ChronoVision is a multimodal framework for temporal visual reasoning that supplements language-based reasoning with latent visual-state reconstruction. During supervised fine-tuning, a Reconstructive Visual Head predicts the latent representation of the final transformed state, while an ROI Attention Locating module identifies relevant visual evidence through semantic span queries. Post-training uses reinforcement learning with rewards for answer correctness, latent process alignment, and unsupervised visual focus. The authors introduce Vbvr-VQA, an image-ordering benchmark, and report 74.8% in-domain, 71.6% out-of-domain, and 55.0% on IntPhys2 accuracy.
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