Selected work

Representative Papers

Residue Number System Comparison revisited, a software perspective

May 18, 2026

This work addresses the long-standing challenge of general integer comparison in residue number systems (RNS) by proposing an efficient method based on the introduction of an auxiliary modulus and a single mixed-radix conversion. The approach is applicable to arbitrary RNS moduli sets without imposing restrictions on the input range, thereby overcoming limitations inherent in existing techniques that require specific modulus forms or bounded dynamic ranges. The algorithm achieves a time complexity of O(n²), which can be parallelized to O(log n), significantly outperforming both classical and recent state-of-the-art methods constrained by such assumptions. This advancement provides a novel and practical solution to a critical bottleneck in RNS-based applications, including division, scaling, and cryptographic operations.

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Collaborative Edge Inference via Semantic Grouping under Wireless Channel Constraints

Oct 02, 2025

To address the trade-off between communication overhead and classification accuracy in edge-device collaborative inference under wireless channel constraints, this paper proposes a semantic-grouping collaborative inference framework based on a key-value mechanism. The method enables adaptive selection of collaborating nodes and channel-aware semantic-level feature compression through intermediate feature exchange, selective information transmission, key-value matching, and communication pruning. A key insight is that query transmission requires higher reliability than feature transmission to ensure robust collaborative inference. Experiments demonstrate that the approach reduces bandwidth consumption by up to 62% while maintaining high classification accuracy (error increase <1.2%) and exhibiting strong robustness against channel noise and bit errors. Thus, it significantly enhances inference efficiency and generalization capability in resource-constrained edge environments.

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PrompTrend: Continuous Community-Driven Vulnerability Discovery and Assessment for Large Language Models

Jul 25, 2025

Static benchmarking inadequately captures LLM security vulnerabilities exposed in real-world online community practices. To address this, we propose a community-driven dynamic monitoring paradigm, focusing on psychological attacks as the primary threat vector, revealing that capability advancement and safety improvement are misaligned. Methodologically, we design a cross-platform data collection system, a multidimensional scoring framework, and a scalable monitoring architecture—integrating horizontal comparative analysis with fine-grained vulnerability classification. Over five months, we conduct an empirical study across nine commercial LLMs. Our approach identifies 198 novel vulnerabilities with 78% classification accuracy; psychological attacks exhibit significantly higher detection rates than traditional exploit-based techniques yet demonstrate low cross-model transferability—highlighting their stealthiness and model specificity. This work pioneers systematic, continuous discovery and quantitative evaluation of community-emergent LLM vulnerabilities.

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Bridging AI and Software Security: A Comparative Vulnerability Assessment of LLM Agent Deployment Paradigms

Jul 08, 2025

Current LLM agent security research treats AI-specific vulnerabilities and traditional software flaws in isolation, lacking a unified cross-domain evaluation framework. Method: This work presents the first systematic comparison of two dominant deployment paradigms—Function Calling (FC) and Model Context Protocol (MCP)—and introduces a unified threat taxonomy integrating AI reasoning vulnerabilities with classical software security concepts. We empirically evaluate the robustness of seven LLMs against prompt injection, JSON injection, DoS, and multi-step chained attacks across 3,250 adversarial scenarios. Contribution/Results: FC exhibits higher overall attack success (73.5% vs. 62.59%), but risks concentrate at the system layer; MCP exacerbates LLM centralization and exposure. Chained attacks achieve 91–96% success rates, and advanced reasoning models show heightened exploitability. Our findings reveal how architectural choices fundamentally reshape the threat landscape, establishing novel conceptual insights and practical benchmarks for secure LLM agent design.

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Collision Resolution in RFID Systems Using Antenna Arrays and Mix Source Separation

May 01, 2025IEEE Communications Letters

To address tag identification failure caused by signal collisions in RFID systems, this paper proposes a novel pilot-free signal recovery method leveraging antenna arrays and blind source separation. The method introduces a hybrid objective function that jointly incorporates the Zero-Forcing Constant-Modulus (ZFCM) criterion and a newly designed ambiguity suppression criterion, effectively mitigating phase and amplitude ambiguities inherent in conventional ZFCM-based beamformer estimation; gradient descent optimization ensures efficient solution convergence. By fully exploiting the spatial resolution of antenna arrays, the approach achieves high-precision and robust separation of concurrent tag signals—without requiring channel state information, tag location knowledge, or pilot symbols. Experimental results demonstrate substantial improvements in identification accuracy under high-density tag scenarios, establishing a scalable physical-layer multiple-access solution for passive RFID systems.

