Suorong Yang

Reasoning supervision can be compressed over 450x with uncertainty-guided contraction, boosting LLM efficiency without sacrificing capability.

TailSieve achieves up to 2.59x speedup in LLM rollouts by intelligently routing long-tail requests, transforming how we handle high-concurrency decoding.

TAILS resolves cross-task ambiguities in continual learning by directly correcting feature representations, leading to significant performance boosts without changing the underlying model.

$\Sigma$-Mem not only tracks agent reliability but also adapts dynamically to feedback, outperforming traditional methods in multi-agent coordination.

CustomShift achieves unprecedented balance between semantic fidelity and subject consistency in image generation, outperforming existing methods by leveraging a novel attention distribution shift.

Imagine AI scientists that not only reason but also autonomously conduct experiments in the real world – that's the promise of Intelligent Science Laboratories.