Paper Citation Record · LEDGER

The Perfect Blend: Redefining RLHF with Mixture of Judges

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2409.20370.

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

pith.paper-citation-record.v1
2409.20370 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:57:53.831378Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T12:26:31.581809Z

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 92f1ac4a-af66-4441-a1e6-c82f7e85207d · inbound

Self-Generated Critiques Boost Reward Modeling for Language Models cites this paper.

Self-Generated Critiques Boost Reward Modeling for Language Models The Perfect Blend: Redefining RLHF with Mixture of Judges

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-12T12:58:30.725632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78d58cf3-19e4-4a9c-b0dd-b33b719e9368 · inbound

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art cites this paper.

Multi-objective Deep Learning: Taxonomy and Survey of the State of the Art The Perfect Blend: Redefining RLHF with Mixture of Judges

Reference 199

Resolution: unresolved; no resolver link, observed 2026-08-12T04:19:52.094559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0f7f0db-581a-470f-b3a7-d376c1862577 · 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 Perfect Blend: Redefining RLHF with Mixture of Judges

Reference 266

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:08:35.786805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 75b57ebb-2c3e-4acd-9fe1-0523b5f07d79 · inbound

Reinforcement Learning from User Feedback cites this paper.

Reinforcement Learning from User Feedback The Perfect Blend: Redefining RLHF with Mixture of Judges

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T15:31:19.744551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9d12446-f629-4000-8a3e-681432b58a87 · inbound

Boosting LLM Reasoning via Spontaneous Self-Correction cites this paper.

Boosting LLM Reasoning via Spontaneous Self-Correction The Perfect Blend: Redefining RLHF with Mixture of Judges

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T05:51:30.744527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b90fd3dc-1854-450a-b2cc-e1efdc81e7d4 · inbound

Bridging the Gap in Vision Language Models in Identifying Unsafe Concepts Across Modalities cites this paper.

Bridging the Gap in Vision Language Models in Identifying Unsafe Concepts Across Modalities The Perfect Blend: Redefining RLHF with Mixture of Judges

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T17:21:35.355204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation baadd476-aa13-4853-a946-7bf01cdf7f50 · inbound

Improving Large Vision and Language Models by Learning from a Panel of Peers cites this paper.

Improving Large Vision and Language Models by Learning from a Panel of Peers The Perfect Blend: Redefining RLHF with Mixture of Judges

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-05T12:27:27.664545Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0f80efe-77d1-4ad1-a195-0bc56ddfee7f · inbound

Debate2Create: Robot Co-design via Multi-Agent LLM Debate cites this paper.

Debate2Create: Robot Co-design via Multi-Agent LLM Debate The Perfect Blend: Redefining RLHF with Mixture of Judges

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T07:27:04.004624Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b2ef0f4-bce6-4a99-9403-4dad04cc9755 · inbound

Token-Level LLM Collaboration via FusionRoute cites this paper.

Token-Level LLM Collaboration via FusionRoute The Perfect Blend: Redefining RLHF with Mixture of Judges

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-22T12:26:31.586299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0daa22a3-e4fe-4bc8-8bd5-3fd64f227851 · inbound

DSpark: Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generation cites this paper.

DSpark: Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generation The Perfect Blend: Redefining RLHF with Mixture of Judges

Reference 204

Resolution: unresolved; no resolver link, observed 2026-07-11T08:05:17.460513Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f1707d8-0451-4278-be2d-ea5f5f65f148 · inbound

SMOPD: Multi-Reward Reinforcement Learning via Specialize-and-Merge Online Policy Distillation cites this paper.

SMOPD: Multi-Reward Reinforcement Learning via Specialize-and-Merge Online Policy Distillation The Perfect Blend: Redefining RLHF with Mixture of Judges

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-15T14:57:53.831378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.