Yunke Zhang

IACM-RL reduces infinite loops and stale context errors by proactively managing dynamic user intents, setting a new standard for robust tool invocation.

Achieving a staggering 99.07% accuracy in polyglot speaker identification, this system outperforms traditional methods by over 30%.

By explicitly modeling and calibrating a model's intrinsic uncertainty, EGPO unlocks significant gains in reasoning performance for RL-trained language models.