Selected work

Representative Papers

EvoMail: Self-Evolving Cognitive Agents for Adaptive Spam and Phishing Email Defense

Sep 25, 2025

Rapid evolution of multimodal phishing emails challenges traditional static detection methods, which suffer from poor adaptability and limited interpretability. Method: We propose a cognition-driven dynamic defense framework that (1) constructs a unified heterogeneous email graph integrating text, metadata, and embedded resources; (2) designs a collaborative reasoning mechanism between a Cognition Graph Neural Network (CGNN) and large language models (LLMs) for deep multimodal signal fusion; and (3) introduces an adversarial self-evolution mechanism—where red teams generate evasion samples and blue teams iteratively update and compress a memory knowledge base using failure experiences—to enable continual learning and strategy refinement. Results: Evaluated on both real-world and synthetic datasets, our approach significantly outperforms state-of-the-art baselines in accuracy, generalization, and interpretability, empirically validating the efficacy of dynamic evolutionary defense paradigms.

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DIFFUMA: High-Fidelity Spatio-Temporal Video Prediction via Dual-Path Mamba and Diffusion Enhancement

Jul 09, 2025

High-precision industrial applications—such as semiconductor manufacturing—lack dedicated benchmark datasets for spatiotemporal video prediction, hindering progress in fine-grained dynamic modeling. Method: We introduce CHDL, the first publicly available time-series image dataset capturing chip dicing processes, and propose DIFFUMA, a dual-path predictive architecture that synergistically integrates Mamba’s long-range temporal modeling with a temporally guided diffusion mechanism: the former captures global dynamics, while the latter refines spatial details to mitigate feature degradation in fine-grained prediction. Contribution/Results: On CHDL, DIFFUMA achieves a 39% reduction in MSE and an SSIM of 0.988, substantially outperforming existing methods. Moreover, it demonstrates strong generalization to natural phenomena datasets, attaining state-of-the-art performance across multiple metrics.

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Recent publications

Latest Papers

EvoMail: Self-Evolving Cognitive Agents for Adaptive Spam and Phishing Email Defense

Sep 25, 2025

Rapid evolution of multimodal phishing emails challenges traditional static detection methods, which suffer from poor adaptability and limited interpretability. Method: We propose a cognition-driven dynamic defense framework that (1) constructs a unified heterogeneous email graph integrating text, metadata, and embedded resources; (2) designs a collaborative reasoning mechanism between a Cognition Graph Neural Network (CGNN) and large language models (LLMs) for deep multimodal signal fusion; and (3) introduces an adversarial self-evolution mechanism—where red teams generate evasion samples and blue teams iteratively update and compress a memory knowledge base using failure experiences—to enable continual learning and strategy refinement. Results: Evaluated on both real-world and synthetic datasets, our approach significantly outperforms state-of-the-art baselines in accuracy, generalization, and interpretability, empirically validating the efficacy of dynamic evolutionary defense paradigms.

0 citationsRead paper

DIFFUMA: High-Fidelity Spatio-Temporal Video Prediction via Dual-Path Mamba and Diffusion Enhancement

Jul 09, 2025

High-precision industrial applications—such as semiconductor manufacturing—lack dedicated benchmark datasets for spatiotemporal video prediction, hindering progress in fine-grained dynamic modeling. Method: We introduce CHDL, the first publicly available time-series image dataset capturing chip dicing processes, and propose DIFFUMA, a dual-path predictive architecture that synergistically integrates Mamba’s long-range temporal modeling with a temporally guided diffusion mechanism: the former captures global dynamics, while the latter refines spatial details to mitigate feature degradation in fine-grained prediction. Contribution/Results: On CHDL, DIFFUMA achieves a 39% reduction in MSE and an SSIM of 0.988, substantially outperforming existing methods. Moreover, it demonstrates strong generalization to natural phenomena datasets, attaining state-of-the-art performance across multiple metrics.

0 citationsRead paper