Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-08T17:29:13.549559Z
Paper Citation Record · LEDGER
StoryAlign: Evaluating and Training Reward Models for Story Generation
As of 6 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2605.04831.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
42 of 42 outbound references displayed
- verified exact16
- verified fuzzy10
- unresolved4
- parse uncertain0
- malformed identifier1
- metadata mismatch11
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation c4aeeda9-41e4-459a-aeca-3db13a84a09d · outbound
This paper cites Qwen Technical Report.
StoryAlign: Evaluating and Training Reward Models for Story Generation Qwen Technical Report
Reference 1
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-11T17:31:07.710832Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 4470974a-34f7-43ee-b112-b21e7bfb9516 · outbound
This paper cites Large Language Monkeys: Scaling Inference Compute with Repeated Sampling.
StoryAlign: Evaluating and Training Reward Models for Story Generation Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
Reference 2
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-12T04:42:23.872588Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation c9f39f89-3136-4683-bdd5-23b2544041dd · outbound
This paper cites Internlm2 technical report.
StoryAlign: Evaluating and Training Reward Models for Story Generation Internlm2 technical report
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:31:54.393749Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation c777bfe9-a4be-4265-a37a-5f570e73b893 · outbound
This paper cites InternLM2 Technical Report.
StoryAlign: Evaluating and Training Reward Models for Story Generation InternLM2 Technical Report
Reference 4
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-15T11:44:38.670888Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 4eb4a61e-6176-4902-a679-4567c307a6bf · outbound
This paper cites Art or Artifice? Large Language Models and the False Promise of Creativity.
StoryAlign: Evaluating and Training Reward Models for Story Generation Art or Artifice? Large Language Models and the False Promise of Creativity
Reference 5
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T17:31:07.697760Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation ca838c40-d0b6-4acf-8f06-b74ce318836a · outbound
This paper cites Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities.
StoryAlign: Evaluating and Training Reward Models for Story Generation Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
Reference 6
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-11T17:31:07.632449Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 44308e25-bf3c-42a2-9ee0-74d2420366a5 · outbound
This paper cites an unresolved cited work.
StoryAlign: Evaluating and Training Reward Models for Story Generation Unresolved cited work
Reference 7
- Resolution
- unresolved
- raw_fallback, observed 2026-05-26T07:31:54.409930Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 66cb0e6f-ee34-4fe3-812d-1d360a9c53c1 · outbound
This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.
StoryAlign: Evaluating and Training Reward Models for Story Generation DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Reference 8
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T17:31:07.586556Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation af1524c6-ab14-47d6-980b-fc1416bfd25c · outbound
This paper cites Quantile Regression for Distributional Reward Models in RLHF.
StoryAlign: Evaluating and Training Reward Models for Story Generation Quantile Regression for Distributional Reward Models in RLHF
Reference 9
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T17:31:07.636900Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 372f4f43-56ab-4385-95fe-1cb073ade821 · outbound
This paper cites doi: 10.18653/v1/P18-1082.
StoryAlign: Evaluating and Training Reward Models for Story Generation doi: 10.18653/v1/P18-1082
Reference 10
- Resolution
- verified exact
- doi, observed 2026-05-08T20:04:06.362625Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation a7091523-85e3-40af-b421-808381a42604 · outbound
This paper cites doi: 10.18653/v1/P19-1254.
StoryAlign: Evaluating and Training Reward Models for Story Generation doi: 10.18653/v1/P19-1254
Reference 11
- Resolution
- verified exact
- doi, observed 2026-05-08T20:04:06.357792Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 1ce6660c-0d80-466f-b9e7-3c2317bc2b09 · outbound
This paper cites LitBench: A Benchmark and Dataset for Reliable Evaluation of Creative Writing.
StoryAlign: Evaluating and Training Reward Models for Story Generation LitBench: A Benchmark and Dataset for Reliable Evaluation of Creative Writing
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T17:31:07.707466Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation adbd8193-fb2b-4579-9161-4e272d7230c7 · outbound
This paper cites Agents' Room: Narrative Generation through Multi-step Collaboration.
StoryAlign: Evaluating and Training Reward Models for Story Generation Agents' Room: Narrative Generation through Multi-step Collaboration
Reference 13
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T17:31:07.718648Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation b63bc4bd-f052-4cd4-91f5-794ae6c39385 · outbound
This paper cites Writing-Zero: Bridge the Gap Between Non-verifiable Tasks and Verifiable Rewards.
