arXiv:2604. 23989v2 Announce Type: replace-cross Abstract: Recent work on large language models (LLMs) has emphasized the importance of scaling inference compute.
arXiv:2604.23989v3 Announce Type: replace-cross Abstract: In multi-turn program correction, the state-of-the-art method Scattered Forest Search (SFS) has been proposed, employing Monte Carlo Tree Sea...
The paper introduces a dictionary-guided HDL repair system that uses ANTLR-derived mutation vocabularies and a simulation-divergence fault localization module to generate syntactically valid Verilog mutations. The mutation operator performs token substitutions, insertions, and deletions via regex matching, while the fault localization scores source lines based on proximity to diverging output wires, guiding the search. Evaluated on the CirFix benchmark, the approach achieves correct repairs on 14 bug variants, including a multi-bug case, and outperforms CirFix with an 18x speedup on a two-edit benchmark.
arXiv:2607. 12605v1 Announce Type: cross Abstract: Large language models (LLMs) have improved automated program repair (APR), but two limitations remain.
arXiv:2608. 14065v1 Announce Type: cross Abstract: Background: Software bugs remain a critical challenge in development, necessitating effective Automated Program Repair (APR) techniques.
arXiv:2507. 22580v2 Announce Type: replace-cross Abstract: Automated Program Repair (APR) seeks to automatically correct software bugs without requiring human intervention.
ReMCTS is a Monte Carlo Tree Search–style framework that enhances code generation by large language models (LLMs). It organizes program candidates as tree states, retains branch‑local debugging context, and retrieves failure experience across branches, distinguishing failed checks from unavailable evidence. Experiments on HumanEval and MBPP‑Sanitized show that visible‑test ReMCTS improves performance over direct generation in 8 of 10 model‑dataset pairs, while a pilot on HumanEval‑X C++ demonstrates compatibility with compiler‑backed execution.
arXiv:2610.15894v1 Announce Type: cross Abstract: Large language model (LLM) code-repair systems fix faulty programs by repeatedly generating and testing revisions. Each revision is costly, making th...
arXiv:2505. 07372v3 Announce Type: replace-cross Abstract: This paper presents a novel methodology for enhancing Automated Program Repair (APR) through synthetic data generation utilizing Large Language Models (LLMs).
arXiv:2608. 15145v1 Announce Type: new Abstract: Large Language Models (LLMs) have been increasingly adopted in Text-to-SQL systems, yet SQL errors remain a major obstacle in real-world Text-to-SQL inference pipelines.
arXiv:2607. 18724v1 Announce Type: new Abstract: Text-to-image (T2I) generators often fail to follow their prompts faithfully, producing wrong counts, swapped attributes, ambiguous relations, and illegible text.
arXiv:2607. 22761v1 Announce Type: cross Abstract: Resolving Design Rule Violations (DRVs) in layouts entails an iterative loop of geometric edits and verification.
arXiv:2610.15708v1 Announce Type: new Abstract: Coding agents based on frozen language models can improve their performance by refining the external context they rely on, without updating model weigh...