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Paper Citation Record · LEDGER

Surprising Negative Results for Generative Adversarial Tree Search

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1806.05780.

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

pith.paper-citation-record.v1
1806.05780 v4

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 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:44:36.584243Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T23:57:02.724613Z

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 f67b3ed9-2b9a-4e02-8ee7-3ffa90927141 · inbound

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model cites this paper.

Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Surprising Negative Results for Generative Adversarial Tree Search

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-16T23:57:02.726748Z

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=2026-05-16T23:57:02.653534Z digest=sha256:e2f94ff17b3cdc2f6bc955d3a38e1bc38b3615ad37e8751d6e68cbdadd54e8f9

Observation 2e81c948-05bc-48b6-a9c7-f80e217370dd · inbound

Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models cites this paper.

Is RL fine-tuning harder than regression? A PDE learning approach for diffusion models Surprising Negative Results for Generative Adversarial Tree Search

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-15T16:44:36.584243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T16:44:36.584243Z digest=sha256:4ebaa09897988ca20f6475a8e703cedddc23de7befc4a492532a9187b03f917d