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This work introduces 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.
No single way of parallelizing attention serves large language models well under all loads. Low concurrency favors tensor parallelism, many independent requests favor data-parallel attention, and long prompts favor context parallelism. Reasoning, agentic, and RL-rollout workloads make a fixed choice untenable: a batch...
Ave, an agentic framework that uses data-flow invariants as compile-time guardrails for kernel optimization and evaluates Ave on AMD MI300X across GEMM, flash attention, and MoE, which together account for up to 90% of GPU time in LLM inference.
Large language models now write a growing share of the world's code, increasingly inside agents and serving systems that compile, execute, or dispatch generated code without line-by-line review. This works well for mainstream languages but remains brittle for low-resource programming surfaces such as domain-specific la...
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