Ye Chen

TARA redefines prompt optimization by routing specific failures to tailored repair operators, achieving superior semantic accuracy without the need for generator retraining.

LLMs show significant vulnerability to logical fallacies, with distinct profiles of resilience that could inform future model training strategies.

Transforming video generation from a pixel sampling problem to a structured orchestration of the physical world, WNM enables unprecedented control and efficiency in content creation.

Ling-2.6 and Ring-2.6 achieve unprecedented efficiency in agentic intelligence, enabling instant responses and deep reasoning at trillion-parameter scale.

You can slash LLM prompt evaluation costs by 35-60% without sacrificing accuracy by intelligently selecting which examples to use.