The Scaling Properties of Implicit Deductive Reasoning in Transformers
Authors: name: Enrico Vompa, name: Tanel Tammet
Organization: Applied AI group of the Tallinn University of Technology

We investigate the scaling properties of implicit deductive reasoning over Horn clauses in depth-bounded Transformers. By systematically decorrelating provability from spurious features and enforcing algorithmic alignment, we find that in sufficiently deep models with a bidirectional prefix mask, implicit reasoning approaches explicit CoT performance across graph topologies and problem widths, though CoT remains necessary for depth extrapolation.