Representational convergence across 16 LLMs on 800 reasoning problems is stronger for failed tasks and pre-decision stages but shows minimal causal influence on predictions, pointing to shared processing constraints over shared reasoning.
Correcting biased centered kernel alignment measures in biological and artificial neural networks.arXiv preprint arXiv:2405.01012
4 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
Sparse autoencoders resolve superposition in image-based neuron representations, recovering geometric fidelity and enabling scRNA-seq adaptation plus GW-map alignment to reconstruct pathology pathways without spatial transcriptomics.
Representational alignment varies monotonically with SNR and non-monotonically with sample size (minimized near interpolation threshold) across linear and nonlinear networks, and is decoupled from generalization error.
Decoding alignment metrics can remain high and unchanged even when encoding manifold topology is causally altered, so they do not imply similar function or computation across neural populations.
citing papers explorer
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Convergence Without Understanding: When Language Models Agree on Representations but Disagree on Reasoning
Representational convergence across 16 LLMs on 800 reasoning problems is stronger for failed tasks and pre-decision stages but shows minimal causal influence on predictions, pointing to shared processing constraints over shared reasoning.
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Resolving superposition in AI for interpretability and cross-modal alignment in patient-neuronal images
Sparse autoencoders resolve superposition in image-based neuron representations, recovering geometric fidelity and enabling scRNA-seq adaptation plus GW-map alignment to reconstruct pathology pathways without spatial transcriptomics.
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Signal-to-Noise Ratio and Sample Size Govern Representational Alignment in Neural Networks
Representational alignment varies monotonically with SNR and non-monotonically with sample size (minimized near interpolation threshold) across linear and nonlinear networks, and is decoupled from generalization error.
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Decoding Alignment without Encoding Alignment: A critique of similarity analysis in neuroscience
Decoding alignment metrics can remain high and unchanged even when encoding manifold topology is causally altered, so they do not imply similar function or computation across neural populations.