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Probabilistic Attribution For Large Language Models
利用条件概率解析大模型生成逻辑，揭示训练与推理中的分布结构。
arXiv:2605.21726v1 Announce Type: new Abstract: The generative nature of Large Language Models (LLMs) is reflected in the conditional probabilities th…
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FM-G-CAM: A Holistic Approach for Explainable AI in Computer Vision
提出FM-G-CAM新方法，全面解释CNN预测，提升可解释AI在视觉领域的实用性。
arXiv:2312.05975v3 Announce Type: replace-cross Abstract: Explainability is a vital aspect of modern AI for real-world impact and usability. The main …
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CUBE: Contrastive Understanding by Balanced Experiments
用因子实验设计解释黑盒模型，CUBE框架通过平衡探针组合评估预测器，提供清晰因果效应总结。
arXiv:2509.10825v5 Announce Type: replace-cross Abstract: Explaining a trained model requires a clear account of how explanatory evidence is generated…