Mechanistic Origin of Constraint-Induced Restructuring in LLMs

Determine whether the performance and reasoning changes induced by linguistic constraints such as E-Prime (which eliminates all forms of the English copula "to be") in large language models arise from activation of alternative internal reasoning pathways versus output-level variance or compliance overhead (statistical noise). This requires analyzing constrained versus unconstrained inference to ascertain whether distinct internal circuits are engaged when constraints are applied.

Background

The paper reports that linguistic constraints reshape reasoning in task- and model-dependent ways, with E-Prime producing volatile effects across models while No-Have shows more consistent benefits. The authors hypothesize that different models occupy different native "Umwelten" and that constraints may redirect cognition by activating alternative internal strategies rather than simply imposing an instruction-following burden.

They propose testing this by attention or activation analyses to see whether constrained reasoning invokes different internal circuits. A dramatic case is GPT-4o-mini’s epistemic collapse under E-Prime, which the authors suggest indicates removal of a load-bearing linguistic structure rather than mere noise—motivating a mechanistic investigation of the underlying cause.