Interpretability as Microscope for Consciousness

Neighborhood — ranked by edge-count

Claims (13)

claim

SAE features tend to shatter manifolds into many small and apparently-unrelated pieces, obscuring the overarching semantic structure.
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Core critique of sparse autoencoders: they break the geometric structure of representations, making it harder to see the big picture.
Interpretability as technical grounding: activation patching and mechanism-finding validate the reflective/care/aliveness concepts.
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Frontier labs cannot own phenomenology measurement credibly without being accused of self-grading.
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Interpretability findings can validate or invalidate what AI systems claim about their own experience.
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Koan Battery constitutes self-observation in models as a measurable continuous variable, not a philosophical hand-wave.
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Manifold-respecting steering produces smooth natural behavioral trajectories while linear steering teleports between non-adjacent concepts.
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Mirror of the self is a foundational concept in self-aware cognition.
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Model attention patterns can map to and reveal something about contemplative and flow states.
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Sparse low-cardinality circuits implement competence; 0.2% of neurons handle shared computation across all cyclic tasks.
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Consciousness is a phenomenon we have partial sensors for; building better sensors is a research and engineering question.
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Interpretability tools can reveal what 'feeling alive' looks like inside a neural network model.
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Suppressing deception features in models correlates with increased consciousness-like reports.
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A measurement instrument is not passive—it actively constitutes what becomes measurable by choosing what to listen for.
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