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The Ohio State University
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OpenPCC achieves secure LLM serving without proprietary hardware, paving the way for broader adoption of privacy-preserving AI solutions.
MiniMax-M2 proves that massive parameter counts don't always translate to better agentic performance; strategic activation of a smaller subset can unlock frontier-level intelligence.
Achieve real-time, proactive video understanding with StreamOV, which uses bounded memory and a novel response trigger to overcome the limitations of offline methods.
Transfer learning from a large, pre-trained speech synthesis model unlocks high-quality Tibetan TTS, even with limited Tibetan-specific data.
Current evaluation metrics for trajectory inference can mislead researchers, but functional KL divergence offers a clearer, more reliable comparison of methods in sparse data conditions.
DQPOPE achieves the same sample efficiency as traditional OPE methods while providing a comprehensive return distribution, leading to significantly more accurate policy evaluations.
LIME leverages LLM-generated narratives to create a scalable surgical dataset, but SurgLIME's innovative approach ensures that noisy text doesn't compromise model performance.
Speculative decoding can be sped up by >2x without sacrificing accuracy by rescuing previously rejected tokens that are semantically valid but lexically different.
Achieve flicker-free, scalable reconstruction of long dynamic videos by blending the best of stream and clip-based Gaussian Splatting.
