TRACE-WM: Learning Physical Concurrency for Asynchronous World Models

World models trained from asynchronous interaction logs can overfit arbitrary action serializations: physically interchangeable actions appear as different futures, while global order invariance can erase interactions for which order genuinely matters. We study whether a world model can instead infer where temporal order is physically irrelevant. We formulate state-dependent action swappability through a stochastic two-step diamond discrepancy and introduce Active Diamond Discovery (ADD), an active probing framework for uncertain likely-safe diamonds, together with TRACE-WM, which imposes invariance only on relation-admitted reorderings. In a fresh pre-specified 16-seed comparison, a local-uncertainty ADD acquisition discovers 96.19 safe cross-robot probes versus 54.19 for Random, a 77.5% gain with 16/16 paired wins, and exceeds the original variance-reduction instantiation by 28.0%. In a separate 16-seed matched held-out-state evaluation, TRACE-WM reduces balanced distributional prediction error by 34.0% relative to Ordered and dependent order-effect error by 57.2% relative to Global invariance. A size-matched, common-initialization control shows that ADD-selected relation elements are modestly more useful downstream than Random-selected elements, while extra imperfect coverage can offset this benefit. The discovery advantage also persists under a discontinuous hard-mutex interaction. Relation extrapolation remains imperfect, so the learned relation is treated as an empirical inference rather than a universal safety certificate.