Yang Cheng
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This paper proposes using a zero-determinant (ZD) strategy to construct an effective Moving Target Defense (MTD) that maintains performance comparable to the optimal Stackelberg equilibrium while drastically reducing computational complexity.
The paper introduces MOV-Bench, a challenging benchmark for multi-hop audio-visual reasoning, and proposes AOP-Agent, an agentic framework that significantly improves open-source Omni-LLMs' ability to perform active cross-modal perception.
The paper proposes a pose-conditioned, permutation-equivariant denoiser to accurately reconstruct work zone geometry using noisy Ultra-Wideband (UWB) range data from connected and autonomous vehicles (CAVs).
The paper proposes LaSR, a context-aware training paradigm that uses latent reasoning to significantly improve speech recognition, especially for specialized terminology, without adding latency.
Papers
LaSR: Context-Aware Speech Recognition via Latent Reasoning
Heyang Liu, Ziyang Cheng, Jiayi Huang, Wenyang Xiao +4 more
The paper proposes LaSR, a context-aware training paradigm that uses latent reasoning to significantly improve speech recognition, especially for specialized terminology, without adding latency.