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Paper Citation Record · LEDGER

ChessGPT: Bridging Policy Learning and Language Modeling

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2306.09200.

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

pith.paper-citation-record.v1
2306.09200 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 2026-08-16T06:30:59.297886+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:26:44.996629Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T09:59:45.735688Z

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 340b0613-f11d-4dde-bd22-1ef2c30e3f63 · inbound

Natural Language Reinforcement Learning cites this paper.

Natural Language Reinforcement Learning ChessGPT: Bridging Policy Learning and Language Modeling

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-12T15:26:44.996629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:26:44.996629Z digest=sha256:563559791c4d1b8970583b9176873b8833bed10c9e21bf9718ea6ebea3cae618

Observation f2a58e12-4387-41a8-b641-cc757c055f81 · inbound

Complete Chess Games Enable LLM Become A Chess Master cites this paper.

Complete Chess Games Enable LLM Become A Chess Master ChessGPT: Bridging Policy Learning and Language Modeling

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-10T14:21:32.830254Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T14:21:32.830254Z digest=sha256:2021c97e5c318a6d4f93f26c14e13518030f8c0357d63a6e9f2ddb2a9151a16d

Observation ddaf116f-56cf-4520-bfc5-fdeaf7cca175 · inbound

Tracking World States with Language Models: State-Based Evaluation Using Chess cites this paper.

Tracking World States with Language Models: State-Based Evaluation Using Chess ChessGPT: Bridging Policy Learning and Language Modeling

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-05T15:30:09.485102Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T15:30:09.485102Z digest=sha256:d0c1307d3f3d183f56d585ce45673fefd463c20a4d6317b1b71e3f58b4749299

Observation 9d4c804a-a0f2-4b71-a7d1-780211abc8ec · inbound

Outbidding and Outbluffing Elite Humans: Mastering Liar's Poker via Self-Play and Reinforcement Learning cites this paper.

Outbidding and Outbluffing Elite Humans: Mastering Liar's Poker via Self-Play and Reinforcement Learning ChessGPT: Bridging Policy Learning and Language Modeling

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-05-18T01:00:34.353190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-18T00:58:51.759880Z digest=sha256:a0559a40b64a5606552dd5f8daf63c2f4e3141e9573539d896ad0b9d96c5d60c

Observation 023441d6-c950-4a57-bec6-862e84320b58 · inbound

MIMIC-Py: An Extensible Tool for Personality-Driven Automated Game Testing with Large Language Models cites this paper.

MIMIC-Py: An Extensible Tool for Personality-Driven Automated Game Testing with Large Language Models ChessGPT: Bridging Policy Learning and Language Modeling

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-11T00:45:50.520220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-10T18:19:10.686438Z digest=sha256:4b8d9d582f15d032cbad5ef561b67884a902938c0972dbedeeb4b9e99b735cf6

Observation 47fa77c7-473a-4ddf-8c88-a443b6c548f5 · inbound

Distilling Game Code World Model Generation into Lightweight Large Language Models cites this paper.

Distilling Game Code World Model Generation into Lightweight Large Language Models ChessGPT: Bridging Policy Learning and Language Modeling

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-06-30T14:04:44.604828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-06-30T13:58:37.956333Z digest=sha256:f720a871cc729c5dd5da9b246742e2824e495a81f450adca8a595dab008fd1b7

Observation f496f7f0-2cc3-4ffa-9bf9-f0ff74d84d47 · inbound

On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners cites this paper.

On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners ChessGPT: Bridging Policy Learning and Language Modeling

Reference 5

Resolution
metadata mismatch
arxiv_id, observed 2026-07-04T09:59:45.737230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-06-26T09:13:55.624609Z digest=sha256:84ada776773348dc306910d77be526735a013060718c3b567b3f149afb6af498