Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T04:26:38.014801Z

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 8c991d8e-4d47-487b-a3ef-53a7dde8e129 · inbound

ROSE: A Reward-Oriented Data Selection Framework for LLM Task-Specific Instruction Tuning cites this paper.

ROSE: A Reward-Oriented Data Selection Framework for LLM Task-Specific Instruction Tuning The Fellowship of the LLMs: Multi-Model Workflows for Synthetic Preference Optimization Dataset Generation

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-12T05:13:41.372000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T05:13:41.372000Z digest=sha256:6ebebe3cb35c12ed2bc951c14faca477ce7bb8d1ba93d564ea5724462da4d28e

Observation beaa721e-40f3-4c38-8774-9de5b937a2a3 · inbound

MAG-V: A Multi-Agent Framework for Synthetic Data Generation and Verification cites this paper.

MAG-V: A Multi-Agent Framework for Synthetic Data Generation and Verification The Fellowship of the LLMs: Multi-Model Workflows for Synthetic Preference Optimization Dataset Generation

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-12T10:20:15.801221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-12T10:20:15.801221Z digest=sha256:e2b8f8248ebfb1c924a4e385222c732da742407e9666af29c7042167dc2ed6e5

Observation a9879c30-9ce1-418c-b32c-f6567150d2cf · 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 The Fellowship of the LLMs: Multi-Model Workflows for Synthetic Preference Optimization Dataset Generation

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-11T23:08:34.527119Z

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.

source=pdf_text observed=2026-05-11T23:08:34.312466Z digest=sha256:77e0f0ff03a5ed42447eb2c131def4bf7f95afe8585948a1f48a4b625f9f2d51

Observation 3addd43c-eb87-4832-b86b-10a29e60f52e · inbound

TalkPlayData 2: An Agentic Synthetic Data Pipeline for Multimodal Conversational Music Recommendation cites this paper.

TalkPlayData 2: An Agentic Synthetic Data Pipeline for Multimodal Conversational Music Recommendation The Fellowship of the LLMs: Multi-Model Workflows for Synthetic Preference Optimization Dataset Generation

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-15T17:25:19.073588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T17:25:19.073588Z digest=sha256:1e1bcf848b128bb7cc4b983cf77f6396e5d9ce358113d884e8e0f9f1e4d7afa7

Observation c3c613e0-a807-4982-8aaa-ef24a2d403c5 · inbound

When Planning Fails Despite Correct Execution: On Epistemic Calibration for LLM-Based Multi-Agent Systems cites this paper.

When Planning Fails Despite Correct Execution: On Epistemic Calibration for LLM-Based Multi-Agent Systems The Fellowship of the LLMs: Multi-Model Workflows for Synthetic Preference Optimization Dataset Generation

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-05-25T04:26:38.018116Z

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.

source=arxiv_source observed=2026-05-25T04:25:26.710488Z digest=sha256:d2e6a2732d6e9a14184c93b3620a247476435aa609acf5af5578c8cd555b9edc