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Paper Citation Record · LEDGER

GameEval: Evaluating LLMs on Conversational Games

As of 6 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2308.10032.

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

pith.paper-citation-record.v1
2308.10032 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-28T22:30:32.061732Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T22:32:43.962947Z

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 7bf281a7-86d2-4a1c-ac14-7c08207e2fba · inbound

Common-agency Games for Multi-Objective Test-Time Alignment cites this paper.

Common-agency Games for Multi-Objective Test-Time Alignment GameEval: Evaluating LLMs on Conversational Games

Reference 57

Resolution
verified exact
arxiv_id, observed 2026-05-15T06:15:06.699406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=arxiv_source observed=2026-05-15T06:14:53.685486Z digest=sha256:8dfccc7484c55c92473d48f48c7647054a18471e49bd0676c8e401e456e9b104

Observation 25e3cbf4-d3f6-4ef6-9ecd-c4db154d670a · inbound

Multi-Turn Multi-Agent Dialogue for Collaborative Reconstruction Improves VLM Performance on Spatial Reasoning, But Only Barely cites this paper.

Multi-Turn Multi-Agent Dialogue for Collaborative Reconstruction Improves VLM Performance on Spatial Reasoning, But Only Barely GameEval: Evaluating LLMs on Conversational Games

Reference 2

Resolution
metadata mismatch
arxiv_id, observed 2026-06-28T22:32:43.964469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-06-28T22:30:32.061732Z digest=sha256:2c7bf1b0a0b9163041052b1ec688a33dd2ff5b04f6bbce583c0c317c6003cca8