StoryAlign: Evaluating and Training Reward Models for Story Generation Writing-Zero: Bridge the Gap Between Non-verifiable Tasks and Verifiable Rewards
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T17:31:07.655332Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 7789fe3b-b0e0-4c0d-8494-b1bc33839093 · outbound
This paper cites http://www.jstor.org/ stable/2332226.
StoryAlign: Evaluating and Training Reward Models for Story Generation http://www.jstor.org/ stable/2332226
Reference 15
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T17:31:07.687421Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation e5d0f684-d099-42a4-9e30-f69e41c108d7 · outbound
This paper cites Navigating the Path of Writing: Outline-guided Text Generation with Large Language Models.
StoryAlign: Evaluating and Training Reward Models for Story Generation Navigating the Path of Writing: Outline-guided Text Generation with Large Language Models
Reference 16
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T17:31:07.660380Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 08081211-5261-4700-b9c9-0904304f7abe · outbound
This paper cites Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs.
StoryAlign: Evaluating and Training Reward Models for Story Generation Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs
Reference 17
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-17T16:18:01.701777Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 095bb39a-a64e-430f-a6b8-04a04abbd3d0 · outbound
This paper cites MoPS: Modular Story Premise Synthesis for Open-Ended Automatic Story Generation.
StoryAlign: Evaluating and Training Reward Models for Story Generation MoPS: Modular Story Premise Synthesis for Open-Ended Automatic Story Generation
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T17:31:07.571420Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation c7283e2f-061c-46fe-b0d6-5114ffd7cdc7 · outbound
This paper cites Accessed: 2025-02-04.
StoryAlign: Evaluating and Training Reward Models for Story Generation Accessed: 2025-02-04
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:31:54.396877Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 47a6c24c-a290-4a22-a5e7-524cdcc21293 · outbound
This paper cites EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models.
StoryAlign: Evaluating and Training Reward Models for Story Generation EQ-Bench: An Emotional Intelligence Benchmark for Large Language Models
Reference 20
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T17:31:07.621013Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 8189beed-aa38-406d-96fc-d76ca7036d29 · outbound
This paper cites Agentic Reward Modeling: Integrating Human Preferences with Verifiable Correctness Signals for Reliable Reward Systems.
StoryAlign: Evaluating and Training Reward Models for Story Generation Agentic Reward Modeling: Integrating Human Preferences with Verifiable Correctness Signals for Reliable Reward Systems
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T17:31:07.649828Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 6d1f5932-d9df-4b65-8943-72d3991e93ce · outbound
This paper cites Constraint Back-translation Improves Complex Instruction Following of Large Language Models.
StoryAlign: Evaluating and Training Reward Models for Story Generation Constraint Back-translation Improves Complex Instruction Following of Large Language Models
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T17:31:07.693503Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 74bd643a-8e73-48be-be89-e8b3f2e1e1e3 · outbound
This paper cites Qwen2.5 Technical Report.
StoryAlign: Evaluating and Training Reward Models for Story Generation Qwen2.5 Technical Report
Reference 23
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T17:31:07.672332Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation de41791b-ffe1-4414-9954-905a05f08121 · outbound
This paper cites Verbosity bias in preference labeling by large language models.
StoryAlign: Evaluating and Training Reward Models for Story Generation Verbosity bias in preference labeling by large language models
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:31:54.399925Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 77bf7f07-dc8a-4a41-8fdf-9904ae9f6240 · outbound
This paper cites GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models.
StoryAlign: Evaluating and Training Reward Models for Story Generation GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
Reference 25
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T17:50:08.654972Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 6e5e2916-47b9-4935-9b3a-d04314fb4766 · outbound
This paper cites QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning.
StoryAlign: Evaluating and Training Reward Models for Story Generation QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning
Reference 26
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T17:31:07.683022Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 9a898a52-06e2-452b-a3c0-51e513f114e0 · outbound
This paper cites Guiding and Diversifying LLM-Based Story Generation via Answer Set Programming.
StoryAlign: Evaluating and Training Reward Models for Story Generation Guiding and Diversifying LLM-Based Story Generation via Answer Set Programming
Reference 27
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T17:31:07.552951Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 54a72acd-d1a8-44c9-b963-254fe5357a45 · outbound
This paper cites Grok (version 2025-09-14).
StoryAlign: Evaluating and Training Reward Models for Story Generation Grok (version 2025-09-14)
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:31:54.388271Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation c58090b1-def5-481d-8983-2a992cc12670 · outbound
This paper cites StoryWriter: A Multi-Agent Framework for Long Story Generation.
