Paper Citation Record · LEDGER

RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment

As of 20 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 5 inbound Pith citation observations for arXiv:2412.13746.

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

pith.paper-citation-record.v1
2412.13746 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T12:54:54.621970Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T17:18:40.319693Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T02:22:25.373813Z

Reference resolution

21 of 21 outbound references displayed

verified exact0
verified fuzzy5
unresolved16
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3e2d1f7b-7bf2-4cf5-99f2-0c6974aea1bb · outbound

This paper cites an unresolved cited work.

RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-11T12:54:55.039764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 690fb714-743f-4265-ba60-533559de0035 · outbound

This paper cites an unresolved cited work.

RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-11T12:54:55.017270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ed43ecdf-fb7d-44d0-89de-0215631e57e0 · outbound

This paper cites an unresolved cited work.

RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-11T12:54:54.994352Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f2653b93-2d53-49c6-a794-10ce992d3ea7 · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T12:54:54.526151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c18656a-cafe-4d2b-9087-c56fc8b7b275 · outbound

This paper cites RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation.

RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T12:54:54.533160Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80984ea3-6cb9-4a08-b7ac-7a8cd0cc4a68 · outbound

This paper cites Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models.

RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T12:54:54.539822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34b4fa2c-c892-47f4-b5bd-1e2b47aa59f6 · outbound

This paper cites LongReward: Improving Long-context Large Language Models with AI Feedback.

RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment LongReward: Improving Long-context Large Language Models with AI Feedback

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T12:54:54.546627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2848aab8-c7f8-41ac-b735-a88ace2d88d3 · outbound

This paper cites for having been specially commended in combat.

RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment for having been specially commended in combat

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:54:54.902942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5bfc7613-123e-4c6a-8a93-a64cd961b885 · outbound

This paper cites an unresolved cited work.

RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-11T12:54:54.974925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d8ea0191-f998-4288-9056-b23221704f8a · outbound

This paper cites an unresolved cited work.

RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-11T12:54:54.956368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 52321dd7-78fa-4f96-93df-6efd944ece21 · outbound

This paper cites an unresolved cited work.

RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-11T12:54:54.938928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 736f8006-d770-4e23-aa0e-a8e1f6c6a5a5 · outbound

This paper cites an unresolved cited work.

RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-11T12:54:54.920859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5ca20279-d2de-4143-9ffa-f2dd20d62e3c · outbound

This paper cites However , when it comes to purchasing pistols ( firearms that are 26 inches or less in length ) , there are specific age restrictions and other requirements that need to be met.

RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment However , when it comes to purchasing pistols ( firearms that are 26 inches or less in length ) , there are specific age restrictions and other requirements that need to be met

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:54:54.884398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 122906f1-b43c-41e4-acc4-90ce0d3f1749 · outbound

This paper cites It also states that at age 21 , a person can legally buy a firearm from a Federally Licensed ( FFL ) dealer.

RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment It also states that at age 21 , a person can legally buy a firearm from a Federally Licensed ( FFL ) dealer

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:54:54.866185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9d258e02-12b8-4987-bfd4-7aa5f0923d4e · outbound

This paper cites Political Differences ? Study by Professor W Ben McCartney ,.

RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment Political Differences ? Study by Professor W Ben McCartney ,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:54:54.849535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 43f546f0-f7c1-4846-988f-adfbe6152531 · outbound

This paper cites an unresolved cited work.

RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-11T12:54:54.830562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation dad15bf3-1b9e-43c1-8b09-8fae0c2c50c8 · outbound

This paper cites an unresolved cited work.

RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-11T12:54:54.812699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ef817516-d091-4fed-b7cd-619d2ec7a9f1 · outbound

This paper cites " Bet Shira.

RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment " Bet Shira

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T12:54:54.793997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b82689fd-1e95-4763-a268-884fda411c31 · outbound

This paper cites M-RewardBench: Evaluating Reward Models in Multilingual Settings.

RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment M-RewardBench: Evaluating Reward Models in Multilingual Settings

Reference 158

Resolution: unresolved; no resolver link, observed 2026-08-11T12:54:54.513004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9dd21aaf-420c-4c30-999e-e3710d3a08a9 · outbound

This paper cites Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation.

RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment Understand What LLM Needs: Dual Preference Alignment for Retrieval-Augmented Generation

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-11T12:54:54.505804Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8618dfc9-139f-4928-a38a-ddb288061f71 · outbound

This paper cites Tulu 3: Pushing Frontiers in Open Language Model Post-Training.

RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment Tulu 3: Pushing Frontiers in Open Language Model Post-Training

Reference 6781

Resolution: unresolved; no resolver link, observed 2026-08-11T12:54:54.519604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 4099f462-0776-4890-a203-4333cab0c352 · inbound

InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model cites this paper.

InternLM-XComposer2.5-Reward: A Simple Yet Effective Multi-Modal Reward Model RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T17:18:40.319693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ddaef899-e2db-45ed-910e-fe64be20b44b · inbound

Supervising the search process produces reliable and generalizable information-seeking agents cites this paper.

Supervising the search process produces reliable and generalizable information-seeking agents RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:22:25.376726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4438838a-bf44-46c5-b41b-361d27448d09 · inbound

RewardBench 2: Advancing Reward Model Evaluation cites this paper.

RewardBench 2: Advancing Reward Model Evaluation RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-19T11:22:16.781117Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 563cd57d-4319-48dc-a29a-5bdf892a5138 · inbound

Agent-RewardBench: Towards a Unified Benchmark for Reward Modeling across Perception, Planning, and Safety in Real-World Multimodal Agents cites this paper.

Agent-RewardBench: Towards a Unified Benchmark for Reward Modeling across Perception, Planning, and Safety in Real-World Multimodal Agents RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T22:36:43.134536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca3a6e68-0545-4c7a-aadb-9fb082c0ee22 · inbound

Activation Reward Models for Few-Shot Model Alignment cites this paper.

Activation Reward Models for Few-Shot Model Alignment RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T21:02:42.396463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.