Hyperdimensional Probe: Decoding LLM Representations via Vector Symbolic Architectures

• URL: http://arxiv.org/abs/2509.25045v1
• Date: Mon, 29 Sep 2025 16:59:07 GMT
• Title: Hyperdimensional Probe: Decoding LLM Representations via Vector Symbolic Architectures
• Authors: Marco Bronzini, Carlo Nicolini, Bruno Lepri, Jacopo Staiano, Andrea Passerini,
• Abstract summary: Hyperdimensional Probe is a novel paradigm for decoding information from the Large Language Models vector space.<n>It combines ideas from symbolic representations and neural probing to project the model's residual stream into interpretable concepts.<n>Our work advances information decoding in LLM vector space, enabling extracting more informative, interpretable, and structured features from neural representations.
• Score: 12.466522376751811
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite their capabilities, Large Language Models (LLMs) remain opaque with limited understanding of their internal representations. Current interpretability methods, such as direct logit attribution (DLA) and sparse autoencoders (SAEs), provide restricted insight due to limitations such as the model's output vocabulary or unclear feature names. This work introduces Hyperdimensional Probe, a novel paradigm for decoding information from the LLM vector space. It combines ideas from symbolic representations and neural probing to project the model's residual stream into interpretable concepts via Vector Symbolic Architectures (VSAs). This probe combines the strengths of SAEs and conventional probes while overcoming their key limitations. We validate our decoding paradigm with controlled input-completion tasks, probing the model's final state before next-token prediction on inputs spanning syntactic pattern recognition, key-value associations, and abstract inference. We further assess it in a question-answering setting, examining the state of the model both before and after text generation. Our experiments show that our probe reliably extracts meaningful concepts across varied LLMs, embedding sizes, and input domains, also helping identify LLM failures. Our work advances information decoding in LLM vector space, enabling extracting more informative, interpretable, and structured features from neural representations.

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