Selected work

Representative Papers

Intelligent Wiretap Code Design: Exploiting Wireless Endogenous Security via Information Theory and Deep Learning Integration

Aug 10, 2026

This work addresses the reliance on traditional cryptographic assumptions by proposing a wiretap coding scheme within a semantic communication framework that leverages the intrinsic randomness of wireless channels to jointly ensure security and reliable transmission. The design employs mutual information (MI) and generalized mutual information (GMI) as optimization criteria for two canonical eavesdropping scenarios, respectively, and integrates maximum a posteriori (MAP) decoding with deep learning–driven discrete semantic representations. By uniquely unifying semantic communication, information-theoretic metrics, and deep learning for wiretap code construction, this approach significantly reduces information leakage to eavesdroppers of varying capabilities while maintaining high reliability for the legitimate receiver.

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Semantic Reformulation Entropy for Robust Hallucination Detection in QA Tasks

Sep 22, 2025

Factuality errors in large language model (LLM) question answering—primarily caused by hallucination—are inadequately captured by existing entropy-based semantic uncertainty estimation methods, which suffer from sampling noise and clustering instability induced by variable-length outputs. Method: We propose Semantic Reconstruction Entropy (SRE), a robust uncertainty quantification framework that first augments input-side semantic diversity via faithful paraphrasing, then applies energy-driven progressive hybrid clustering in semantic space to enable adaptive, length-agnostic grouping and uncertainty estimation. Contribution/Results: SRE eliminates sensitivity to output length and sampling strategy. Extensive experiments on SQuAD and TriviaQA demonstrate that SRE significantly outperforms state-of-the-art baselines in hallucination detection accuracy, stability under distributional shift, and cross-dataset generalization—achieving consistent improvements across all metrics.

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Recent publications

Latest Papers

Intelligent Wiretap Code Design: Exploiting Wireless Endogenous Security via Information Theory and Deep Learning Integration

Aug 10, 2026

This work addresses the reliance on traditional cryptographic assumptions by proposing a wiretap coding scheme within a semantic communication framework that leverages the intrinsic randomness of wireless channels to jointly ensure security and reliable transmission. The design employs mutual information (MI) and generalized mutual information (GMI) as optimization criteria for two canonical eavesdropping scenarios, respectively, and integrates maximum a posteriori (MAP) decoding with deep learning–driven discrete semantic representations. By uniquely unifying semantic communication, information-theoretic metrics, and deep learning for wiretap code construction, this approach significantly reduces information leakage to eavesdroppers of varying capabilities while maintaining high reliability for the legitimate receiver.

0 citationsRead paper

Semantic Reformulation Entropy for Robust Hallucination Detection in QA Tasks

Sep 22, 2025

Factuality errors in large language model (LLM) question answering—primarily caused by hallucination—are inadequately captured by existing entropy-based semantic uncertainty estimation methods, which suffer from sampling noise and clustering instability induced by variable-length outputs. Method: We propose Semantic Reconstruction Entropy (SRE), a robust uncertainty quantification framework that first augments input-side semantic diversity via faithful paraphrasing, then applies energy-driven progressive hybrid clustering in semantic space to enable adaptive, length-agnostic grouping and uncertainty estimation. Contribution/Results: SRE eliminates sensitivity to output length and sampling strategy. Extensive experiments on SQuAD and TriviaQA demonstrate that SRE significantly outperforms state-of-the-art baselines in hallucination detection accuracy, stability under distributional shift, and cross-dataset generalization—achieving consistent improvements across all metrics.

0 citationsRead paper