Paper Citation Record · LEDGER

PokerBench: Training Large Language Models to become Professional Poker Players

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2501.08328.

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

pith.paper-citation-record.v1
2501.08328 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:42:59.016494Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T06:47:26.963289Z

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 4b5535ce-44fe-40b9-899e-abf9e06914ce · inbound

Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Review cites this paper.

Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Review PokerBench: Training Large Language Models to become Professional Poker Players

Reference 160

Resolution: unresolved; no resolver link, observed 2026-08-06T17:42:59.016494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f49b6f6-5ae9-4788-8807-a56fc5849d5e · inbound

Think in Games: Learning to Reason in Games via Reinforcement Learning with Large Language Models cites this paper.

Think in Games: Learning to Reason in Games via Reinforcement Learning with Large Language Models PokerBench: Training Large Language Models to become Professional Poker Players

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-05T14:23:27.591640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72c368dc-84b8-4a7a-ac84-c65345189ef4 · inbound

Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse cites this paper.

Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse PokerBench: Training Large Language Models to become Professional Poker Players

Reference 227

Resolution: verified exact; arxiv_id, observed 2026-05-12T03:26:19.145489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bea6f925-331c-456f-86b6-3819f4cd963d · inbound

Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse cites this paper.

Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse PokerBench: Training Large Language Models to become Professional Poker Players

Reference 227

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:47:26.967716Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.