AI RESEARCH
Geometric Latent Reasoning Induces Shorter Generations in LLMs
arXiv CS.CL
•
ArXi:2606.02248v1 Announce Type: new Large language models solve complex problems by generating lengthy chains of explicit reasoning tokens. While effective, this makes reasoning expensive, length-sensitive, and constrained to (discrete) natural language. While latent reasoning offers a continuous alternative, determining useful structures for intermediate latent states is an open challenge. In this paper, we formulate latent reasoning as a geometric path-approximation problem within the model's pretrained token-embedding space. We.