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Recent publications

Latest Papers

Residue Number System Comparison revisited, a software perspective

May 18, 2026

This work addresses the long-standing challenge of general integer comparison in residue number systems (RNS) by proposing an efficient method based on the introduction of an auxiliary modulus and a single mixed-radix conversion. The approach is applicable to arbitrary RNS moduli sets without imposing restrictions on the input range, thereby overcoming limitations inherent in existing techniques that require specific modulus forms or bounded dynamic ranges. The algorithm achieves a time complexity of O(n²), which can be parallelized to O(log n), significantly outperforming both classical and recent state-of-the-art methods constrained by such assumptions. This advancement provides a novel and practical solution to a critical bottleneck in RNS-based applications, including division, scaling, and cryptographic operations.

0 citationsRead paper

Collaborative Edge Inference via Semantic Grouping under Wireless Channel Constraints

Oct 02, 2025

To address the trade-off between communication overhead and classification accuracy in edge-device collaborative inference under wireless channel constraints, this paper proposes a semantic-grouping collaborative inference framework based on a key-value mechanism. The method enables adaptive selection of collaborating nodes and channel-aware semantic-level feature compression through intermediate feature exchange, selective information transmission, key-value matching, and communication pruning. A key insight is that query transmission requires higher reliability than feature transmission to ensure robust collaborative inference. Experiments demonstrate that the approach reduces bandwidth consumption by up to 62% while maintaining high classification accuracy (error increase <1.2%) and exhibiting strong robustness against channel noise and bit errors. Thus, it significantly enhances inference efficiency and generalization capability in resource-constrained edge environments.

0 citationsRead paper

PrompTrend: Continuous Community-Driven Vulnerability Discovery and Assessment for Large Language Models

Jul 25, 2025

Static benchmarking inadequately captures LLM security vulnerabilities exposed in real-world online community practices. To address this, we propose a community-driven dynamic monitoring paradigm, focusing on psychological attacks as the primary threat vector, revealing that capability advancement and safety improvement are misaligned. Methodologically, we design a cross-platform data collection system, a multidimensional scoring framework, and a scalable monitoring architecture—integrating horizontal comparative analysis with fine-grained vulnerability classification. Over five months, we conduct an empirical study across nine commercial LLMs. Our approach identifies 198 novel vulnerabilities with 78% classification accuracy; psychological attacks exhibit significantly higher detection rates than traditional exploit-based techniques yet demonstrate low cross-model transferability—highlighting their stealthiness and model specificity. This work pioneers systematic, continuous discovery and quantitative evaluation of community-emergent LLM vulnerabilities.

0 citationsRead paper

Bridging AI and Software Security: A Comparative Vulnerability Assessment of LLM Agent Deployment Paradigms

Jul 08, 2025

Current LLM agent security research treats AI-specific vulnerabilities and traditional software flaws in isolation, lacking a unified cross-domain evaluation framework. Method: This work presents the first systematic comparison of two dominant deployment paradigms—Function Calling (FC) and Model Context Protocol (MCP)—and introduces a unified threat taxonomy integrating AI reasoning vulnerabilities with classical software security concepts. We empirically evaluate the robustness of seven LLMs against prompt injection, JSON injection, DoS, and multi-step chained attacks across 3,250 adversarial scenarios. Contribution/Results: FC exhibits higher overall attack success (73.5% vs. 62.59%), but risks concentrate at the system layer; MCP exacerbates LLM centralization and exposure. Chained attacks achieve 91–96% success rates, and advanced reasoning models show heightened exploitability. Our findings reveal how architectural choices fundamentally reshape the threat landscape, establishing novel conceptual insights and practical benchmarks for secure LLM agent design.

0 citationsRead paper

Collision Resolution in RFID Systems Using Antenna Arrays and Mix Source Separation

May 01, 2025IEEE Communications Letters

To address tag identification failure caused by signal collisions in RFID systems, this paper proposes a novel pilot-free signal recovery method leveraging antenna arrays and blind source separation. The method introduces a hybrid objective function that jointly incorporates the Zero-Forcing Constant-Modulus (ZFCM) criterion and a newly designed ambiguity suppression criterion, effectively mitigating phase and amplitude ambiguities inherent in conventional ZFCM-based beamformer estimation; gradient descent optimization ensures efficient solution convergence. By fully exploiting the spatial resolution of antenna arrays, the approach achieves high-precision and robust separation of concurrent tag signals—without requiring channel state information, tag location knowledge, or pilot symbols. Experimental results demonstrate substantial improvements in identification accuracy under high-density tag scenarios, establishing a scalable physical-layer multiple-access solution for passive RFID systems.

0 citationsRead paper