3 papers
cs.SE2026
Compressing Code Context for LLM-based Issue Resolution
Haoxiang Jia, Earl T. Barr, Sergey Mechtaev
Large Language Models (LLMs) are now capable of resolving real-world GitHub issues. However, current approaches overapproximate the code context and suffer from two compounding pro…
cs.SE2025
Statistical Independence Aware Caching for LLM Workflows
Yihan Dai, Dimitrios Stamatios Bouras, Haoxiang Jia +1
Large language models (LLMs) inference is both expensive and slow. Local caching of responses offers a practical solution to reduce the cost and latency of LLM queries. In research…
cs.SE2025
Automated Repair of Ambiguous Problem Descriptions for LLM-Based Code Generation
Haoxiang Jia, Robbie Morris, He Ye +2
The growing use of large language models (LLMs) has increased the importance of natural language (NL) in software engineering. However, ambiguity of NL can harm software quality, a…