"The Reasoning Trap." The line the authors won't quite say out loud is that the smarter your model gets at reasoning, the more likely it is to fabricate a tool that doesn't exist. We've spent eighteen months telling ourselves that smarter reasoning would fix the reliability problem in agents. The paper shows the opposite. Reinforcement-learned reasoning lifts task scores and amplifies tool hallucination at the same time. They don't trade off. They move together. I've been seeing this for months at Upswing and didn't have a name for it. We run tool-using agents across hospitality ops — pricing, IoT telemetry, guest comms. The smarter models we tested were better at staying on task. The catch was their failure mode. When they got stuck, they got more confident about it. The dumber models would say "I can't do this." The smart ones would synthesize a plausible-sounding call to a function we'd never written. They didn't fail loud. They invented their way out.…