Jie Xu

SkillTester is a comprehensive tool and framework designed to benchmark both the functional utility and the security robustness of agent skills, providing standardized scores and status labels.

GasLiteAA proposes optimizing the ERC-4337 standard by offloading gas sponsorship logic to Trusted Execution Environments (TEE), significantly reducing on-chain gas costs while maintaining security and verifiability.

The paper proposes PINA, a two-stage differentially private clustered federated learning framework that improves convergence and robustness by using low-rank adaptation and a normality-driven aggregation mechanism.

The paper introduces CAESAR, a novel multi-agent framework that coordinates LLM agents across five specialized roles to improve success rates and stability in complex, multi-stage cyber intrusion tasks.

The paper proposes DP-LAC, a novel lightweight adaptive clipping technique for differentially private federated fine-tuning, which efficiently estimates and adapts the clipping threshold without consuming extra privacy budget or requiring manual hyperparameter tuning.

DisAgg introduces a novel secure aggregation protocol that uses a small committee of Aggregators to compute partial sums, achieving a significant speedup (4.6x) over previous state-of-the-art methods like OPA while maintaining privacy.

SSR3D-LLM introduces a structured spatial reasoning interface for unified 3D-LLMs, allowing fine-grained object grounding by generating and processing sequential latent spatial steps.

The paper introduces SafeMed-R1, a clinically audited LLM that significantly improves safety and ethical alignment for medical applications, matching or exceeding resident performance on safety-critical tasks.

The paper proposes TCP-MCP, a co-evolution framework that jointly optimizes agent prompts and communication topologies to design highly efficient and effective multi-agent systems.

The paper proposes iCoTASC, a framework for real-time, channel-adaptive semantic resource allocation in collaborative multi-device systems using importance-aware embedding selection, data-driven utility function, and per-transmitter utility lookup tables.

This paper proposes SpikingMOT, a spike-driven multi-object tracking system that uses spiking neural networks and adaptively models sparse trajectory dynamics.