Experiment · game benchmarks

Character Chess benchmark candidate audit (2026-08-22)

A benchmark candidate set is not a benchmark until the frontier ceiling validates the ruler. Retained outcome: rejected.

Outcome
rejected
Family
game benchmarks
Evidence confidence
exact
Record
model evaluation

Evidence readout

100 stable candidates produced 86 admitted positions, but the best recorded frontier screen reached only 75% exact agreement

What the attempt taught

A benchmark candidate set is not a benchmark until the frontier ceiling validates the ruler.

Why it stopped or stayed bounded

The frontier calibration did not approach the required ceiling, external-model coverage was incomplete, and the model alias was mutable, so the suite could not be frozen as a fair ruler.

Method and scope

  • Experiment family: game benchmarks.
  • Record kind: model evaluation.
  • Objective: benchmark validity.
  • Methods: evaluation.
  • Base models: codex-gpt-5.5.
  • Recorded data rows: 100.

Disposition

Closed as candidate-only evidence; design any future chess ruler independently after the learning phase.

This retained disposition is historical evidence, not authorization to restart the experiment. A new run needs a fresh question, frozen evaluator, explicit resource budget, and scoped tracking issue.

How to read this dossier

This canonical page separates the retained repository decision from the material that informed it. A source may describe an external claim, historical measurement, local observation, or planned procedure; those evidence classes are not interchangeable. Follow the provenance links before reusing a number or method.

Status and disposition describe what PostTrainLLM retained when this record was indexed. They are not a live product promise, a newly run benchmark, or permission to restart historical work. Unknown or unmeasured fields remain unknown rather than being treated as zero.

To reuse the record, first name the exact claim you need and trace it to the linked source. Then check whether the original environment, model revision, data split, evaluator, hardware, and budget match the proposed use. If they do not, treat the record as a hypothesis or design reference and run the smallest fresh comparison that can falsify it. Preserve negative outcomes and regressions beside any improvement; a local win on one slice does not silently become a general capability claim.

Source provenance

The normalized record comes from docs/attempts.json. The links below are the tracked evidence and explanatory sources preserved with the record.