Yichen Wang

LLM pre-training power laws are not fixed but dynamically shaped by learning-rate and batch-size schedules, with a novel forcing-memory surrogate accurately predicting loss across these regimes.

A closed-loop world model that learns from both successes and failures could revolutionize dexterous manipulation in robotics.

JTA reveals that validation gaps in safety-critical software can be systematically addressed by treating scenarios, test systems, and systems under test as interconnected entities.

Urban facade reconstruction can achieve superior geometric accuracy by integrating lightweight structural supervision, overcoming common pitfalls of traditional methods.

Concordia achieves fault tolerance for LLM inference by seamlessly integrating persistent kernel checkpointing, enabling rapid recovery without CPU bottlenecks.