Representation as a Bottleneck for Mechanistic Interpretability: The Manifestation Unit Protocol

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[Submitted on 30 Jun 2026]

Title:Representation as a Bottleneck for Mechanistic Interpretability: The Manifestation Unit Protocol

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Abstract:Mechanistic interpretability has produced a rich inventory of component-level analyses that characterise what neural-network components encode and how they interact. Their outputs, however, are not easily reusable: selectivity tables, circuit diagrams, and feature lists remain locked in per-study notebooks - non-composable, not queryable in natural language, and not directly actionable for downstream audit or intervention. We study the representation layer that sits between these analyses and downstream use as a bottleneck that can be evaluated independently, and introduce Manifestation Units, a typed tuple protocol (E, S, R, D, G) extended with attention-head primitives (T) for transformer architectures, organising per-component statistics into structured fields populated automatically and queried through hybrid retrieval. Instantiated across generative vision (beta-VAE), discriminative vision (CNN), and language (GPT-2), the protocol supports two findings: typed structure substantially outperforms unstructured baselines on retrieval, and CNN filters retrieved by the schema satisfy causal sufficiency and necessity criteria under matched-budget controls. The schema absorbs attention-head primitives without modification, set-recovers known IOI circuit members under retrieval-budget-matched controls, and reveals an irreducible two-field core (S+R) with remaining fields either redundant or actively interfering. We present this as schema infrastructure for mechanistic interpretability rather than frontier-scale validation.

Comments: 65 pages. Interactive demos: this https URL , this https URL

Subjects:

Machine Learning (cs.LG)

Cite as: arXiv:2607.00089 [cs.LG]

(or arXiv:2607.00089v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.00089

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Hussein Chouman [view email] [v1] Tue, 30 Jun 2026 19:26:11 UTC (7,229 KB)

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