Insight cascades and implicit learning require balance between directed attention and openness.

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Mechanistic interpretability & model evaluation
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Spans attention head decomposition, benchmark awareness, and genomic pathogenicity prediction via neural models.
Mechanistic structure of transformer attention computations
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Identifies distributed algorithms implemented across attention heads, with focus on causal masking limitations and emergent capabilities via activation manifold steering.
Emergence through distributed attention and uncertainty
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Explores how complex phenomena arise from non-linear interactions across distributed systems, emphasizing productive not-knowing and implicit learning mechanisms.

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Non-Dual Consciousness Teachers
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unfold-chat-catalog.md
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Related by similarity (8)

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Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

We hypothesize that insight produces a profound reduction in the latency of evoked neuronal responses when subjects know the meaning of cues (have learned a rule), equivalently an increase in ERP amplitude to initial cues in a sequence.hypothesis0.769
Empirically testable neural prediction of the active inference model of insight
Any system that exists will appear to minimize free energy and therefore engage in active inference. Furthermore, this apparently inferential or mindful behaviour is (almost) inevitable.concept0.761
Friston's key assertion resolving the tautology: existence implies free energy minimization, making inference inevitable.
The affordances of software are often abstract: actions have direct effects that users cannot see and indirect effects they cannot anticipate.claim0.759
Why concepts are needed to make sense of complex systems.
The key challenge for active inference is finding the generative model that best explains observable data.claim0.759
Identifies an outstanding problem, Section 10.
Cognition and learning are substrate-independent.quote0.759
Bold, load-bearing tenet of the paper's framework.
Do human participants demonstrate the same insight dynamics predicted by active inference in the rule-learning paradigm (currently under investigation with eye tracking and crowd-sourced reaction times)?question0.757
Empirical gap explicitly acknowledged; experiments reportedly in progress at time of writing
H1: Alignment training is attention training for models — Constitutional AI trains self-observation explicitly.hypothesis0.757
Confirmatory hypothesis supported at p=0.006
Any system monitoring and controlling its own processing requires all three hierarchical levels: perception, attention, meta-awareness.claim0.756
Design principle with implications for AI and consciousness-UX; architectural requirement for self-directed cognition.