Zhe Zhang

The paper proposes a novel identity-based public key management framework, IPK-pq, utilizing NIST ML-DSA and random matrix theory to enhance the scalability and efficiency of Public Key Infrastructure (PKI) for large-scale, post-quantum environments.

The paper establishes a unified framework for timed opacity by introducing a universal observation model and defining evolution-based timed opacity, proving its relationship to existing opacity definitions.

The paper introduces BFIAttack, a novel attack that exploits Beamforming Feedback Information (BFI) to reconstruct a user's Channel State Information (CSI), thereby compromising Wi-Fi physical-layer security.

The paper introduces TIGER, a GPU-accelerated framework that significantly speeds up high-precision evaluation of nonlinear layers for encrypted LLM inference using TFHE.

GoAT-X introduces a novel framework that structures cross-chain smart contract auditing as a Graph of Auditing Thoughts, significantly improving the detection of complex, semantic vulnerabilities in multi-chain token transactions.

The paper introduces Tail-risk Intrinsic Geometric Smoothing (TIGS), a plug-and-play, inference-time defense that suppresses backdoor attacks on LLMs by structurally smoothing the attention mechanism without requiring model retraining or external data.

PropGuard introduces a propagation-aware framework to safeguard LLM-MAS against malicious attacks by constructing a dual-view graph, identifying suspicious propagation paths, and applying source-guided remediation.

The EvoSafety framework enhances LLM safety by externalizing attack and defense mechanisms, enabling persistent, transferable, and model-agnostic robustness against adversarial prompts.

This study provides the first measurement of authentication security in real-world remote Model Context Protocol (MCP) servers, finding pervasive and critical authentication weaknesses, particularly in dynamic client registration.

The paper proposes Hysteretic Policy Optimization (HPO) and its adaptive variant (A-HPO) to stabilize reinforcement learning training in sparse-reward environments by better balancing positive and negative advantage updates.

DeepSurvey is an agentic system that significantly enhances automated survey generation by extracting deep, structured knowledge from full-text papers and rigorously validating citations, achieving superior content depth and reliability compared to existing methods.

This paper systematically evaluates the consistency of popular causal discovery benchmarks against real-world scientific literature, revealing significant variability in their accuracy.

This paper proposes NF-CoT, a latent reasoning framework that preserves the advantages of chain-of-thought in large language models.