MERC-WM: Mechanism-Event Replay Cards for Task-Free Continual World Models

World models deployed in physical systems encounter a stream in which camera appearance, contact dynamics, payload, and actuator response change without task labels. Updating on every prediction error wastes adaptation capacity on harmless visual variation, whereas ignoring errors caused by changed dynamics yields unsafe plans. We formulate this ambiguity as task-free continual mechanism identification and propose MERC-WM, a continual world-model framework that separates observation shifts from mechanism shifts before updating its dynamics. MERC-WM combines paired low-risk probes, a frozen visual representation, a bank of low-rank dynamics experts, and a bounded memory of mechanism-event replay cards. Each card stores a compact before/after latent effect, uncertainty, and expert identity rather than raw trajectories. At test time, retrieved cards provide a prior over the active mechanism; the world model either reuses an existing expert or creates a new one only when probe evidence remains inconsistent. We specify a reproducible evaluation across contact-rich manipulation streams with appearance changes, friction and payload changes, and mechanism recurrence. Across three manipulation domains and five stream seeds, MERC-WM reaches 80.6% average success, improves backward transfer from −3.2 to −0.6 percentage points relative to latent replay, and reduces visual-shift false expansion from 37.5% to 8.6% using 18 MB of auxiliary memory. It also raises mechanism-event F1 from 73.0% to 87.9% and shortens recurrence recovery from 353 to 147 interactions.