Manh Nguyen

Under a shared token budget, a model's remaining reasoning capacity is more critical than the volume of context it reads.

Standard next-token adaptation fails on financial time series unless fine-tuning objectives explicitly penalize rollouts for distorting higher-order temporal structures like volatility clustering.

Expert-guided forecast editing can dramatically enhance time-series predictions by intelligently balancing exploitation and exploration, leading to superior outcomes.

LLMs can reason more accurately without extra compute: just intelligently reallocate your sampling budget based on a simple uncertainty estimate.