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ARXIV:2604.00208 · NEURAL REPRESENTATION ANALYSIS · SUBMITTED 02 APR · 20:56 UTC · FRESHNESS STALE
ARXIV:2604.00208NEURAL REPRESENTATION ANALYSISSUBMITTED 02 APR · 20:56 UTCFRESHNESS STALESunny Liu · Habon Issa · André Longon · Liv Gorton · Meenakshi Khosla · David Klindt · arXiv
A theoretical framework for understanding neural network representations in superposition, highlighting limitations of current alignment metrics.
Opportunity summary
Pain A theoretical framework for understanding neural network representations in superposition, highlighting limitations of current alignment metrics.
Evidence 34 refs | 3 sources | 33% coverage
Blocker Evidence unverified
A theoretical framework for understanding neural network representations in superposition, highlighting limitations of current alignment metrics. Standard alignment metrics operate on raw neural activations, implicitly assuming that similar representations produce similar activity patterns.
Comparing the internal representations of neural networks is a central goal in both neuroscience and machine learning. Standard alignment metrics operate on raw neural activations, implicitly assuming that similar representations produce similar activity patterns.
ScienceToStartup currently rates this 1.0/10 on the public viability pass. We therefore argue that comparing neural systems in superposition requires extracting and aligning the underlying features rather than comparing the raw neural mixtures.
Neural Representation Analysis moved forward this cycle; last verified April 2026. Public score 1.0/10.
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A theoretical framework for understanding neural network representations in superposition, highlighting limitations of current alignment metrics.
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10.48550/arXiv.2604.00208A theoretical framework for understanding neural network representations in superposition, highlighting limitations of current alignment metrics.
Abstract
Comparing the internal representations of neural networks is a central goal in both neuroscience and machine learning. Standard alignment metrics operate on raw neural activations, implicitly assuming that similar representations produce similar activity patterns. However, neural systems frequently operate in superposition, encoding more features than they have neurons via linear compression. We derive closed-form expressions showing that superposition systematically deflates Representational Similarity Analysis, Centered Kernel Alignment, and linear regression, causing networks with identical feature content to appear dissimilar. The root cause is that these metrics are dependent on cross-similarity between two systems' respective superposition matrices, which under assumption of random projection usually differ significantly, not on the latent features themselves: alignment scores conflate what a system represents with how it represents it. Under partial feature overlap, this confound can invert the expected ordering, making systems sharing fewer features appear more aligned than systems sharing more. Crucially, the apparent misalignment need not reflect a loss of information; compressed sensing guarantees that the original features remain recoverable from the lower-dimensional activity, provided they are sparse. We therefore argue that comparing neural systems in superposition requires extracting and aligning the underlying features rather than comparing the raw neural mixtures.
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unverified34 refs; 3 sources; 33% coverage.
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PROBLEM
A theoretical framework for understanding neural network representations in superposition, highlighting limitations of current alignment metrics. Standard alignment metrics operate on raw neural activations, implicitly assuming that similar representations produce similar activi...
METHOD
Comparing the internal representations of neural networks is a central goal in both neuroscience and machine learning. Standard alignment metrics operate on raw neural activations, implicitly assuming that similar representations produce similar activity patterns.
RESULT
ScienceToStartup currently rates this 1.0/10 on the public viability pass. We therefore argue that comparing neural systems in superposition requires extracting and aligning the underlying features rather than comparing the raw neural mixtures.
WHY NOW
Neural Representation Analysis moved forward this cycle; last verified April 2026. Public score 1.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A theoretical framework for understanding neural network representations in superposition, highlighting limitations of current alignment metrics. Standard alignment metrics operate on raw neural activations, implicitly assuming that similar representations produce similar activity patterns.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Comparing the internal representations of neural networks is a central goal in both neuroscience and machine learning. Standard alignment metrics operate on raw neural activations, implicitly assuming that similar representations produce similar activity patterns.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 1.0/10 on the public viability pass. We therefore argue that comparing neural systems in superposition requires extracting and aligning the underlying features rather than comparing the raw neural mixtures.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Neural Representation Analysis moved forward this cycle; last verified April 2026. Public score 1.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A theoretical framework for understanding neural network representations in superposition, highlighting limitations of current alignment metrics.
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Neural Representation Analysis
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34 references, 3 sources, 33% evidence coverage.
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