14 papers
Route-Align-Verify for Functional Correctness in Code Generation
Erxue Zhou, Jingxiang Meng, Aofan Liu
Large language models (LLMs) have substantially improved code generation, yet achieving strong functional correctness remains difficult, especially for heterogeneous programming ta…
CURE: Local Uncertainty Repair for Block-Parallel Speculative Decoding
Aofan Liu, Jingxiang Meng, Fangxin Liu +1
Speculative decoding mitigates the latency of sequential generation in autoregressive Large Language Models (LLMs) by interleaving draft generation with target verification. Howeve…
TaPR: Test-Aware Policy Refinement for Feedback-Conditioned Code Generation
Aofan Liu, Jingxiang Meng, Fangxin Liu +1
Multi-turn code agents rely on execution feedback to repair incorrect programs, yet standard reinforcement learning paradigms optimize and evaluate policy performance primarily usi…
Self-Correction as Feedback Control: Error Dynamics, Stability Thresholds, and Prompt Interventions in LLMs
Aofan Liu, Jingxiang Meng
Iterative self-correction is increasingly deployed in agentic LLM systems, yet whether repeated refinement improves or degrades performance remains inconsistent across models. We r…
TestExplora: Benchmarking LLMs for Proactive Bug Discovery via Repository-Level Test Generation
Steven Liu, Jane Luo, Xin Zhang +7
Given that Large Language Models (LLMs) are increasingly applied to automate software development, comprehensive software assurance spans three distinct goals: regression preventio…
GraphLocator: Graph-guided Causal Reasoning for Issue Localization
Wei Liu, Chao Peng, Pengfei Gao +4
The issue localization task aims to identify the locations in a software repository that requires modification given a natural language issue description. This task is fundamental…