Xinyan Chen

Significant gains in video reasoning performance reveal that diverse temporal supervision and structured reasoning states are crucial for advancing model capabilities.

Effect alignment allows multi-agent systems to thrive in real-world scenarios despite dynamics mismatch, significantly boosting training efficiency and success rates.

LRMs can slash up to 40% of reasoning tokens without sacrificing accuracy by dynamically adjusting their "thinking speed" at each step.