claim

active

claim:activation-based-interpretability-does-not-immediately-explain-the-computations-that-gave-rise-to-activations-understanding-parameters-is-necessary-for-deeper-insight

Activation-based interpretability does not immediately explain the computations that gave rise to activations; understanding parameters is necessary for deeper insight

Motivates shift from studying model activations ('thoughts') to understanding parameters ('the computations themselves').

Source paper

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cimcWhitepaper

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Communities (4)

community

Mechanistic interpretability & model evaluation
members_of
Spans attention head decomposition, benchmark awareness, and genomic pathogenicity prediction via neural models.
Mechanistic interpretability via parameter decomposition
members_of
Tracing information flow through weight matrices and attention heads using attribution graphs to identify causally important subcomponents in language models.
AI phenomenology & mechanistic interpretability
members_of
Linking mechanistic interpretability methods to validating AI self-reports of inner experience
Mechanistic interpretability through parameter analysis
members_of
Understanding neural network computation by examining weights, circuits, and signal routing rather than activation patterns alone.

Claims (1)

claim

Bottom-up interpretability explains computation in the model's own terms rather than imposing top-down abstractions
extends
VPD is positioned as advancing a paradigm shift from top-down mechanistic interpretability (activation-based) to parameter-centric, data-driven discovery.

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

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

The field of interpretability has focused mainly on understanding model activations, not the computations themselvesclaim0.854
Motivation for VPD's parameter-focused approach.
Interpretability as technical grounding: activation patching and mechanism-finding validate the reflective/care/aliveness concepts.claim0.794
Causal abstraction is not enough for mechanistic interpretability because it becomes vacuous without assumptions about how models encode informationclaim0.787
Central thesis of the paper
Higher-activating feature intervals are systematically more interpretable than lower-activating intervals in human analysisfinding0.782
Shows interpretability correlates with activation strength, most model effect comes from high activations
Interpretability findings can validate or invalidate what AI systems claim about their own experience.claim0.781
We hypothesize that interpretability methods could provide further evidence about indicators in particular systems or serve as the basis for distinct tests for consciousness.hypothesis0.777
Forward-looking suggestion for how inner interpretability could extend the indicator method
interpretabilityconcept0.772
The capability to explain model predictions; a central theme of the paper, with disruption profiles as vehicle.
Can NLAs provide mechanistic grounding of which aspects of an activation drove components of explanations?question0.770
Identified as a key limitation: NLAs are blackboxes by construction.