Same Maps, Different Moves: Why LLMs Can Converge Without Understanding
Meetings are useful theatre. Everyone can nod at the same slide, repeat the same market keywords, and still leave the room with incompatible plans. The agreement was real. The shared understanding was not. Large language models may be doing something uncomfortably similar. The paper Convergence Without Understanding: When Language Models Agree on Representations but Disagree on Reasoning studies whether models that look similar internally are actually reasoning in similar ways.1 This matters because a tempting story has been building around representational convergence: as models scale, their internal representations become more alike, perhaps because they are converging toward a shared statistical model of reality. That story is elegant. It is also a little too convenient, which is usually where expensive mistakes begin. ...