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arXiv · 2505.07831

Polysemy of Synthetic Neurons Towards a New Type of Explanatory Categorical Vector Spaces

Michael Pichat·William Pogrund·Paloma Pichat·Judicael Poumay·Armanouche Gasparian·Samuel Demarchi·Martin Corbet·Alois Georgeon·Michael Veillet-Guillem
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Abstract

The polysemantic nature of synthetic neurons in artificial intelligence language models is currently understood as the result of a necessary superposition of distributed features within the latent space. We propose an alternative approach, geometrically defining a neuron in layer n as a categorical vector space with a non-orthogonal basis, composed of categorical sub-dimensions extracted from preceding neurons in layer n-1. This categorical vector space is structured by the activation space of each neuron and enables, via an intra-neuronal attention process, the identification and utilization of a critical categorical zone for the efficiency of the language model - more homogeneous and located at the intersection of these different categorical sub-dimensions.

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Michael Pichat, William Pogrund, Paloma Pichat, Judicael Poumay, Armanouche Gasparian, Samuel Demarchi, Martin Corbet, Alois Georgeon, Michael Veillet-Guillem. 2025-04-30. Polysemy of Synthetic Neurons Towards a New Type of Explanatory Categorical Vector Spaces. https://arxiv.org/abs/2505.07831

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