Researchers have long claimed that induction heads—attention mechanisms enabling in-context learning in transformers—need multiple layers. But a mechanistic interpretability researcher on LessWrong is challenging that. Using synthetic datasets, they're asking: could induction heads emerge from just two attention heads within a single layer? The question matters because it probes something fundamental—whether architectural limitations are inherent or just how we've built things. The author is refreshingly candid: there's often a gap between exciting claims and rigorous evidence. Mechanistic interpretability is empirical work; you have to get your hands dirty. This piece does exactly that—testing conventional wisdom and staying open to correction. The details are technical, but the lesson is universal: rigorously question what you're told, even by authoritative sources.

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