arXiv Machine Learning June 3, 2026 By Mahdi Erfanian, Nelson Daniel Troncoso, Aashna Garg, Amabel Gale, Xiaoyu Liu, Pareesa Ameneh Golnari, Shengyu Fu

Synthetic Hallucinations, Real Gains: Hard Negatives from Frontier Models for FIM Hallucination Mitigation

Read the original on arXiv Machine Learning →

arXiv:2606. 03130v1 Announce Type: new Abstract: Small open-source code models that power IDE autocomplete still emit hallucinated Fill-in-the-Middle (FIM) completions: syntactically natural calls to methods, parameters, variables, and imports that do not exist in the surrounding project.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

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CodeAlchemy: Synthetic Code Rewriting at Scale

arXiv:2606. 10087v1 Announce Type: cross Abstract: Pre-training on raw code teaches syntax but provides sparse signal for diverse real-world task formats.

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arXiv Machine Learning

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Spaghetti Architect: A Contamination-Resistant, By-Construction-Labelled, Multi-Language Code Dataset Generator

arXiv:2607. 18642v1 Announce Type: new Abstract: Mined code corpora are abundant but uncontrolled: a snippet's semantics, surface "messiness," and difficulty are whatever the wild contained; there is no known-optimal reference to grade against; and any public sample may already sit in a model's training set.

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arXiv Machine Learning

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MetaLint: Easy-to-Hard Generalization for Code Linting

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arXiv AI

Jul 22

SciCodePile: A 128GB Corpus and Executable Benchmark for Challenging Scientific Code Generation

arXiv:2607. 19104v1 Announce Type: cross Abstract: Large language models (LLMs) excel at general-purpose code generation, yet how well they handle scientific code remains an open question.

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arXiv AI

Aug 10

Beyond Text Matching: Towards Reference-Free Evaluation for Human-Oriented Binary Reverse Engineering

arXiv:2608. 07038v1 Announce Type: cross Abstract: Human-Oriented Binary Reverse Engineering (HOBRE) aims to transform decompiled pseudocode into a more human-friendly representation, thereby reducing the cognitive burden of reverse analysis and improving efficiency.

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arXiv AI

Jun 12

HalluJudge: A Reference-Free Hallucination Detection for Context Misalignment in Code Review Automation

arXiv:2601. 19072v3 Announce Type: replace-cross Abstract: Large Language models (LLMs) have shown strong capabilities in code review automation, such as review comment generation, yet they suffer from hallucinations -- where the generated review comments are ungrounded in the actual code -- poses a significant challenge to the adoption of LLMs in code review workflows.

By Kla Tantithamthavorn, Hong Yi Lin, Patanamon Thongtanunam, Wachiraphan Charoenwet, Minwoo Jeong, Ming Wu

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Related stories

CodeAlchemy: Synthetic Code Rewriting at Scale

Spaghetti Architect: A Contamination-Resistant, By-Construction-Labelled, Multi-Language Code Dataset Generator

MetaLint: Easy-to-Hard Generalization for Code Linting

SciCodePile: A 128GB Corpus and Executable Benchmark for Challenging Scientific Code Generation

Beyond Text Matching: Towards Reference-Free Evaluation for Human-Oriented Binary Reverse Engineering

HalluJudge: A Reference-Free Hallucination Detection for Context Misalignment in Code Review Automation