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Our workshop focuses on research at the intersection of event-based sensing and multimodal vision, spanning the full pipeline from sensing systems and low-level imaging to perception and high-level understanding.

Pre-trained generative models are built using massive, typically unlabeled corpora, enabling them to capture broad, generic knowledge across diverse domains. However, at inference time, we often aim to adjust and customize these models — to exert control, enhance specific capabilities, and align their behavior with user intent and preferences. Post-training techniques have therefore emerged as both a practical necessity and an accessible means of adapting these powerful, yet static, models. This tutorial surveys the state-of-the-art in post-training methods for diffusion models, analyzing their strengths, limitations, and areas of application. We conclude with a critical discussion on the boundaries of post-training — asking whether fundamental semantic malfunctions can truly be resolved without revisiting the pretraining process.

The next challenge for intelligent systems is not only perception but also anticipating and acting in physical space under interventions and uncertainty. World models support simulation, data generation, and planning by rolling out futures conditioned on observations and actions, in applications such as robot manipulation, autonomous driving, and embodied navigation.

Yet recent rapid progress in world models has outpaced evaluation: beyond visual fidelity, we must assess controllability, physical plausibility, and robustness to distribution shift. As world models are getting embedded in larger systems and are effectively used in-the-loop for planning or training and evaluating perception and control policies, open-loop benchmarks provide limited insight. In such settings, errors compound, and missing controllability, physical plausibility, or robustness under distribution shift directly translate into inaccurate rollouts, suboptimal decisions, degraded downstream performance, or potentially catastrophic errors in safety-critical applications. Such properties are task-dependent and, thus, require task-specific evaluation criteria.