Correct latent-reasoning trajectories are geometrically distinguishable in Huginn-3.5B
measured in 1 paper- In the recurrent-depth model Huginn-3.5B, a four-metric shape suite (entropy, effective rank, anisotropy, intrinsic dimension) applied to latent-thought trajectories separates correct from incorrect reasoning. [latent-thinking-optimization-2025-latent-reasoning-encodes-reward-signals] - Correct trajectories show higher entropy, LOWER effective rank, and higher anisotropy/intrinsic dimension than incorrect ones - a richer but more consolidated latent region. [latent-thinking-optimization-2025-latent-reasoning-encodes-reward-signals] - The signal is reliable enough that a learned Latent Reward Model predicts answer correctness from latent geometry alone (no text decoding), and is used as a training-time reward (Latent Thinking Optimization). [latent-thinking-optimization-2025-latent-reasoning-encodes-reward-signals]