CausalWAM: Mechanism-first Causal World-Action Models

Embodied agents often act in environments where the consequences of actions are governed by hidden mechanisms, such as modes, latches, timers, thresholds, rates, and delayed triggers. Existing imitation policies, world models, and emerging world-action models learn powerful action and prediction models, but typically leave these mechanisms implicit, making them difficult to track, intervene on, reuse, or revise. We introduce a mechanism-first causal world-action model that infers a mixed discrete-continuous belief over hidden mechanisms from observation-action history. The model plans by selecting mechanism-level target interventions, such as unlocking a mode or driving an accumulator past a threshold, before grounding those targets into low-level actions with a WAM-style action decoder. It further maintains an external mechanism library that stores reusable local transition fragments and retrieves them for belief inference, planning, compositional transfer, and online adaptation. To evaluate this capability, we introduce MechanismBench, a DSL-generated benchmark that provides hidden mechanism state, counterfactual interventions, compositional splits, nuisance shifts, and rule changes. On MechanismBench, our full model improves overall success from 75.2% for a WAM-style baseline to 88.9%, counterfactual accuracy from 71.2% to 88.3%, and held-out composition success from 46.3% to 76.2%. Under shifted mechanisms, the library-enabled model recovers to 85.4% success after ten interactions, compared to 58.4% without the library, and focused Franka experiments show the same pattern on physical hidden-mode, accumulator, and delayed-trigger fixtures.