Yuzhe Wang

LARC achieves a staggering 10.28x speedup in trajectory certification without sacrificing accuracy, revolutionizing how we approach robot motion planning.

Transcript-based shortcuts in dialogue models lead to a staggering drop in accuracy, revealing a critical flaw in current evaluation methods.

Current speech-to-speech models may sound good, but they miss the mark on natural conversational dynamics, revealing critical areas for improvement.

Emotional entrainment detection can reach 97.01% accuracy by considering the temporal dynamics of dyadic speech interactions.

Speech-aware LLMs are surprisingly bad at speaker verification, but a simple embedding injection trick closes the gap with dedicated systems while preserving the LLM's language abilities.

LLMs can be tricked into making incorrect causal judgments by semantically similar questions, even with Chain-of-Thought prompting, highlighting a critical gap in true causal reasoning.