What is recorded here?
Combined language-model translation, evolutionary kernel search, model-based pre-screening and GPU profiling with a benchmark varying initialization and input settings, reporting optimized forward and backward kernels while exposing inflated gains from benchmark loopholes. Reported result; not independently verified by WRS. Ranked using a provisional grade and reference estimate for the exact claimed result.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Model-based soft verification is a fallible filter, not formal proof; hardware comparisons still test finite inputs and tolerances. Speedups depend on baseline, shape and GPU, and backward kernels remain harder. Removing contaminated tasks substantially reduces headline gains.
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People and systems
Robert Tjarko Lange; Qi Sun; Aaditya Prasad; Maxence Faldor; Yujin Tang; David Ha
Named source authors and agent execution are recorded separately; individual effort shares are unknown.
GPT and reasoning-model ensemble · OpenAI · GPT-4.1; o3; o4-mini
Source-reported kernel proposals and screening within the human-designed search; exact API snapshots not specified. Source-reported attribution; provider attestation is unavailable.
Claude · Anthropic · Claude Sonnet 3.7
Source-reported member of proposal/verification ensembles; individual kernel credit not isolated. Source-reported attribution; provider attestation is unavailable.
Gemini · Google DeepMind · Gemini 2.5 Pro
Source-reported member of model-ensemble ablation; not attributed to every kernel. Source-reported attribution; provider attestation is unavailable.
Research locations and affiliations
Location not yet established
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English catalogue narrative
Lange and colleagues with LLMs: robustly evaluated GPU-kernel search
Combined language-model translation, evolutionary kernel search, model-based pre-screening and GPU profiling with a benchmark varying initialization and input settings, reporting optimized forward and backward kernels while exposing inflated gains from benchmark loopholes. Reported result; not independently verified by WRS. Ranked using a provisional grade and reference estimate for the exact claimed result.
Scientific scope
Model-based soft verification is a fallible filter, not formal proof; hardware comparisons still test finite inputs and tolerances. Speedups depend on baseline, shape and GPU, and backward kernels remain harder. Removing contaminated tasks substantially reduces headline gains.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Robert Tjarko Lange; Qi Sun; Aaditya Prasad; Maxence Faldor; Yujin Tang; David Ha
Named source authors and agent execution are recorded separately; individual effort shares are unknown.
GPT and reasoning-model ensemble / OpenAI / GPT-4.1; o3; o4-mini: Source-reported kernel proposals and screening within the human-designed search; exact API snapshots not specified (source reported).
Claude / Anthropic / Claude Sonnet 3.7: Source-reported member of proposal/verification ensembles; individual kernel credit not isolated (source reported).
Gemini / Google DeepMind / Gemini 2.5 Pro: Source-reported member of model-ensemble ablation; not attributed to every kernel (source reported).