arXiv cs.AITuesday · May 26, 2026FREE

HyperGuide: Hyperbolic Guidance for Efficient Multi-Step Reasoning in Large Language Models

HyperGuide, introduced in a paper on arXiv (2605.24140), addresses multi-step reasoning in LLMs by using hyperbolic geometry to guide generation. The method leverages the structural asymmetry of reasoning trees: solution states are few, dead ends are many. Hyperbolic space naturally matches this, with distance from origin encoding solution proximity and angular separation distinguishing branches. A lightweight head projects LLM hidden states into this space, and a low-rank adapter is fine-tuned interactively on the model's own reasoning attempts. The approach yields consistent improvements across multiple benchmarks, with larger gains on deeper reasoning chains. Code is available at https://github.com/yuyuliu1. The paper was published on May 26, 2026.