arXiv:2605.28213v1 Announce Type: new Abstract: LLM-based agents are increasingly used to generate GPU kernels, but they often know what optimizations to try without knowing when those optimizations are sound. We introduce KLineage, which learns this missing "when" knowledge from expert kernels: instead of relying on forward rollouts, KLineage walks expert implementations backward through validation-gated simplifications and reverses each accepted step into a reusable optimization skill. Each skill records not only the optimization intent, but also where it applies in code, what conditions mad
The increasing reliance on LLMs for code generation, particularly for performance-critical GPU kernels, is highlighting the need for more intelligent optimization strategies beyond brute-force rollout evaluations.
This research addresses a critical limitation in AI-driven code generation, moving towards more efficient and reliable autonomous optimization for high-performance computing, which underpins much of the AI stack.
AI agents will become more adept at generating not just functional but also highly optimized code by learning contextual application of optimizations, rather than just knowing what optimizations exist.
- · AI agents
- · GPU manufacturers
- · High-performance computing (HPC) sector
- · Software developers
- · Manual GPU kernel optimizers
- · Inefficient AI code generation models
LLMs generating GPU kernels will produce significantly more performant and robust code.
This improved code generation will accelerate AI model training and inference, potentially lowering compute costs and accelerating AI development.
The methodology could be extended to other complex optimization problems, enabling AI to learn 'when' to apply interventions across various engineering and scientific domains.
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Read at arXiv cs.AI