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DeepDiscovery boosts task-relevant file recovery by up to 9.2 percentage points, transforming how coding agents navigate complex industrial codebases.
Natural backdoor vulnerabilities are not just a theoretical concern; they are prevalent in CodeLMs and can significantly compromise code security.
MAAD not only automates architecture design but also enhances the quality of outputs through a collaborative agent framework and advanced LLM integration.
Code dataset watermarking gets a stealthy upgrade: PuzzleMark hides watermarks in variable names based on code complexity, making them nearly undetectable while guaranteeing perfect verification.
NPM malware detection tools often fail because they struggle to distinguish between innocuous code behavior and malicious intent, a problem addressable by analyzing behavioral chains.
Graph-based code representations, largely unexplored in automated patch correctness assessment, crush sequence- and heuristic-based methods, achieving 82.6% accuracy in predicting patch correctness.
