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The Ohio State University
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LLM agents struggle to generalize from experience to reusable skills, often performing worse than simply replaying past trajectories, revealing a critical gap in current abstraction methods.
Text-based speculative decoding falls flat for vision-language models, but ViSkip dynamically adapts to vision tokens for state-of-the-art acceleration.
LLMs can perfectly cluster speakers in overlapping multi-party conversations, enabling near-perfect Joint ASR-Clustering Error Rate in challenging CHiME-9 tasks.
