Yan-Kai Chen

Removing a mean shift from data representations requires a signal-to-noise ratio that is $O(d)$ times higher than simply detecting it, a gap that standard linear methods cannot close.

ANIMASK, a simulation framework that freezes books and scripts into story worlds whose characters act on their own motivations and replays each story from its freeze point, is introduced.

Anomaly detection can now adapt in real-time to evolving patterns without retraining, achieving state-of-the-art results on diverse datasets.

No memory substrate is a one-size-fits-all solution; the right choice depends on the task, with broad retrieval boosting QA but hindering decision-making.

Leading LLM investment advisors can significantly enhance long-term investor outcomes, revealing a critical gap in traditional evaluation methods.