Core Insights - The article discusses the recent announcements from NeurIPS 2025, focusing on advancements in autonomous driving, visual perception reasoning, large model training, embodied intelligence, reinforcement learning, video understanding, and code generation [1]. Autonomous Driving - The article highlights various research papers related to autonomous driving, including "FutureSightDrive" and "AutoVLA," which explore visual reasoning and end-to-end driving models [2][4]. - A collection of papers and codes from institutions like Alibaba, UCLA, and Tsinghua University is provided, showcasing the latest developments in the field [6][7][13]. Visual Perception Reasoning - The article mentions "SURDS," which benchmarks spatial understanding and reasoning in driving scenarios using vision-language models [11]. - It also references "OmniSegmentor," a flexible multi-modal learning framework for semantic segmentation [16]. Large Model Training - The article discusses advancements in large model training, including papers on scaling offline reinforcement learning and fine-tuning techniques [40][42]. - It emphasizes the importance of adaptive methods for improving model performance in various applications [44]. Embodied Intelligence - Research on embodied intelligence is highlighted, including "Self-Improving Embodied Foundation Models" and "ForceVLA," which enhance models for contact-rich manipulation [46][48]. Video Understanding - The article covers advancements in video understanding, particularly through the "PixFoundation 2.0" project, which investigates the use of motion in visual grounding [28][29]. Code Generation - The article mentions developments in code generation, including "Fast and Fluent Diffusion Language Models" and "Step-By-Step Coding for Improving Mathematical Olympiad Performance" [60].