Fisher-information-guided margin refinement reorganizes the Voronoi tessellation of an LLM's latent space to raise median token margins by 28% with no benchmark degradation, though gains concentrate on high-frequency structural tokens.
When intermediate hidden states are projected through the final lm_head, positions that are ultimately predicted correctly often remain geometrically ambiguous at these depths
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Geometric Properties of the Voronoi Tessellation in Latent Semantic Manifolds of Large Language Models
Fisher-information-guided margin refinement reorganizes the Voronoi tessellation of an LLM's latent space to raise median token margins by 28% with no benchmark degradation, though gains concentrate on high-frequency structural tokens.