Core Viewpoint - The European Union's newly released "General Artificial Intelligence Code of Conduct" introduces significant regulatory challenges for the automotive industry, particularly in the context of smart and connected vehicles [3][4]. Group 1: Regulatory Framework - The "Code" serves as an extension of the EU's "Artificial Intelligence Act," focusing on transparency, copyright, safety, and security for AI models used in the automotive sector [4]. - The Code will take effect on August 2, 2025, requiring companies to comply with regulations for AI models built before this date within two years, while models developed after must comply within one year [4]. - The EU adopts a strict risk-based regulatory model, categorizing AI applications into unacceptable, high, medium, and low-risk, with high-risk applications requiring pre-assessment and ongoing monitoring [4]. Group 2: Challenges for the Automotive Industry - Automotive companies must transition from "black box" decision-making to transparent compliance, particularly for Level 2+ autonomous driving systems, which must disclose algorithms, training data sources, and decision logic [5]. - Compliance costs are expected to rise, with estimates indicating a 15%-20% increase in the development costs of intelligent systems per vehicle due to the need for algorithm explainability and real-time monitoring systems [5]. - The automotive sector faces new challenges in copyright compliance and user data governance, necessitating renegotiation of licensing agreements with content copyright holders and ensuring compliance with the EU's General Data Protection Regulation (GDPR) [6]. Group 3: Business Model Innovation - The shift from "data-driven" to "compliance-driven" business models will impact over-the-air (OTA) updates, requiring prior notification to regulatory bodies for changes involving AI model parameters [7]. - Chinese automotive companies exporting to the EU must embed multi-regional compliance modules in their AI systems, ensuring data localization for the EU market [7]. Group 4: Strategic Responses - Automotive companies are advised to establish an AI compliance committee to oversee technical development, legal, and data security departments, and recruit professionals with expertise in EU AI regulations and GDPR [8]. - Long-term strategies should include partnerships with EU-certified open data platforms and content distributors to mitigate infringement risks and the development of lightweight, auditable AI models [9]. - Companies must balance technological innovation with regulatory compliance, as the Code may increase compliance costs but also drive responsible innovation in AI technology [9][10].

Core Insights - The research highlights the importance of evaluating the robustness of Retrieval-Augmented Generation (RAG) systems in real-world scenarios, particularly when faced with various types of noise and disturbances [2][3][17] - A new framework called Retrieval-Aware Robustness Evaluation (RARE) has been proposed to comprehensively assess the robustness of RAG systems [3][4][18] Group 1: RAG System Challenges - RAG systems are designed to enhance the accuracy and timeliness of large language models by utilizing an external memory repository for information retrieval [2] - Current evaluation methods often rely on static datasets that do not account for real-world complexities, leading to overly optimistic assessments of RAG systems [2][3] Group 2: RARE Framework Components - RARE consists of three main components: RARE-Met, RARE-Get, and RARE-Set, each addressing different aspects of robustness evaluation [3][4][5] - RARE-Met provides a set of metrics to measure RAG system performance under various disturbances, including query and document perturbations [5][6] - RARE-Get automates the generation of high-quality evaluation data, significantly improving the efficiency of creating specialized benchmarks [7][8][9] - RARE-Set is a large-scale benchmark dataset that includes over 400 time-sensitive documents across finance, economics, and policy, designed to test RAG systems in specialized contexts [10][11] Group 3: Experimental Findings - Extensive experiments conducted on the RARE-Set revealed that larger models generally exhibit better robustness, but model size alone does not determine performance [12][13][17] - RAG systems showed significant vulnerability to document perturbations, while query perturbations had a relatively smaller impact [16][17] - The robustness of RAG systems varied across different domains, with finance performing best and economics facing the most challenges [14][17] Group 4: Implications and Future Directions - The findings underscore the necessity for improved evaluation and enhancement of RAG system robustness, especially in real-world applications [17][18] - The RARE framework offers a new perspective for assessing RAG systems, paving the way for the development of more reliable systems capable of functioning effectively in noisy and dynamic environments [18]