Towards Cold-Start Drafting and Continual Refining: A Value-Driven Memory Approach with Application to NPU Kernel Synthesis

Deploying Large Language Models to data-scarce programming domains poses significant challenges, particularly for kernel synthesis on emerging Domain-Specific Architectures where a "Data Wall" limits available training data. While models excel on data-rich platforms like CUDA, they suffer catastrophic performance drops on data-scarce ecosystems such as NPU programming. To overcome this cold-start barrier without expensive fine-tuning, we introduce EvoKernel, a self-evolving agentic framework that automates the lifecycle of kernel synthesis from initial drafting to continual refining. EvoKernel addresses this by formulating the synthesis process as a memory-based reinforcement learning task. Through a novel value-driven retrieval mechanism, it learns stage-specific Q-values that prioritize experiences based on their contribution to the current objective-whether bootstrapping a feasible draft or iteratively refining latency. Furthermore, by enabling cross-task memory sharing, the agent generalizes insights from simple to complex operators. By building an NPU variant of KernelBench and evaluating on it, EvoKernel improves frontier models' correctness from 11.0% to 83.0% and achieves a median speedup of 3.60× over initial drafts through iterative refinement. This demonstrates that value-guided experience accumulation allows general-purpose models to master the kernel synthesis task on niche hardware ecosystems. Our official page is available at https://evokernel.zhuo.li . INTRODUCTION A practical limitation when deploying Large Language Models (LLMs) to niche domains is their inability to generalize beyond their pre-training distribution (Minaee et al., 2024; Wang et al., 2025) . When faced with cold-start scenarios, domains where training data is sparse and expert demonstrations are unavailable, even frontier models struggle significantly (Kostikova et al., 2025; Joel et al., 2024) . This challenge is particularly acute in domains where (i) correctness is binary and machine-verifiable, leaving little room for "partially correct" solutions (Jain et al., 2024; Yan et al., 2024) , (ii) expert knowledge is scarce and expensive to acquire, and (iii) the gap between in-distribution and out-of-distribution performance is stark. Automated kernel synthesis for emerging hardware accelerators exemplifies this extreme scarcity (Yu et al., 2026) . While the industry is aggressively diversifying toward Domain-Specific Architectures (DSAs) like NPUs, TPUs, and neuromorphic chips (Silvano et al., 2025; Liao et al., 2021; Jouppi et al., 2023) to address escalating computational costs (Kaplan et al., 2020) , these nascent ecosystems face a severe "Data Wall". Unlike the mature NVIDIA landscape, where decades of CUDA repositories provide a massive pre-training corpus, emerging platforms are characterized by extreme data scarcity: public code is rare, documentation is esoteric, and compiler * Equal contribution.