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The paper proposes HEST, a method that embeds LLM hidden states into the Poincaré ball with an entropy-trained probe and steers only high-entropy (hesitant) tokens along hyperbolic geodesics, improving math reasoning accuracy on Qwen2.5-Math and Llama-3.1 models where fixed-vector Euclidean steering fails.
Proposes HDE-Net, a manifold-constrained deep neural network that uses hyperbolic space to better model rule-based structures in tabular data, achieving state-of-the-art performance on the TALENT-tiny-core benchmark while maintaining efficiency.
PoLAR introduces a geometrically structured latent action representation in hyperbolic space that separates transition extent from mode, improving robotic policy learning performance.