Fan Wang

The paper introduces TrEEStealer, a novel side-channel attack that efficiently steals Decision Trees (DTs) protected within Trusted Execution Environments (TEEs), demonstrating that TEEs fail to provide adequate protection against control-flow leakage.

The paper introduces PersTurnBench, a novel benchmark and evaluator for assessing personalized user conversation satisfaction at specific turns, addressing the limitation of generic response quality metrics.

Lumos-Nexus is a training-efficient framework that enhances video generation quality by progressively bridging generation from a lightweight model to a high-fidelity generator in a shared latent space, without sacrificing reasoning capabilities.

SkillSmith is a synergy-aware framework that jointly co-evolves skills and tools, significantly improving self-improving agent systems by modeling skill-tool interactions and diagnosing failures.

The paper proposes DAG-MoE, a novel sparse Mixture-of-Experts framework that replaces standard weighted-sum aggregation with structural aggregation to enhance model performance and enable multi-step reasoning.

The paper introduces Repair-Augmented Constraint Learning (RACL), a framework that models contextual decisions by allowing systems to learn whether a candidate should be repaired before being vetoed, significantly reducing false vetoes compared to existing methods.

The paper proposes a novel render-free framework that conditions video diffusion models directly on compressed 3D human mesh tokens, enabling robust 3D-aware human motion control without relying on rendered 2D guidance.