Evidence readout
2026-10-23 H200 check 45dde2ec9cfa4fbe passed all nine fresh correctness cases with no flags: 1.0915x aggregate, reported 95% interval 1.0751-1.1079x. The one-token router took 3.939 microseconds versus SGLang 10.352 and selected source's earlier 10.089. This is an off-board check, not a scored submission.
What the attempt taught
One whole-row FP32 reduction removes the cast launch for the one-token router and shows a substantial measured case gain. The diagnostic profile contains one 2.4-microsecond router kernel at one token and one 10.9-microsecond kernel at eight tokens. A case gain does not establish an aggregate or full-layer improvement.
Why it stopped or stayed bounded
The aggregate point estimate is 2.26 percent above selected dense 1.0673x, but reported intervals overlap and the source remains unscored. The eight-token router took 11.218 microseconds versus the selected source's earlier 10.997, so extending the reduction to eight tokens did not establish a gain. Owner policy parks this source without scoring or promotion.
Method and scope
- Experiment family: runtime perf.
- Record kind: infrastructure.
Disposition
Experiment closed after scored run beb82b4224554523 finished at rank 283/749, outside the owner top-100 target. Retain the research artifact; no further jobs.
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.