StoryAlign: Evaluating and Training Reward Models for Story Generation StoryWriter: A Multi-Agent Framework for Long Story Generation
Reference 29
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T17:31:07.663724Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 68714fb5-68aa-476d-9c59-4a3b6fff7f38 · outbound
This paper cites Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning.
StoryAlign: Evaluating and Training Reward Models for Story Generation Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning
Reference 30
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T17:31:07.625809Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation a3a564ad-2141-44d4-aaad-5bc368c63ae0 · outbound
This paper cites Qwen3 technical report.
StoryAlign: Evaluating and Training Reward Models for Story Generation Qwen3 technical report
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:31:54.390851Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 20f00639-504e-4917-bf06-baef71e7a0cb · outbound
This paper cites Qwen3 Technical Report.
StoryAlign: Evaluating and Training Reward Models for Story Generation Qwen3 Technical Report
Reference 32
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-11T17:31:07.609349Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation b80ce6e4-1d60-4892-af25-9ed48cca04e3 · outbound
This paper cites A Comprehensive Survey of Reward Models: Taxonomy, Applications, Challenges, and Future.
StoryAlign: Evaluating and Training Reward Models for Story Generation A Comprehensive Survey of Reward Models: Taxonomy, Applications, Challenges, and Future
Reference 33
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T17:31:07.645525Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 8ed93a61-1fcb-49c1-898d-60526c82308f · outbound
This paper cites a scientist proposes a theorem proving that free will is an illusion and faces backlash from multiple sides.
StoryAlign: Evaluating and Training Reward Models for Story Generation a scientist proposes a theorem proving that free will is an illusion and faces backlash from multiple sides
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:31:54.382520Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 46947e5d-5615-49b3-afea-b32f98d62c56 · outbound
This paper cites an unresolved cited work.
StoryAlign: Evaluating and Training Reward Models for Story Generation Unresolved cited work
Reference 35
- Resolution
- unresolved
- raw_fallback, observed 2026-05-26T07:31:54.406515Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 722c1558-e90a-44dd-8249-295671a28d03 · outbound
This paper cites On one hand we prompt larger LLMs to evaluate two stories generated by smaller LLMs, on the other hand we compare a story from a larger LLM with a story from a smaller LLM.
StoryAlign: Evaluating and Training Reward Models for Story Generation On one hand we prompt larger LLMs to evaluate two stories generated by smaller LLMs, on the other hand we compare a story from a larger LLM with a story from a smaller LLM
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:31:54.379308Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 8b801b60-4747-4e37-84a5-512980f90c76 · outbound
This paper cites an unresolved cited work.
StoryAlign: Evaluating and Training Reward Models for Story Generation Unresolved cited work
Reference 37
- Resolution
- unresolved
- raw_fallback, observed 2026-05-26T07:31:54.385620Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 81d6a349-98ca-4c51-bb53-6012c2753c8f · outbound
This paper cites “Tie” means both models select the same story.
StoryAlign: Evaluating and Training Reward Models for Story Generation “Tie” means both models select the same story
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:31:54.403301Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 7d5f7925-951c-4f11-8522-1233a7425964 · outbound
This paper cites (-) Premise Back-generation.
StoryAlign: Evaluating and Training Reward Models for Story Generation (-) Premise Back-generation
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:31:54.373059Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation b7788b1e-c191-4e2c-b2f4-d5416d0adb42 · outbound
This paper cites H.4 LINGUISTICANALYSIS We conduct linguistic analysis on stories selected by different reward models.
StoryAlign: Evaluating and Training Reward Models for Story Generation H.4 LINGUISTICANALYSIS We conduct linguistic analysis on stories selected by different reward models
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-26T07:31:54.367646Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation d6dafab6-ba94-4304-8e4e-2298b46eb0a2 · outbound
This paper cites Difference.
StoryAlign: Evaluating and Training Reward Models for Story Generation Difference
Reference 41
- Resolution
- malformed identifier
- raw_fallback, observed 2026-05-26T07:31:54.370206Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Observation 59652d13-0c43-48f5-a949-2e3ffb5b38b6 · outbound
This paper cites an unresolved cited work.
StoryAlign: Evaluating and Training Reward Models for Story Generation Unresolved cited work
Reference 42
- Resolution
- unresolved
- raw_fallback, observed 2026-05-26T07:31:54.375812Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.
Pith citing papers
No inbound Pith citation observations are available.