Papers

📄 **[Read on arXiv](https://arxiv.org/abs/2505.09388)** Qwen3, developed by the Qwen team at Alibaba, represents a major step forward in open-weight language models by offering a comprehensive family spanning both dense…

📄 **[Read on arXiv](https://arxiv.org/abs/2503.19755)** ORION bridges the reasoning-action gap in driving VLAs through a three-component architecture consisting of QT-Former (visual encoding), an LLM reasoning core, and…

📄 **[Read on arXiv](https://arxiv.org/abs/2503.20020)** Gemini Robotics introduces a family of AI models built on Gemini 2.0 designed to extend advanced multimodal capabilities into physical robotics. The work addresses…

📄 **[Read on arXiv](https://arxiv.org/abs/2507.06261)** Gemini 2.5 is Google's frontier multimodal model family, built on a sparse Mixture-of-Experts (MoE) Transformer architecture. It represents a major advance in reas…

📄 **[Read on arXiv](https://arxiv.org/abs/2501.12948)** DeepSeek-R1 demonstrates that sophisticated reasoning capabilities -- including self-verification, reflection, and extended chain-of-thought -- can emerge in large…

📄 **[Read on arXiv](https://arxiv.org/abs/2506.13757)** AutoVLA presents a unified approach to autonomous driving that integrates vision, language understanding, and action generation within a single autoregressive mode…

Bo Jiang, Shaoyu Chen, Qian Zhang, Wenyu Liu, Xinggang Wang, arXiv, 2025. 📄 **[Read on arXiv](https://arxiv.org/abs/2503.07608)** AlphaDrive is the first application of GRPO (Group Relative Policy Optimization) reinforc…

[Read on arXiv](https://arxiv.org/abs/2407.08693) ECoT (UC Berkeley / Stanford / University of Warsaw, 2024) introduces Embodied Chain-of-Thought reasoning for Vision-Language-Action (VLA) models, demonstrating that gen…

📄 **[Read on arXiv](https://arxiv.org/abs/2402.01817)** This paper by Subbarao Kambhampati and colleagues at Arizona State University addresses one of the most important questions in modern AI: can large language models…

📄 **[Read on arXiv](https://arxiv.org/abs/2312.14150)** DriveLM formalizes driving reasoning as Graph Visual Question Answering (GVQA), where QA pairs are connected via logical dependencies forming a reasoning graph tha…

📄 **[Read on arXiv](https://arxiv.org/abs/2311.10813)** Agent-Driver reframes autonomous driving as a cognitive agent problem, positioning a large language model as the central reasoning and planning engine rather than…

📄 **[Read on arXiv](https://arxiv.org/abs/2305.10601)** Language models are typically used in a left-to-right token-generation mode, which limits their ability to explore alternative reasoning paths or backtrack from mi…

📄 **[Read on arXiv](https://arxiv.org/abs/2312.03661)** Reason2Drive provides the largest reasoning chain dataset for driving (>600K video-text pairs from nuScenes, Waymo, and ONCE) and introduces an aggregated evaluati…

📄 **[Read on arXiv](https://arxiv.org/abs/2210.03629)** Large language models had demonstrated two powerful capabilities in isolation: chain-of-thought reasoning for multi-step problem solving, and action generation for…

📄 **[Read on arXiv](https://arxiv.org/abs/2310.01415)** GPT-Driver reformulates autonomous driving motion planning as a language modeling problem. Scene context (object positions, velocities, lane geometry) and ego vehi…

📄 **[Read on arXiv](https://arxiv.org/abs/2201.11903)** Wei et al., arXiv 2201.11903, 2022 (NeurIPS 2022). - [Paper](https://arxiv.org/abs/2201.11903) Chain-of-thought (CoT) prompting demonstrates that including interme…