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Paper Citation Record · LEDGER

Large Language Models and Games: A Survey and Roadmap

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2402.18659.

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

pith.paper-citation-record.v1
2402.18659 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:55:17.509466Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T07:43:14.179799Z

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 f87a756e-2a08-45f8-ac4b-56666f83b927 · inbound

Cracking Aegis: An Adversarial LLM-based Game for Raising Awareness of Vulnerabilities in Privacy Protection cites this paper.

Cracking Aegis: An Adversarial LLM-based Game for Raising Awareness of Vulnerabilities in Privacy Protection Large Language Models and Games: A Survey and Roadmap

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-07T14:55:17.509466Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:55:17.509466Z digest=sha256:83fc4e898570fd99c3e3d103064d2250b7d71fece9cf11543ea0620acda0b241

Observation dd569d29-a017-456d-835d-3548d7dfe999 · inbound

ScriptDoctor: Automatic Generation of PuzzleScript Games via Large Language Models and Tree Search cites this paper.

ScriptDoctor: Automatic Generation of PuzzleScript Games via Large Language Models and Tree Search Large Language Models and Games: A Survey and Roadmap

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-07T05:57:57.484809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:57:57.484809Z digest=sha256:b891c31282b2599677fa662ff9f81ba1c2aae7c281f8742ee3ff77e313a71c87

Observation 565b9286-a440-4889-b495-556a44434f81 · inbound

DipLLM: Fine-Tuning LLM for Strategic Decision-making in Diplomacy cites this paper.

DipLLM: Fine-Tuning LLM for Strategic Decision-making in Diplomacy Large Language Models and Games: A Survey and Roadmap

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-07T04:53:22.369881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:53:22.369881Z digest=sha256:b963de581cf7316b7775cf0621790227c167fcce13d4cb4babb84682991da645

Observation 5adb59ca-68e8-466a-83c2-bba8cd927a67 · inbound

Game Master LLM: Task-Based Role-Playing for Natural Slang Learning cites this paper.

Game Master LLM: Task-Based Role-Playing for Natural Slang Learning Large Language Models and Games: A Survey and Roadmap

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-17T20:45:14.278198Z

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.

source=pdf_text observed=2026-05-17T20:43:01.322253Z digest=sha256:a933cfd04faa8d736c53e8b98a57b862f6887708ba97519ab89a5de6a947fd87

Observation d876eb05-9947-4d5c-9c0c-ee36a4f7a967 · inbound

Improving Collaborative Storytelling with a Multi-Agent Framework Based on Large Language Models cites this paper.

Improving Collaborative Storytelling with a Multi-Agent Framework Based on Large Language Models Large Language Models and Games: A Survey and Roadmap

Reference 9

Resolution
metadata mismatch
arxiv_id, observed 2026-06-29T07:43:14.181420Z

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.

source=pdf_text observed=2026-06-29T07:36:19.786202Z digest=sha256:cd4acca930dbcfe1bfe9be5935b7372eecf295c5c7f44de9b5ecf4d321a52478