Shaoxiong Feng

Amortized agentic policy discovery enables personalized test-time scaling to satisfy complex, multidimensional user requirements, outperforming single-objective optimization on unseen profiles.

LLMs can reason more efficiently by sharing intermediate thoughts during parallel search, achieving better accuracy with less computation.