Nan Zhang

The paper proposes SkillProbe, a multi-agent security auditing framework, demonstrating that high-popularity skills in LLM agent marketplaces are often insecure due to systemic combinatorial risks.

The paper proposes DAMPER, a domain-aware framework that autonomously extracts and rewrites private information from text while providing rigorous differential privacy guarantees, significantly improving the privacy-utility trade-off.

This paper provides a large-scale empirical analysis of indirect prompt injections found in webpages, revealing that prompt-based interference is a widespread, persistent, and growing threat targeting automated LLM systems.

CubePart is a generative framework that enables the creation of complex 3D meshes by explicitly controlling and generating individual, semantically defined parts based on open-vocabulary text prompts.

EigeNet introduces a geometry-informed multi-modal Transformer framework to achieve state-of-the-art few-shot novel view Room Impulse Response (RIR) prediction by effectively integrating spatial geometry and multi-view acoustic context.

The paper introduces CardioLens, a rigorous evaluation testbed for multi-sequence Cardiac MRI, which reveals that current Multimodal Large Language Models (MLLMs) exhibit a significant 'clinical reality gap' and perform poorly when simulating real-world cardiac interpretation workflows.

DynaTree introduces a two-stage framework that pre-constructs a reusable retrieval tree offline using coordinated agents, allowing for efficient, structure-aware, and highly effective time-sensitive news retrieval online.

SkillPager is a novel two-stage framework that efficiently selects minimal, execution-sufficient context from large procedural skill documents by leveraging typed semantic nodes, significantly reducing token usage while maintaining high context sufficiency for LLM agents.

The paper proposes Distribution-Aligned Self-Distillation (DASD) to improve self-distillation by dynamically filtering high-perplexity tokens, thereby preserving useful logical knowledge while suppressing harmful stylistic biases.