Papers by Tomasz Czajkowski
PerfCoder: Large Language Models for Interpretable Code Performance Optimization (2026.findings-acl)
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Jiuding Yang, Shengyao Lu, Hongxuan Liu, Shayan Shirahmad Gale Bagi, Zahra Fazel, Tomasz Czajkowski, Di Niu
| Challenge: | Large language models (LLMs) have advanced automatic code generation, but their ability to produce high-performance code remains limited. |
| Approach: | They propose a family of large language models that generate performance-enhanced code through interpretable and customized optimization strategies. |
| Outcome: | The proposed model outperforms existing models on the PIE code performance benchmark and produces interpretable feedback that can guide larger LLMs in a planner–optimizer workflow. |