Paper Citation Record · LEDGER

Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis

As of 8 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 6 inbound Pith citation observations for arXiv:2602.00846.

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

pith.paper-citation-record.v1
2602.00846 v2

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T06:00:50.338348Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T20:36:56.521633Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T23:06:20.525726Z

Reference resolution

17 of 17 outbound references displayed

verified exact0
verified fuzzy0
unresolved15
parse uncertain1
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e357885a-119e-4db6-ac51-d82d8adbe2fe · outbound

This paper cites A”, “B”, or “equal.

Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis A”, “B”, or “equal

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T06:00:47.593343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c558a13-be46-46ef-8414-3c220520019c · outbound

This paper cites an unresolved cited work.

Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis Unresolved cited work

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T06:00:47.266170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 744666d1-78d0-4806-9fb7-7277b12dcc1e · outbound

This paper cites reasoning.

Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis reasoning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T06:00:47.725120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cce84fdd-284d-4e6c-b206-ecd94ec02179 · outbound

This paper cites Thisreference answerwill serve as the gold standard.

Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis Thisreference answerwill serve as the gold standard

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T06:00:48.151028Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d22eef55-2e51-4b5e-a905-ce7beed3f35d · outbound

This paper cites reasoning.

Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis reasoning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T06:00:48.663669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42a80df2-d919-4cc7-9d77-f2d6f2bda0e2 · outbound

This paper cites an unresolved cited work.

Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis Unresolved cited work

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T06:00:49.089439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4313aae2-e189-4b4d-bbe1-804445ce7a5e · outbound

This paper cites Thisreference answerwill be used as the gold stan- dard in your evaluation.

Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis Thisreference answerwill be used as the gold stan- dard in your evaluation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T06:00:49.162516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55c23518-44be-43d4-8046-51fb0fc5b38e · outbound

This paper cites an unresolved cited work.

Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis Unresolved cited work

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T06:00:49.297117Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94d3f106-481a-41f8-acb7-98e8c84111ce · outbound

This paper cites an unresolved cited work.

Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis Unresolved cited work

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T06:00:49.450863Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d88a4f3-ea6b-40ae-8973-ff4e479cc0f0 · outbound

This paper cites A”, “B”, or “equal.

Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis A”, “B”, or “equal

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-03T06:00:49.617545Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32181cbb-866e-44ec-8e77-2dadd7e83e6e · outbound

This paper cites {” and end with “}.

Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis {” and end with “}

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-03T06:00:49.744987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 738e4bff-a2b3-4c00-b09f-5f1f77b0958e · outbound

This paper cites an unresolved cited work.

Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis Unresolved cited work

Reference 30

Resolution: parse uncertain; no resolver link, observed 2026-08-03T06:00:49.886410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67daa269-a403-4eae-8c4d-dca9b0331b91 · outbound

This paper cites an unresolved cited work.

Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis Unresolved cited work

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-03T06:00:50.060823Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f05b1555-7e9a-462d-a2a5-67d97cd2b587 · outbound

This paper cites an unresolved cited work.

Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis Unresolved cited work

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-03T06:00:50.214006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfeda259-6e42-49b1-9056-4ef31680e704 · outbound

This paper cites score_A": [0-10],.

Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis score_A": [0-10],

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-03T06:00:50.338348Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 847335d8-7547-4ca6-a0d8-4876e69b82fb · outbound

This paper cites Direct Preference Optimization of Video Large Multimodal Models from Language Model Reward.

Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis Direct Preference Optimization of Video Large Multimodal Models from Language Model Reward

Reference 2024

Resolution: malformed identifier; no resolver link, observed 2026-08-03T06:00:46.835828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7852e5ee-89b0-40e9-8f72-b4afdb5c8030 · outbound

This paper cites Learning to Generate Structured Output with Schema Reinforcement Learning.

Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis Learning to Generate Structured Output with Schema Reinforcement Learning

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-03T06:00:46.754400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation fdcacea5-c8b7-40a3-9453-ed08dc977960 · inbound

OmniGAIA: Towards Native Omni-Modal AI Agents cites this paper.

OmniGAIA: Towards Native Omni-Modal AI Agents Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis

Reference 2026

Resolution: unresolved; no resolver link, observed 2026-08-02T20:36:56.521633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 619a7f8e-818b-49b2-8e32-18b49440bb9e · inbound

Visual Preference Optimization with Rubric Rewards cites this paper.

Visual Preference Optimization with Rubric Rewards Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis

Reference 40

Resolution: verified exact; arxiv_id, observed 2026-07-08T02:18:41.255312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e263baa2-06bd-4747-86c2-0eca1dd19f0c · inbound

EarlyTom: Early Token Compression Completes Fast Video Understanding cites this paper.

EarlyTom: Early Token Compression Completes Fast Video Understanding Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-07-08T02:18:41.255312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 47f2f70a-36e8-4660-9b45-93b40b8176cf · inbound

Reinforcement Learning with Robust Rubric Rewards cites this paper.

Reinforcement Learning with Robust Rubric Rewards Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-07-08T02:18:41.255312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 56d37dab-4fe5-41a2-8e94-82c19423c538 · inbound

A Primer in Post-Training Reasoning Data: What We Know About How It Works cites this paper.

A Primer in Post-Training Reasoning Data: What We Know About How It Works Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis

Reference 8

Resolution: metadata mismatch; arxiv_id, observed 2026-07-08T02:18:41.255312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9cda11fd-c187-4577-8691-28bea1c15d20 · inbound

Weak-to-Strong On-Policy Distillation cites this paper.

Weak-to-Strong On-Policy Distillation Omni-RRM: Advancing Omni Reward Modeling via Automatic Rubric-Grounded Preference Synthesis

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T00:26:15.884060Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.