Kai Chen

The paper introduces Cross-Model Neuron Transfer (CNT), a post-hoc method that efficiently transfers safety-oriented functionalities between different large language models by transferring minimal subsets of neurons, achieving high performance with minimal degradation.

The paper proposes a novel, locally deployable agentic workflow using large language models (LLMs) to accurately and privately detect various types of personally identifiable information (PII) within unstructured crash narratives.

This survey establishes persistent, writable memory as an independent security problem for LLM agents, proposing a comprehensive framework for 'mnemonic sovereignty' to govern the entire memory lifecycle.

The paper proposes FreeUp, a frequency-decoupled framework that improves encrypted network anomaly detection by separately modeling and fusing low- and high-frequency components of traffic data.

The paper proposes Multi-Teacher Bayesian Knowledge Distillation (MT-BKD), a framework that uses Bayesian inference and teacher-informed priors to improve model compression, enhance predictive accuracy, and quantify uncertainty when distilling knowledge from multiple expert models.

The paper proposes Multi-Recall Memory MIA (MRMMIA), a unified attack framework to test for privacy leakage by determining if a candidate memory unit belongs to a chat agent's private memory store.

The paper proposes replacing expensive, always-on LLM calls for proactive agent triggering with a specialized Temporal-Graph-Learning (TGL) model, significantly improving efficiency and performance.