# Open Kernel additional full-context attention splits | PTLLM > Doubling the full-context split target preserves correctness but does not establish a meaningful aggregate gain. Diagnostic profiles place 97.1 and 98.6… Canonical: https://posttrainllm.com/experiments/openkernel-attention-more-splits-20261024 Experiment · runtime perf Doubling the full-context split target preserves correctness but does not establish a meaningful aggregate gain. Diagnostic profiles place 97.1 and 98.6 percent of those full-context decode cases in the partial-attention kernel. The underlying hardware bottleneck remains unmeasured. Retained outcome: inconclusive. **Outcome:** inconclusive **Family:** runtime perf **Evidence confidence:** exact **Record:** infrastructure ## Evidence readout 2026-10-23 H200 check 6a1c90b00a0c46a5 passed all sixteen fresh correctness cases with no flags: 1.2968x aggregate, reported 95% interval 1.2774-1.3221x. All twelve benchmark cases beat SGLang. This is an off-board check, not a scored submission. ## What the attempt taught Doubling the full-context split target preserves correctness but does not establish a meaningful aggregate gain. Diagnostic profiles place 97.1 and 98.6 percent of those full-context decode cases in the partial-attention kernel. The underlying hardware bottleneck remains unmeasured. ## Why it stopped or stayed bounded The aggregate is only 1.28 percent above selected attention 1.2804x, and their reported intervals overlap. Full-context decode at batches 16 and 32 took 179.835 and 341.780 microseconds versus the joint-head check's earlier 140.938 and 266.767 measurements. This check does not establish a repeatable improvement over the selected source. ## 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. - [docs/learn/openkernel.md](https://posttrainllm.com/docs/learn/openkernel) - [experiments/openkernel/results.json](https://github.com/PostTrainLLM/posttrainllm/blob/main/experiments/openkernel/results.json) ## Related evidence - [Primary proof](https://posttrainllm.com/docs/learn/openkernel) - [Applicable recipe](https://posttrainllm.com/recipes) - [Learning path](https://posttrainllm.com/learn/paths/runtime-and-agents) Canonical record: `experiment:openkernel-attention-more-splits-20261024`