Boundary Suppression Asymmetry in Post-trained Assistants: Over-expansion as a Controllability Cost

ArXi:2605.27969v1 Announce Type: new Post-trained language-model assistants are often optimized to avoid under-answering, encouraging complete, helpful, cautious, and proactive responses. We ask whether this optimization creates asymmetric controllability costs: when users explicitly request narrower answers, which assistant behaviors remain suppressible, and which continue to shape the response? We study this problem as boundary-suppression asymmetry.