Silvio Savarese

Test-time harnesses can nearly double the performance of weaker models, transforming how we think about capability transfer in AI.

Evolving the harness of LLM agents can yield a 17-point performance boost without compromising their ability to generalize across tasks.

Video LLMs can significantly improve their QA performance by integrating spatio-temporal evidence, bridging the gap between accuracy and visual perception.

Achieving 4.7x to 8.2x higher throughput for trillion-parameter MoE models could redefine the limits of large-scale model training.

Whisper's speech-centric training leaves audio-LLMs tone-deaf to music and environmental sounds, but a simple fine-tune can fix that.

Forget slow, end-to-end models: building real-time voice agents hinges on a cascaded streaming pipeline, as demonstrated by a new tutorial achieving sub-second latency.

Forget text prompts: vector prompt interfaces are the key to unlocking scalable and stable LLM customization.

Real-time voice agents can bypass slow vector DB lookups with a dual-agent architecture that pre-fetches relevant documents into a sub-millisecond semantic cache.

A unified Vision-Language Model and Diffusion architecture unlocks surprisingly effective optical flow forecasting from noisy web data, enabling language-conditioned robot control and video generation.