World-Action-Retrospection Model for Consequence-Aligned Reuse of Experience

Local-context world–action models (WAMs) couple visual prediction with control but leave task-critical earlier interactions outside their decision context. Memory-augmented embodied policies extend access to history, yet retaining past information does not establish whether a recalled action remains appropriate. The central challenge is to use ongoing interaction as evidence for deciding which past behavior to reuse and how it should shape action generation. We introduce WARM (World–Action–Retrospection Model), organized around event-grounded retrospection: current context and situated recollection ground inference of a candidate-independent transition requirement, while a cross-episode sensorimotor repertoire supplies state–action–effect events. Adapted repertoire actions are assessed by comparing their predicted effects with this learned forecast; consequence-gated source transport then jointly regulates proposal-informed stochastic initialization and candidate-derived conditioning, turning recalled behavior into a revisable prior for flow-based action generation. WARM achieves 98.7% success on LIBERO and 83.9% on RMBench, including 87.3% on tasks requiring multiple past observations. Component comparisons suggest complementary benefits from recollection and repertoire; complete-event distraction tests show less degradation with explicit consequence comparison. These findings support using current evidence to govern both the selection of past behavior and its influence on action generation.