Han Fang

The paper proposes a novel proof-of-authorship framework for AI-generated content by cryptographically binding the random seed used in latent diffusion model generation to the author's identity, offering a stronger guarantee than traditional ownership methods.

SnapGuard proposes a lightweight, multimodal method to detect prompt injection attacks in screenshot-based web agents by analyzing visual stability and contrast-polarity textual signals, achieving high accuracy with significantly reduced computational overhead.

The paper proposes FluxMem, a novel connectivity-evolving memory framework that models memory as a dynamic graph to improve LLM agent performance in complex, changing environments.

The paper introduces MemTrace, a framework that treats LLM memory pipelines as traceable graphs to systematically diagnose and automatically correct memory-related errors, boosting performance by up to 7.62%.

The paper introduces a formal, logically constrained framework, ePCA, to secure advanced AI agents by forcing them to translate natural language intentions into first-order logical constraints before execution, achieving provably secure performance.

The paper introduces an executable Proof-Constrained Action (ePCA) framework that secures AI agents by forcing them to formalize their intentions into first-order logical constraints, achieving provably secure operation.