The argument that interpretability belongs in the design, not the post-mortem
Circuit tracing and activation patching give causal insight into failures that behavioural testing cannot see — including deceptive reasoning. The proposal is to treat that as an architectural requirement rather than a tool you reach for afterwards.
Recent work argues that interpretability, and mechanistic interpretability in particular, should be treated as a design principle for alignment rather than an auxiliary diagnostic. The supporting claim: techniques like circuit tracing and activation patching yield causal insight into internal failures — including deceptive or misaligned reasoning — that behavioural methods may not surface at all.
The distinction is not rhetorical. A diagnostic is something applied to a finished artifact, and what it can see is limited by choices already frozen. A design principle constrains the artifact while it is being built: representations kept separable, structure that stays legible under scale, training procedures that do not destroy the properties you will later need to inspect.
The deception argument is the strongest part of the case and the hardest to act on. A model that produces correct-looking output for reasons unrelated to correctness passes behavioural evaluation by construction — that is what the failure mode means. Only internal evidence can distinguish the two, which makes internal legibility a prerequisite rather than a nicety.
The cost is where this proposal will be contested. Every architectural constraint imposed for legibility is a constraint not available for capability, and no lab has demonstrated that an interpretability-constrained model can be trained to the frontier without paying for it. That trade has been asserted more often than measured.
It arrives while the same question is being decided in regulation, where obligations to explain a decision are being written on the assumption that explanation is available.
Lexsi.ai — Interpretability as Alignment: Making Internal Understanding a Design Principle → · Zylos Research — AI Safety, Alignment, and Interpretability in 2026 →