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Center for Language and Cognition, University of Groningen
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Human-like color categories can emerge in neural agents by simply upsampling rare color terms and training with multiple listeners, closing the gap between artificial and human color lexicons.
LLMs, when combined with rule-based analysis and iterative refinement, can significantly outperform existing tools in detecting subtle smart contract vulnerabilities arising from library misuse.
LLMs can boost code clone detection accuracy by selectively arbitrating only 0.2% of uncertain cases flagged by a multimodal fusion model, achieving a 0.3% absolute Macro-F1 gain.
