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Large language models (LLMs) are increasingly used as interactive agents and simulators, yet it remains unclear whether they can recover latent sequential structure beyond surface action frequencies. This distinction is critical for behavioral simulation, where actions are often shaped by prior context rather than marg...
When a large language model solves a mathematical problem, its reasoning is largely hierarchical, and the solution often branches at a few tokens where the next-token entropy is high. Such tree-like structure embeds in hyperbolic space with far lower distortion than in Euclidean space. Activation steering, however, usu...
HyperGuide is introduced, a framework that treats reasoning as movement through a tree of states embedded in hyperbolic space that matches or exceeds the accuracy of search- and verifier-based baselines while generating substantially fewer tokens than these baselines and about as many as few-shot prompting.
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