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question:what-nuances-do-we-miss-when-we-fail-to-causally-probe-the-representations-of-the-systems

What nuances do we miss when we fail to causally probe the representations of the systems?

Motivates the empirical comparison between MAS and RSA/CKA in the paper.

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Model Alignment Search

(2025) · Satchel Grant

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Model Alignment Search
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Correlative methods like RSA and CKA are insufficient for determining functional similarity between neural systems; causal methods are necessary
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Central motivating claim of the paper; supported by empirical comparisons showing RSA/CKA miss Markovian differences detectable by MAS.

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.

Are high-accuracy probe representations also causally relevant for the task?question0.795
Question raised by the discrepancy between DAS IIA and linear probe accuracy in Case Study II
A probe may achieve high performance even on representations that are not causally relevant for the taskclaim0.789
Key interpretive claim from Case Study II distinguishing probe accuracy from causal relevance
Investigating the causal substructure of neural representations is necessary to avoid misidentifying data structures of simpler representations as abstract conceptsclaim0.776
Motivated by the finding that lexical entailment decomposes into word identities.
Direct probes over learned activations in standard basis may fail to reveal the actual causal role of representations because they are highly distributedclaim0.771
Supported by the finding that non-trivial rotations are required to find aligned representations.
Even if a case successfully meets all three criteria, this does not necessarily indicate that the corresponding sequence of representations is conscious. Rather, it suggests the observation of a potential 'consciousness' phenomenon within these representations — nothing more.quote0.758
Load-bearing epistemic caution the author places on the entire analytical framework.
How does representation geometry causally drive model behavior?question0.757
The central scientific question the paper addresses through the lens of interventional causality.
Simple difference-in-mean probes generalize as well as other probing techniques while identifying directions which are more causally implicated in model outputsclaim0.756
Key methodological claim: MM probes are both competitive in accuracy and superior in causal influence
Any system that persists must minimise surprisal, thereby gathering evidence for its own generative model.quote0.754
Opening sentence defining self-evidencing.