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: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:2509.19839v1 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: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:2509.19800v1 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:2509.20190v1 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:2509.19783v1 Announce Type: new Abstract: The inherent non-deterministic nature of autonomous agents, particularly within low-code/no-code (LCNC) environments, presents significant reliability challenges. Agents can become trapped in unforeseen loops, generate inaccurate outputs, or encounter unrecoverable failures, leading to user frustration and…
arXiv:2509.19464v1 Announce Type: new Abstract: Policy evaluation is often a prerequisite for deploying safety- and performance-critical systems. Existing evaluation approaches frequently suffer from high variance due to limited data and long-horizon tasks, or high bias due to unequal support or…
arXiv:2509.19762v1 Announce Type: new Abstract: Modern LLM reasoning relies on extensive test-time computation, driven by internal model training and external agentic orchestration. However, this synergy is often inefficient, as model verbosity and poor instruction following lead to wasted compute. We…
arXiv:2507.08540v3 Announce Type: replace-cross Abstract: The proliferation of software vulnerabilities presents a significant challenge to cybersecurity, necessitating more effective detection methodologies. We introduce White-Basilisk, a novel approach to vulnerability detection that demonstrates superior performance while challenging prevailing assumptions in AI…
