Paper Citation Record · LEDGER

Learning to Reason for Long-Form Story Generation

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2503.22828.

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

pith.paper-citation-record.v1
2503.22828 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:13:14.743741Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation a27acc06-c949-4c8c-8792-6b2e0fbe7e01 · inbound

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess cites this paper.

Can Large Language Models Develop Strategic Reasoning? Post-training Insights from Learning Chess Learning to Reason for Long-Form Story Generation

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T21:13:14.743741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aefa57ea-aeb5-415b-8397-01a463fa3bba · inbound

Scalpel vs. Hammer: GRPO Amplifies Existing Capabilities, SFT Replaces Them cites this paper.

Scalpel vs. Hammer: GRPO Amplifies Existing Capabilities, SFT Replaces Them Learning to Reason for Long-Form Story Generation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T17:52:01.587596Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3c0a4ee-ab0d-4674-a0e0-a4c00805dfa1 · inbound

PlotTwist: A Creative Plot Generation Framework with Small Language Models cites this paper.

PlotTwist: A Creative Plot Generation Framework with Small Language Models Learning to Reason for Long-Form Story Generation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T18:06:39.773455Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20ea069d-3c79-456d-8092-b44c749f5cba · inbound

NARRA-Gym for Evaluating Interactive Narrative Agents cites this paper.

NARRA-Gym for Evaluating Interactive Narrative Agents Learning to Reason for Long-Form Story Generation

Reference 50

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T02:26:15.835838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation aa2b65e9-d250-4546-b41c-92b5604b04a8 · inbound

BOOKMARKS: Efficient Active Storyline Memory for Role-playing cites this paper.

BOOKMARKS: Efficient Active Storyline Memory for Role-playing Learning to Reason for Long-Form Story Generation

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-15T04:55:02.353178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 240912b8-8ab2-45c0-a046-912c712c4777 · inbound

AI as a Tool for Simulation-Based Experiments in Literary Studies cites this paper.

AI as a Tool for Simulation-Based Experiments in Literary Studies Learning to Reason for Long-Form Story Generation

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-06-28T15:02:19.072678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d0c922e2-00f3-4cdf-b562-48aecb2ecd1c · inbound

CapRL++: Unified Reinforcement Learning with Verifiable Rewards for Dense Image and Video Captioning cites this paper.

CapRL++: Unified Reinforcement Learning with Verifiable Rewards for Dense Image and Video Captioning Learning to Reason for Long-Form Story Generation

Reference 22

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T00:17:28.998812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.