arXiv:2509.20190v1 Announce Type: cross Abstract: In modern automotive development, security testing is critical for safeguarding systems against increasingly advanced threats. Attack trees are widely used to systematically represent potential attack vectors, but generating comprehensive test cases from these trees remains…
arXiv:2509.19800v1 Announce Type: new Abstract: There are two primary approaches to solving Markov decision problems (MDPs): dynamic programming based on the Bellman equation and linear programming (LP). Dynamic programming methods are the most widely used and form the foundation of…
arXiv:2509.20321v1 Announce Type: cross Abstract: Disfluencies — such as “um,” “uh,” interjections, parentheticals, and edited statements — remain a persistent challenge for speech-driven systems, degrading accuracy in command interpretation, summarization, and conversational agents. We introduce DRES (Disfluency Removal Evaluation Suite),…
arXiv:2509.19839v1 Announce Type: new Abstract: Achieving robust safety alignment in large language models (LLMs) while preserving their utility remains a fundamental challenge. Existing approaches often struggle to balance comprehensive safety with fine-grained controllability at the representation level. We introduce LATENTGUARD,…
arXiv:2503.23326v3 Announce Type: replace Abstract: Monte-Carlo Tree Search (MCTS) is a family of sampling-based search algorithms widely used for online planning in sequential decision-making domains and at the heart of many recent advances in artificial intelligence. Understanding the behavior of…
arXiv:2509.19925v1 Announce Type: new Abstract: As enterprises increasingly integrate cloud-based large language models (LLMs) such as ChatGPT and Gemini into their legal document workflows, protecting sensitive contractual information – including Personally Identifiable Information (PII) and commercially sensitive clauses – has…
arXiv:2407.10999v2 Announce Type: replace-cross Abstract: With the rapid development of large language models (LLM), the evaluation of LLM becomes increasingly important. Measuring text generation tasks such as summarization and article creation is very difficult. Especially in specific application domains (e.g.,…
arXiv:2509.20021v1 Announce Type: new Abstract: Embodied Artificial Intelligence (AI) is an intelligent system paradigm for achieving Artificial General Intelligence (AGI), serving as the cornerstone for various applications and driving the evolution from cyberspace to physical systems. Recent breakthroughs in Large…
arXiv:2502.17715v2 Announce Type: replace-cross Abstract: Generating diverse follow-up questions that uncover missing information remains challenging for conversational agents, particularly when they run on small, locally hosted models. To address this, we develop an information-gap-driven knowledge distillation pipeline in which a…
arXiv:2509.20067v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated notable potential in medical applications, yet they face substantial challenges in handling complex real-world clinical diagnoses using conventional prompting methods. Current prompt engineering and multi-agent approaches typically optimize isolated…
