ChronoVision: Temporal Reasoning via Latent State Reconstruction
For researchers in multimodal AI and video understanding, this work addresses the bottleneck of temporal reasoning in visual cognitive tasks, offering a new benchmark and method that shows strong cross-domain generalization.
ChronoVision introduces a multimodal framework that improves temporal reasoning in video understanding by reconstructing latent visual states and using reinforcement learning with process grounding. It achieves state-of-the-art results on the new Vbvr-VQA benchmark (74.8% in-domain, 71.6% out-of-domain) and 55.0% on IntPhys2.
Multimodal large language models excel at passive perception but struggle with complex visual cognitive tasks requiring multi-step temporal reasoning. This degradation largely stems from the inherent ambiguity of language-based reasoning, which often fails to accurately articulate continuous visual transformations. To address this, we propose ChronoVision, a multimodal framework designed to align visual logic with latent imagery. During supervised fine-tuning, a Reconstructive Visual Head predicts the latent representation of the final transformed state, while an ROI Attention Locating module focuses the model on key visual evidence via semantic span queries. In post-training, we apply reinforcement learning with an implicit process grounding mechanism, guided by a composite reward function that evaluates outcome correctness, latent process alignment, and unsupervised visual focus. Furthermore, we introduce Vbvr-VQA, a novel dataset that evaluates temporal tracking by reformulating video reasoning into a strict image-ordering task. Experiments demonstrate that ChronoVision achieves state-of-the-art performance on Vbvr-VQA with 74.8% in-domain and 71.6% out-of-domain accuracy, alongside a strong 55.0% accuracy on IntPhys2, a highly challenging cross-domain benchmark.