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.

Traditional autonomous driving systems have mainly focused on making driving decisions without human interaction, overlooking human-like decision-making and human preference required in complex traffic scenarios. To bridge this gap, we introduce a novel framework leveraging Large Language Models (LLMs) for learning human-centered driving decisions from diverse simulation scenarios and environments that incorporate human feedback. Our contributions include a GPT-4-based programming planner that integrates seamlessly with the existing CARLA simulator to understand traffic scenes and react to human instructions. Specifically, we build a human-guided learning pipeline that incorporates human driver feedback directly into the learning process and stores optimal driving programming policy using Retrieval Augmented Generation (RAG). Impressively, our programming planner, with only 50 saved code snippets, can match the performance of baseline extensively trained reinforcement learning (RL) models. Our paper highlights the potential of an LLM-powered shared-autonomy system, pushing the frontier of autonomous driving system development to be more interactive and intuitive.