works on

From the 1 of 8 linked papers with an AI index.

collaborators

8 papers

cs.PL2026

Can Large Language Models Recover Semantic Optimization Opportunities That Compilers Miss?

Hailong Jiang, Feng Yu, Emran Hossain +4

Optimizing compilers miss profitable transformations when their enabling semantics are absent from the analyzed program representation. We ask whether large language models (LLMs)…

cs.PL2026

BMOA: Baseline-Mechanism-Outcome Attribution for Compiler-Induced Numerical Deviations

Hailong Jiang, Emran Hossain, Feng Yu +4

The paper presents BMOA, a diagnostic framework that separates baseline, compiler mechanism, and outcome to attribute observed floating-point differences to compiler behavior and n…

cs.PL2026

Decode-Time Grammars: Constrained LLM Generation over a Refinement Order of Grammar Fragments

Shuoming Zhang, Ruiyuan Xu, Haofeng Li +7

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-b…

cs.DC2026

Tessera: Unlocking Heterogeneous GPUs through Kernel-Granularity Disaggregation

Tiancheng Hu, Jin Qin, Zheng Wang +10

Disaggregation maps parts of an AI workload to different types of GPUs, offering a path to utilize modern heterogeneous GPU clusters. However, existing solutions operate at a coars…

cs.CR2026

When Grammar Guides the Attack: Uncovering Control-Plane Vulnerabilities in LLMs with Structured Output

Shuoming Zhang, Jiacheng Zhao, Hanyuan Dong +9

Content Warning: This paper may contain unsafe or harmful content generated by LLMs that may be offensive to readers. Large Language Models (LLMs) increasingly serve as tooling pla…

cs.PL2026

The New Compiler Stack: A Survey on the Synergy of LLMs and Compilers

Shuoming Zhang, Jiacheng Zhao, Qiuchu Yu +4

This survey has provided a systematic overview of the emerging field of LLM-enabled compilation by addressing several key research questions. We first answered how LLMs are being i…