Evidence readout
the historical curve remains unqualified except at Large, where a 2026-09-02 Apple M5 Pro ABBA receipt measured `10.67x` median WebGPU-over-WASM speedup with `4.72%` maximum paired final-loss drift and zero runtime errors
What the attempt taught
Qualify performance one frozen shape and machine at a time; a retained adapter identity plus alternated paired timings can convert one point without laundering the surrounding curve.
Why it stopped or stayed bounded
The original headline made a shape-dependent result look universal, and the retained tree cannot establish that the historical curve used a real hardware adapter rather than a software fallback.
Method and scope
- Experiment family: browser product.
- Record kind: infrastructure.
Disposition
Keep Large as the measured M5 Pro point; independently reproduce Small, Medium, and XL before promoting the historical curve.
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.