arXiv subjects

Chunhao Liao

Publications and source records attributed to Chunhao Liao.

3 recordsLinked to original sources

arXiv2026

Semantic-aware and Self-improving Program Reduction via Agentic Large Language Models

Reducing bug-triggering programs to their minimal essential form is a fundamental task in debugging language processors such as compilers and interpreters. Existing reduction techniques are limited by their reliance on predefined, syntax-driven transformations that lack semantic understanding of the target program, and by their inability to learn from past reduction experiences. We present a new approach that recasts program reduction as an autonomous reasoning task powered by agentic Large Language Models (LLMs). Instead of applying fixed transformation rules, our method enables an LLM to analyze program semantics, formulate reduction hypotheses, and iteratively refine its approach based on execution outcomes. Successful reduction experiences are further distilled into reusable strategies, allowing the system to continuously improve over time. We realize this approach in PROJ, a framework built around two collaborative components: a reducer agent that performs semantic-aware, case-specific program reduction, and a reflector agent that extracts and accumulates transferable reduction knowledge. Extensive experiments on 90 benchmarks spanning three programming languages show that PROJ consistently produces smaller reduced programs than all existing state-of-the-art reducers while maintaining high efficiency.

cs.SE↗

arXiv2026

Can Coding Agents Implement Missed Compiler Optimizations? Evaluating LLM Agents on LLVM Peephole Optimizations

Coding agents built on large language models are now capable of patching sizable real-world codebases, yet whether they can develop compiler optimizations remains an open question. To study this question, we introduce PeepholeBench, an evaluation framework whose tasks are constructed from real-world missed peephole optimizations reported against LLVM's InstCombine pass. Since missed peephole optimizations are typically fixed with small, localized patches, they offer a well-scoped but demanding testbed for coding agents: a correct fix demands rigorous reasoning about program semantics along with familiarity with optimizer-specific conventions. PeepholeBench derives its tasks from 21 resolved LLVM issues and 19 merged pull requests (PRs), supplies agents with only the issue context that existed before each fix, and assesses the resulting patches for both correctness and profitability. With PeepholeBench, we benchmark state-of-the-art coding agents on fixing missed peephole optimizations in LLVM's InstCombine pass, measuring their patches against the corresponding human-written fixes. We observe a tension between correctness and profitability, and no agent matches human developers on both dimensions at once. The dominant failure modes are overly narrow transformations and misuse of LLVM-specific mechanisms, errors that existing test suites rarely expose. Together, these results establish PeepholeBench as a realistic and challenging benchmark for coding agents, and suggest future directions for building agents that can more dependably assist compiler optimization development.

cs.SE↗

arXiv2026

Leveraging Large Language Models for Generalizing Peephole Optimizations

Peephole optimizations are a core component of modern optimizing compilers. It rewrites specific instruction into semantically equivalent but more efficient forms. In practice, creating a new peephole optimization often starts from a concrete optimization instance and requires lifting it into a more general rewrite rule that matches a wider range of instruction patterns. This generalization step is critical to optimization effectiveness, but it is also difficult: producing rules that are both correct and sufficiently general typically demands substantial manual effort and domain expertise. Existing approaches such as Hydra attempt to automate this task with program synthesis, but their generalization capability is often limited by search-space explosion, under-generalization, and restricted support for diverse instruction domains. We present LPG, large language model aided peephole optimization generalization, a framework that uses large language models (LLMs) to generalize peephole optimizations. The design of LPG is motivated by the observation that LLMs are effective at semantic abstraction and exploratory reasoning, while formal analyses are necessary to ensure that generated rules are sound and profitable. Based on this observation, LPG adopts a closed-loop workflow that integrates LLM-driven symbolic constant generalization, structural generalization, constraint relaxation, and bitwidth/precision generalization with feedback from syntactic validation, semantic verification, and profitability checking. We evaluate LPG on real-world peephole optimization issues drawn from the LLVM ecosystem. Overall, LPG successfully generalizes 90 out of 102 optimizations. On the integer-focused subset that is directly comparable to Hydra, LPG generalizes 74 out of 81 optimizations, whereas Hydra generalizes 35.

cs.PL↗