Ehrenfeucht-Haussler Rank and Chain of Thought
Pablo Barceló, Alexander Kozachinskiy, Tomasz Steifer; 27(185):1−41, 2026.
Abstract
The notion of rank of a Boolean function has been a cornerstone in PAC learning, enabling quasipolynomial-time learning algorithms for polynomial-size decision trees. We present a novel characterization of rank, grounded in the well-known Transformer architecture. We show that the rank of a function $f$ corresponds to the minimum number of Chain of Thought (CoT) iterations required by a single-layer Transformer with hard attention to compute $f$. Based on this characterization, we establish tight bounds on the number of CoT iterations required for specific problems, showing that \(\ell\)-fold function composition necessitates exactly \(\ell\) CoT iterations. Furthermore, we analyze the problem of identifying the position of the \(k\)-th occurrence of 1 in a Boolean sequence, proving that it requires \(k\) CoT iterations. Finally, we introduce the notion of the multi-head rank that captures multi-head single-layer transformers, and perform the analysis of PAC-learnability of the classes of functions with bounded multi-head rank.
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