Lulu He | Searcharxiv

arXiv2026

CoRaCommit: A VS Code Extension for Commit Message Generation with Exemplar Retrieval

Commit messages are essential textual artifacts that describe the intent behind code changes, and play a critical role in version control, code review, and historical tracking. However, in practice, commit messages are primarily authored manually, which is time-consuming and often results in inconsistent quality and non-uniform expression. Existing VS Code extensions for commit message generation typically directly invoke large language models based on the code diff, without leveraging similar commit exemplars as references, and rarely support user feedback-driven LLM recommendation. To address these limitations, this paper presents CoRaCommit, a VS Code extension that enhances commit message generation by retrieving similar commit exemplars as prompt context, invoking multiple LLMs in parallel for candidate commit message comparison, and dynamically recommending LLMs based on user feedback. Experimental results on 945 commits from the ApacheCM dataset show that CoRaCommit outperforms existing VS Code extensions across BLEU, CIDEr, METEOR, and ROUGE-L metrics, demonstrating the effectiveness of retrieval-augmented context for commit message generation.

cs.SE↗

arXiv2026

OCR-Agent: Agentic OCR with Capability and Memory Reflection

Large Vision-Language Models (VLMs) have demonstrated significant potential on complex visual understanding tasks through iterative optimization methods.However, these models generally lack effective self-correction mechanisms, making it difficult for them to independently rectify cognitive biases. Consequently, during multi-turn revisions, they often fall into repetitive and ineffective attempts, failing to achieve stable improvements in answer quality.To address this issue, we propose a novel iterative self-correction framework that endows models with two key capabilities: Capability Reflection and Memory Reflection. This framework guides the model to first diagnose errors and generate a correction plan via Capability Reflection, then leverage Memory Reflection to review past attempts to avoid repetition and explore new solutions, and finally, optimize the answer through rigorous re-reasoning. Experiments on the challenging OCRBench v2 benchmark show that OCR-Agent outperforms the current open-source SOTA model InternVL3-8B by +2.0 on English and +1.2 on Chinese subsets, while achieving state-of-the-art results in Visual Understanding (79.9) and Reasoning (66.5) - surpassing even larger fine-tuned models. Our method demonstrates that structured, self-aware reflection can significantly enhance VLMs' reasoning robustness without additional training. Code: https://github.com/AIGeeksGroup/OCR-Agent.

cs.CV↗