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Paper Citation Record · LEDGER

Can Large Language Models Play Text Games Well? Current State-of-the-Art and Open Questions

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2304.02868.

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

pith.paper-citation-record.v1
2304.02868 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:31:30.811659Z

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

9
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 45ef27d1-5064-4dff-98ae-44004bbc8bbd · inbound

Scaling Laws for State Dynamics in Large Language Models cites this paper.

Scaling Laws for State Dynamics in Large Language Models Can Large Language Models Play Text Games Well? Current State-of-the-Art and Open Questions

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-07T15:31:30.811659Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:31:30.811659Z digest=sha256:ba4e9d8662fe98671087b1144c4fe7344dad7f20370b812bce778c1992988beb

Observation d3d40880-4245-4e72-8577-0a94be536f50 · inbound

VideoGameBench: Can Vision-Language Models complete popular video games? cites this paper.

VideoGameBench: Can Vision-Language Models complete popular video games? Can Large Language Models Play Text Games Well? Current State-of-the-Art and Open Questions

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-19T12:47:17.777725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-19T12:45:58.202759Z digest=sha256:e1195f5841001ab455c9807819f13d38315f4204d8e654f457181cbeb959de4f

Observation ac80acf6-b176-4898-8172-ee4f48f990d3 · inbound

Orak: A Foundational Benchmark for Training and Evaluating LLM Agents on Diverse Video Games cites this paper.

Orak: A Foundational Benchmark for Training and Evaluating LLM Agents on Diverse Video Games Can Large Language Models Play Text Games Well? Current State-of-the-Art and Open Questions

Reference 14

Resolution
metadata mismatch
arxiv_id, observed 2026-05-19T12:02:16.583847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-19T12:01:42.681135Z digest=sha256:32c934a9689b0f0d93a1918e67b3ce32e25d5ae6edfc99067278b6663e490883

Observation de3202ed-187d-423b-95af-579c94c86b8b · inbound

Tracing LLM Reasoning Processes with Strategic Games: A Framework for Planning, Revision, and Resource-Constrained Decision Making cites this paper.

Tracing LLM Reasoning Processes with Strategic Games: A Framework for Planning, Revision, and Resource-Constrained Decision Making Can Large Language Models Play Text Games Well? Current State-of-the-Art and Open Questions

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-07T01:06:47.184068Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T01:06:47.184068Z digest=sha256:2aed650bd934fa907f7e52ddf839cb44864c3cf0f94046ead3a52be43b33f794

Observation fb1a3b94-ddc4-4c99-a97f-d39e229c5233 · inbound

Use of a genetic algorithm to find solutions to introductory physics problems cites this paper.

Use of a genetic algorithm to find solutions to introductory physics problems Can Large Language Models Play Text Games Well? Current State-of-the-Art and Open Questions

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-05T23:43:32.223150Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T23:43:32.223150Z digest=sha256:7c55d98752e40e32167796b7d3291f69960e0173fb642e14d5f40f4742b94dc1

Observation ee8d45ee-475c-44b7-b71d-2e2df4eb0191 · inbound

High-quality generation of dynamic game content via small language models: A proof of concept cites this paper.

High-quality generation of dynamic game content via small language models: A proof of concept Can Large Language Models Play Text Games Well? Current State-of-the-Art and Open Questions

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-05-21T14:34:12.766460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-21T14:32:58.163849Z digest=sha256:c14462e302f8f8ba9ed59bfb5c16c4dc3134f06ea88a4186f0cd272520a9146f

Observation 1b836264-9b90-49bc-92cc-a6b8bcfb048f · inbound

OmniGameArena: A Unified UE5 Benchmark for VLM Game Agents with Improvement Dynamics cites this paper.

OmniGameArena: A Unified UE5 Benchmark for VLM Game Agents with Improvement Dynamics Can Large Language Models Play Text Games Well? Current State-of-the-Art and Open Questions

Reference 62

Resolution
verified exact
arxiv_id, observed 2026-07-03T01:07:29.927728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-06-27T16:50:36.194650Z digest=sha256:780574ab8c73941e439063c2fd6b2ad38b417c7fb48598f55bc598a2f77a8682

Observation 29a2a912-d3b8-44d2-9699-14f1e3472bf1 · inbound

Training with (Swap) Regret Loss in a Single-Layer Self-Attention Model: A Case Study on the Probability Simplex cites this paper.

Training with (Swap) Regret Loss in a Single-Layer Self-Attention Model: A Case Study on the Probability Simplex Can Large Language Models Play Text Games Well? Current State-of-the-Art and Open Questions

Reference 251

Resolution
unresolved
no resolver link, observed 2026-07-31T23:52:13.023014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-31T23:52:13.023014Z digest=sha256:267e4277ed41e379655d82191ffb0d22ec0e505a10edb7f92988dd1e7fa8e351