Core Insights - The article emphasizes the increasing importance of foundational models such as LLM (Large Language Models), VLM (Vision-Language Models), and VLA (Vision-Language-Action Models) in autonomous driving decision-making, attracting significant attention from both academia and industry [2]. Summary by Categories LLM-Based Approaches - LLM-based methods leverage the reasoning capabilities of large models to describe autonomous driving, marking the early stages of integration between autonomous driving and large models [4]. - Notable research includes: - "Distilling Multi-modal Large Language Models for Autonomous Driving" - "LearningFlow: Automated Policy Learning Workflow for Urban Driving with Large Language Models" - "CoT-Drive: Efficient Motion Forecasting for Autonomous Driving with LLMs and Chain-of-Thought Prompting" - "PADriver: Towards Personalized Autonomous Driving" [4][5]. VLM-Based Approaches - VLM and VLA algorithms are currently mainstream due to the reliance on visual sensors in autonomous driving. The article summarizes the latest works in this area for reference and learning [8]. - Key studies include: - "Drive-R1: Bridging Reasoning and Planning in VLMs for Autonomous Driving with Reinforcement Learning" - "FutureSightDrive: Visualizing Trajectory Planning with Spatio-Temporal CoT for Autonomous Driving" [8][9]. VLA-Based Approaches - VLA methods focus on integrating vision, language, and action for end-to-end autonomous driving, emphasizing adaptive reasoning and reinforcement fine-tuning [17]. - Significant contributions include: - "AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning" - "DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving" [17][21].

Core Viewpoint - The article discusses the application of Large Language Models (LLM) in stock research, focusing on factor mining and stock review processes to enhance investment research efficiency and effectiveness [1][6]. Factor Mining Framework - The design of prompts is crucial in guiding the direction of factor creation within the LLM-based framework, impacting the probability of generating high IC factors [2][16]. - Factors generated using LLM have a strong interpretability compared to machine learning factors, and innovative operators can optimize existing factors, achieving an IC_IR of 0.78 during backtesting [3][19]. - The LLM can create new factors that are less correlated with existing ones, enhancing the diversity of investment strategies [20][23]. Stock Review System - The LLM-based stock review system extracts key information from various sources, significantly improving the efficiency of stock reviews by over 70% compared to traditional methods [4][27]. - The system utilizes a retrieval-augmented generation (RAG) approach to compare current information with historical data, providing initial assessments of stock price impacts [25][31]. - The review process can yield valuable insights, although the depth of analysis may be limited, necessitating improvements in prompt design and input quality [30][34]. Performance and Effectiveness - The LLM's stock review framework has shown promising results in predicting stock price movements, particularly with long-term scoring metrics indicating potential future performance [35][37]. - A simple long-only timing strategy based on LLM-generated scores has demonstrated the ability to capture upward price movements effectively, improving annualized returns and reducing maximum drawdown [42][43].