Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
PokerGPT: An End-to-End Lightweight Solver for Multi-Player Texas Hold'em via Large Language Model
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2401.06781.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T22:50:32.405072Z
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
5
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 5f9a5e21-3fe6-428c-8217-99f75b4a01bf · inbound
Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontiers cites this paper.
Game Theory Meets Large Language Models: A Systematic Survey with Taxonomy and New Frontiers PokerGPT: An End-to-End Lightweight Solver for Multi-Player Texas Hold'em via Large Language Model
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T22:50:32.405072Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 74aaef0d-59ed-44bf-916e-c9e549841167 · inbound
Foresight Optimization for Strategic Reasoning in Large Language Models cites this paper.
Foresight Optimization for Strategic Reasoning in Large Language Models PokerGPT: An End-to-End Lightweight Solver for Multi-Player Texas Hold'em via Large Language Model
Reference 4
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-10T13:45:27.775160Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 1cdebcbd-506c-4993-bb52-0f8dd1a9b0ca · inbound
AlphaExploitem: Going Beyond the Nash Equilibrium in Poker by Learning to Exploit Suboptimal Play cites this paper.
AlphaExploitem: Going Beyond the Nash Equilibrium in Poker by Learning to Exploit Suboptimal Play PokerGPT: An End-to-End Lightweight Solver for Multi-Player Texas Hold'em via Large Language Model
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T05:56:34.870471Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation c4ff676d-6344-45c7-b8d7-9f68a634f8bb · inbound
GENSTRAT: Toward a Science of Strategic Reasoning in Large Language Models cites this paper.
GENSTRAT: Toward a Science of Strategic Reasoning in Large Language Models PokerGPT: An End-to-End Lightweight Solver for Multi-Player Texas Hold'em via Large Language Model
Reference 6
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T04:45:20.646143Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation e2660e37-5cee-484a-b785-e3c2ad38e221 · inbound
Unsupervised Causal Abstractions Discovery cites this paper.
Unsupervised Causal Abstractions Discovery PokerGPT: An End-to-End Lightweight Solver for Multi-Player Texas Hold'em via Large Language Model
Reference 33
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-26T20:49:57.389567Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.