Representation as a Bottleneck for Mechanistic Interpretability: The Manifestation Unit Protocol
arXiv:2607.00089v1 Announce Type: new 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 i
The proliferation of complex AI models necessitates more robust and standardized methods for understanding and auditing their internal workings.
Improving mechanistic interpretability is crucial for developing trustworthy, auditable, and ultimately more capable AI systems, impacting their deployment in critical applications.
This protocol introduces a structured approach to make mechanistic interpretability outputs more reusable and actionable, moving from bespoke analyses to standardized interfaces.
- · AI Safety Researchers
- · AI Developers
- · Auditors and Regulators
- · Developers of foundational models
- · Black-box AI systems
- · Organizations relying solely on performance metrics
The adoption of common interpretability protocols standardizes the audit and evaluation of neural networks.
Increased transparency and understanding of AI models accelerate their deployment in sensitive sectors like finance and defense.
Standardized mechanistic interpretability could lead to regulatory frameworks mandating a 'manifestation unit protocol' for all deployed AI.
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