StressDream Steers Diffusion World Models Toward High-Impact Robot Policy Failures

StressDream optimizes the initial noise of diffusion-based video world models at inference time to generate high-impact but plausible outcomes, enabling more efficient stress-testing of robot policies without requiring prohibitively large sample counts. The method targets rare, scene-dependent failure events that nominal imagination sampling tends to miss.