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Introduces Behavior Forecasters (BFs) that take reasoning trajectories as input and achieve more accurate forecasts than frontier models at a fraction of the cost.
This paper proposes training Behavior Forecasters to predict large reasoning model outputs from single trajectories, outperforming large language models like GPT-5.4 and Claude Opus-4.6 at lower computational cost, bypassing traditional explainability methods.
Geometric Latent Reasoning (GLR) introduces a geometric path-approximation method for latent reasoning in LLMs, enabling shorter generations while maintaining accuracy across mathematical reasoning benchmarks.