arXiv:2603.15970v5 Announce Type: replace-cross Abstract: Several data warehouse and database providers have recently introduced extensions to SQL called AI Queries, enabling users to specify functions and conditions in SQL that are evaluated by LLMs, thereby broadening significantly the kinds of queries one can express over the combination of structured and unstructured data. LLMs offer remarkable […]
AeroTherm-GPT: A Verification-Centered LLM Framework for Thermal Protection System Engineering Workflows
arXiv:2604.01738v1 Announce Type: new Abstract: Integrating Large Language Models (LLMs) into hypersonic thermal protection system (TPS) design is bottlenecked by cascading constraint violations when generating executable simulation artifacts. General-purpose LLMs, treating generation as single-pass text completion, fail to satisfy the sequential, multi-gate constraints inherent in safety-critical engineering workflows. To address this, we propose AeroTherm-GPT, the […]
Towards Transparent and Efficient Anomaly Detection in Industrial Processes through ExIFFI
arXiv:2405.01158v4 Announce Type: replace-cross Abstract: Anomaly Detection (AD) is crucial in industrial settings to streamline operations by detecting underlying issues. Conventional methods merely label observations as normal or anomalous, lacking crucial insights. In Industry 5.0, interpretable outcomes become desirable to enable users to understand the rational under model decisions. This paper presents the first industrial […]
Not All Tokens See Equally: Perception-Grounded Policy Optimization for Large Vision-Language Models
arXiv:2604.01840v1 Announce Type: new Abstract: While Reinforcement Learning from Verifiable Rewards (RLVR) has advanced reasoning in Large Vision-Language Models (LVLMs), prevailing frameworks suffer from a foundational methodological flaw: by distributing identical advantages across all generated tokens, these methods inherently dilute the learning signals essential for optimizing the critical, visually-grounded steps of multimodal reasoning. To bridge […]
Exploring Effective Strategies for Building a User-Configured GPT for Coding Classroom Dialogues
arXiv:2506.07194v2 Announce Type: replace Abstract: This study investigated effective strategies for developing a custom GPT to code classroom dialogue. While classroom dialogue is widely recognised as a crucial element of education, its analysis remains challenging due to the need for a nuanced understanding of dialogic functions and the labour-intensive nature of manual transcript coding. Recent […]
Efficient Constraint Generation for Stochastic Shortest Path Problems
arXiv:2604.01855v1 Announce Type: new Abstract: Stochastic Shortest Path problems (SSPs) are traditionally solved by computing each state’s cost-to-go by applying Bellman backups. A Bellman backup updates a state’s cost-to-go by iterating through every applicable action, computing the cost-to-go after applying each one, and selecting a minimal action’s cost-to-go. State-of-the-art algorithms use heuristic functions; these give […]
Efficient Reasoning with Balanced Thinking
arXiv:2603.12372v3 Announce Type: replace Abstract: Large Reasoning Models (LRMs) have shown remarkable reasoning capabilities, yet they often suffer from overthinking, expending redundant computational steps on simple problems, or underthinking, failing to explore sufficient reasoning paths despite inherent capabilities. These issues lead to inefficiencies and potential inaccuracies, limiting practical deployment in resource-constrained settings. Existing methods to […]
BraiNCA: brain-inspired neural cellular automata and applications to morphogenesis and motor control
arXiv:2604.01932v1 Announce Type: new Abstract: Most of the Neural Cellular Automata (NCAs) defined in the literature have a common theme: they are based on regular grids with a Moore neighborhood (one-hop neighbour). They do not take into account long-range connections and more complex topologies as we can find in the brain. In this paper, we […]
APEX: Agent Payment Execution with Policy for Autonomous Agent API Access
arXiv:2604.02023v1 Announce Type: cross Abstract: Autonomous agents are moving beyond simple retrieval tasks to become economic actors that invoke APIs, sequence workflows, and make real-time decisions. As this shift accelerates, API providers need request-level monetization with programmatic spend governance. The HTTP 402 protocol addresses this by treating payment as a first-class protocol event, but most […]
Qiana: A First-Order Formalism to Quantify over Contexts and Formulas with Temporality
arXiv:2604.01952v1 Announce Type: new Abstract: We introduce Qiana, a logic framework for reasoning on formulas that are true only in specific contexts. In Qiana, it is possible to quantify over both formulas and contexts to express, e.g., that “everyone knows everything Alice says”. Qiana also permits paraconsistent logics within contexts, so that contexts can contain […]
Generative AI Spotlights the Human Core of Data Science: Implications for Education
arXiv:2604.02238v1 Announce Type: cross Abstract: Generative AI (GAI) reveals an irreducible human core at the center of data science: advances in GAI should sharpen, rather than diminish, the focus on human reasoning in data science education. GAI can now execute many routine data science workflows, including cleaning, summarizing, visualizing, modeling, and drafting reports. Yet the […]
SenseMath: Do LLMs Have Number Sense? Evaluating Shortcut Use, Judgment, and Generation
arXiv:2604.01988v1 Announce Type: new Abstract: Large language models often default to step-by-step computation even when efficient numerical shortcuts are available. This raises a basic question: do they exhibit number sense in a human-like behavioral sense, i.e., the ability to recognize numerical structure, apply shortcuts when appropriate, and avoid them when they are not? We introduce […]