AI repair agents solve bugs more reliably when reports include executable reproduction scripts, file-level localization cues, and clear structure, while longer prose reports and human-oriented steps to reproduce show no benefit or hurt.
Autocoderover: Autonomous program improvement,
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.SE 3years
2026 3representative citing papers
Refusal-ablated LLMs outperform aligned models on code-grounded localization and early executable patch generation, while aligned models retain advantages on shallow diagnostic tasks under neutral wording.
DUALVIEW is a dual-modal framework using Module Coupling, Function Call, Class Hierarchy, and Program Dependence graphs to enable persistent structural reasoning for agentic issue resolution, reporting gains on SWE-bench Pro and Verified.
citing papers explorer
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What Makes a Good Bug Report for an AI Agent?
AI repair agents solve bugs more reliably when reports include executable reproduction scripts, file-level localization cues, and clear structure, while longer prose reports and human-oriented steps to reproduce show no benefit or hurt.
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Beyond Refusal: A Same-Lineage Study of Aligned and Abliterated LLMs for Vulnerability Analysis
Refusal-ablated LLMs outperform aligned models on code-grounded localization and early executable patch generation, while aligned models retain advantages on shallow diagnostic tasks under neutral wording.
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Beyond Textual Repository Exploration: Dual-Modal Structural Reasoning for Agentic Issue Resolution
DUALVIEW is a dual-modal framework using Module Coupling, Function Call, Class Hierarchy, and Program Dependence graphs to enable persistent structural reasoning for agentic issue resolution, reporting gains on SWE-bench Pro and Verified.