论文

结果揭示了集成组合对精度–召回权衡的直接影响：异构跨范式集成通常提升精度，而同构 LLM 集成更常取得更高的整体 F1。It is revealed that ensemble composition directly affects the precision-recall trade-off: heterogeneous cross-paradigm ensembles generally improve precision, whereas homogeneous LLM ensembles more often achieve higher overall F1-scores.

结果表明数据组成控制安全性与可用性的权衡，且安全对齐应在预期拒答边界的两侧进行评估。The results show that data composition controls the safety and usability trade-off, and that safety alignment should be evaluated on both sides of the intended refusal boundary.

SUFLECA（Scaling Up Feature LEarning for CAD-to-image Alignment），一种用于零样本 CAD 对齐的弱监督框架，含两项关键贡献，并提出一种几何一致的匹配算法以建立可靠的 CAD-图像对应。SUFLECA (Scaling Up Feature LEarning for CAD-to-image Alignment), a weakly supervised framework for zero-shot CAD alignment with two key contributions, and proposes a geometrically consistent matching algorithm that establishes reliable CAD-to-image correspondences.