arXiv AI By Weiwei Yang

PatchWrite: One Line, Not One Section -- Compile-Gated, Validity-Preserving Editing for AI-Drafted Manuscripts

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arXiv AI
Sep 2

REVISE: Validity-Guided Recovery for Online Revisions in Agent Workflows

The paper introduces “Revise”, a runtime system that performs validity-guided, fine-grained recovery for online revisions in structured agent workflows. When a revision arrives, Revise intersects the change with recorded data and control dependencies, propagates the impact through the partially executed DAG, stops invalid work, preserves unaffected progress, and recomputes only the affected region. Experiments on real coding‑agent traces and LangGraph/LLMCompiler applications show that Revise matches a latest‑version oracle, reduces model calls by up to 56%, and improves service‑level objective goodput under load.

By Ruoling Qi, Xuaner Wu, Penghang Liu, Jian Chen, Yirui Liu
arXiv AI
Sep 7

Better Understanding, Better Fixes? A Study of Hallucination in LLM-based Automated Program Repair

The paper investigates hallucination in large language model–based automated program repair (APR). It defines hallucination as producing patches or intermediate artifacts that are not grounded in available repair evidence, and analyzes it across final patches and intermediate tasks such as triggering test case identification, line coverage prediction, and additional test case generation. Experiments on 832 Defects4J bugs show that only 21.0%–55.9% of patches pass the developer test suite, with 72.7% of sampled repairs exhibiting hallucinations, often due to incorrect causal localization or repair strategies.

By Xuemeng Cai, Jiakun Liu, Linhan Yang, Wei Ma, Lingxiao Jiang
arXiv AI
Oct 9

Can LLMs Fix It Without Code? Toward Automated Verification of No-Code Bug Fixes

The paper investigates whether large language models (LLMs) can automatically verify no-code bug fixes—solutions that instruct users to change settings, upgrade versions, or alter workflows—by executing them in a real browser environment. Using 322 fixes from a benchmark, the study tests three executor agents (OpenCUA‑72B, Claude Sonnet 5, and Meta’s Muse Glimmer) and finds that only 17.6% of issues could be set up and passed sanity checks, with resolution rates ranging from 14.6% to 49.7% depending on the executor. Even the best configuration resolved fewer than half of the fixes, and executor choice significantly affected outcomes, highlighting the need for execution-based verification before such fixes reach users.

By Utku Boran Torun, Veli Karakaya, Eray T\"uz\"un