Selected work

Representative Papers

Interpretable but Fragile? Robustness of Concept Bottlenecks under Geometric-Semantic Perturbations

Sep 29, 2026

This study addresses the ongoing debate regarding whether Concept Bottleneck Models (CBMs) enhance robustness by constructing a generative evaluation framework that systematically compares CBMs with standard classifiers under geometric and semantic perturbations. By disentangling robustness concepts from perturbation types and integrating randomized smoothing certification with latent space sensitivity analysis, this work proposes a controlled comparison paradigm to reconcile contradictory findings in existing literature. The results demonstrate that interpretability does not inherently confer robustness; rather, it redistributes model sensitivity, indicating that the two constitute fundamentally independent optimization objectives. Ultimately, this research clarifies the trade-off mechanisms between interpretability and robustness, providing a theoretical foundation for the design of trustworthy AI systems.

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An Interpretable Memory Decision Controller for LLM Agents Based on Three-Signal Complementarity: Decoupling Confidence and Consistency

Sep 18, 2026

为了解决大语言模型记忆系统中记忆可信度决策问题，提出了一种基于三信号互补的记忆决策层（MDL），通过几何操作实现记忆的可靠性和一致性解耦，有效减少幻觉。

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Environment-Invariant Subspace Learning for Generalizable Deepfake Detection

Aug 18, 2026

为解决深伪检测中的环境干扰问题，提出环境不变子空间学习框架EISL，通过可学习低秩投影分解特征，增强模型对未知伪造类型和环境变化的鲁棒性。

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Tianmu-TC: Physics-constraints Generative Artificial Intelligence for Global Tropical Cyclone Forecasting

Aug 18, 2026

为解决热带气旋预报不确定性问题，提出基于物理约束的生成式AI框架Tianmu-TC，有效提高预报准确性和效率。

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LAD-COD: Language-Aligned Dense Perception for Camouflaged Object Detection

Aug 08, 2026

This work addresses the challenge of camouflaged object detection, where high similarity between foreground and background and weak boundary cues hinder accurate segmentation. To this end, the authors propose the LAD-COD framework, which introduces a novel Language-Aligned Dual Visual Fusion (LADVF) mechanism. This mechanism propagates language-instruction-guided semantic information to the image patch level and aligns it with low-level dense visual features, enabling synergistic optimization between semantic guidance and fine structural perception. Additionally, a trainable hierarchical visual branch is incorporated to extract camouflage-sensitive features, and a residual gating mechanism fuses multi-source information effectively. Extensive experiments demonstrate that the proposed method achieves state-of-the-art performance across all 12 dataset–metric combinations on three major benchmarks: CAMO, COD10K, and NC4K.

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Latest Papers

Interpretable but Fragile? Robustness of Concept Bottlenecks under Geometric-Semantic Perturbations

Sep 29, 2026

This study addresses the ongoing debate regarding whether Concept Bottleneck Models (CBMs) enhance robustness by constructing a generative evaluation framework that systematically compares CBMs with standard classifiers under geometric and semantic perturbations. By disentangling robustness concepts from perturbation types and integrating randomized smoothing certification with latent space sensitivity analysis, this work proposes a controlled comparison paradigm to reconcile contradictory findings in existing literature. The results demonstrate that interpretability does not inherently confer robustness; rather, it redistributes model sensitivity, indicating that the two constitute fundamentally independent optimization objectives. Ultimately, this research clarifies the trade-off mechanisms between interpretability and robustness, providing a theoretical foundation for the design of trustworthy AI systems.

0 citationsRead paper

An Interpretable Memory Decision Controller for LLM Agents Based on Three-Signal Complementarity: Decoupling Confidence and Consistency

Sep 18, 2026

为了解决大语言模型记忆系统中记忆可信度决策问题，提出了一种基于三信号互补的记忆决策层（MDL），通过几何操作实现记忆的可靠性和一致性解耦，有效减少幻觉。

0 citationsRead paper

Environment-Invariant Subspace Learning for Generalizable Deepfake Detection

Aug 18, 2026

为解决深伪检测中的环境干扰问题，提出环境不变子空间学习框架EISL，通过可学习低秩投影分解特征，增强模型对未知伪造类型和环境变化的鲁棒性。

0 citationsRead paper

Tianmu-TC: Physics-constraints Generative Artificial Intelligence for Global Tropical Cyclone Forecasting

Aug 18, 2026

为解决热带气旋预报不确定性问题，提出基于物理约束的生成式AI框架Tianmu-TC，有效提高预报准确性和效率。

0 citationsRead paper

LAD-COD: Language-Aligned Dense Perception for Camouflaged Object Detection

Aug 08, 2026

This work addresses the challenge of camouflaged object detection, where high similarity between foreground and background and weak boundary cues hinder accurate segmentation. To this end, the authors propose the LAD-COD framework, which introduces a novel Language-Aligned Dual Visual Fusion (LADVF) mechanism. This mechanism propagates language-instruction-guided semantic information to the image patch level and aligns it with low-level dense visual features, enabling synergistic optimization between semantic guidance and fine structural perception. Additionally, a trainable hierarchical visual branch is incorporated to extract camouflage-sensitive features, and a residual gating mechanism fuses multi-source information effectively. Extensive experiments demonstrate that the proposed method achieves state-of-the-art performance across all 12 dataset–metric combinations on three major benchmarks: CAMO, COD10K, and NC4K.

0 citationsRead paper