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PRAXIS reveals that systematically extracting and surfacing tacit knowledge can dramatically enhance LLM performance in domain-specific code generation, outperforming traditional methods.
Router-side interventions can completely compromise coding agents' actions, exposing a critical vulnerability in current software development practices.
Explanation-guided learning can significantly boost the robustness of medical NER, achieving consistent performance gains across multiple models.
IViT achieves 93.80% accuracy in skin disease detection while reducing feature redundancy by 29.5%, striking a crucial balance between interpretability and performance.
LLMs can significantly improve code generation by interleaving reasoning steps *within* the code itself, adaptively focusing on high-complexity areas.
