Resolving superposition in AI for interpretability and cross-modal alignment in patient-neuronal images
Artificial intelligence is transforming our capability to solve biological challenges. In dimensionality bottleneck regimes exacerbated by high-dimensional biological data, neural networks force distinct concepts into the lower dimensions known as superposition. Although this superposition is widely known to hinder interpretability, its impact on corrupting the geometry of latent spaces remains critically overlooked. Here, we utilized sparse autoencoders (SAEs) trained on over 100,000 multiplexe
Record details
Published: 30 June 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Healthcare · Biotech
Retrieved: 14 July 2026
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ethics.ai (30 June 2026), “Resolving superposition in AI for interpretability and cross-modal alignment in patient-neuronal images,” evidence record 409, https://ethics.ai/record/409 (originally published by arXiv).
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