PREBA: Surgical Duration Prediction via PCA-Weighted Retrieval-Augmented LLMs and Bayesian Averaging Aggregation
arXiv:2603.13275v1 Announce Type: new Abstract: Accurate prediction of surgical duration is pivotal for hospital resource management. Although recent supervised learning approaches-from machine learning (ML) to fine-tuned large language models (LLMs)-have shown strong performance, they remain constrained by the need for...
ICPRL: Acquiring Physical Intuition from Interactive Control
arXiv:2603.13295v1 Announce Type: new Abstract: VLMs excel at static perception but falter in interactive reasoning in dynamic physical environments, which demands planning and adaptation to dynamic outcomes. Existing physical reasoning methods often depend on abstract symbolic inputs or lack the...
Enhanced Atrial Fibrillation Prediction in ESUS Patients with Hypergraph-based Pre-training
arXiv:2603.13297v1 Announce Type: new Abstract: Atrial fibrillation (AF) is a major complication following embolic stroke of undetermined source (ESUS), elevating the risk of recurrent stroke and mortality. Early identification is clinically important, yet existing tools face limitations in accuracy, scalability,...
Evidence-based Distributional Alignment for Large Language Models
arXiv:2603.13305v1 Announce Type: new Abstract: Distributional alignment enables large language models (LLMs) to predict how a target population distributes its responses across answer options, rather than collapsing disagreement into a single consensus answer. However, existing LLM-based distribution prediction is often...
Preventing Curriculum Collapse in Self-Evolving Reasoning Systems
arXiv:2603.13309v1 Announce Type: new Abstract: Self-evolving reasoning frameworks let LLMs improve their reasoning capabilities by iteratively generating and solving problems without external supervision, using verifiable rewards. Ideally, such systems are expected to explore a diverse problem space and propose new...
Feature-level Interaction Explanations in Multimodal Transformers
arXiv:2603.13326v1 Announce Type: new Abstract: Multimodal Transformers often produce predictions without clarifying how different modalities jointly support a decision. Most existing multimodal explainable AI (MXAI) methods extend unimodal saliency to multimodal backbones, highlighting important tokens or patches within each modality,...
RBF-Solver: A Multistep Sampler for Diffusion Probabilistic Models via Radial Basis Functions
arXiv:2603.13330v1 Announce Type: new Abstract: Diffusion probabilistic models (DPMs) are widely adopted for their outstanding generative fidelity, yet their sampling is computationally demanding. Polynomial-based multistep samplers mitigate this cost by accelerating inference; however, despite their theoretical accuracy guarantees, they generate...
Lipschitz-Based Robustness Certification Under Floating-Point Execution
arXiv:2603.13334v1 Announce Type: new Abstract: Sensitivity-based robustness certification has emerged as a practical approach for certifying neural network robustness, including in settings that require verifiable guarantees. A key advantage of these methods is that certification is performed by concrete numerical...
AI-Driven Predictive Maintenance with Real-Time Contextual Data Fusion for Connected Vehicles: A Multi-Dataset Evaluation
arXiv:2603.13343v1 Announce Type: new Abstract: Most vehicle predictive maintenance systems rely exclusively on internal diagnostic signals and are validated on deterministic synthetic data, limiting the credibility of reported metrics. This paper presents a simulation-validated proof-of-concept framework for V2X-augmented predictive maintenance,...
Thermal Robustness of Retrieval in Dense Associative Memories: LSE vs LSR Kernels
arXiv:2603.13350v1 Announce Type: new Abstract: Understanding whether retrieval in dense associative memories survives thermal noise is essential for bridging zero-temperature capacity proofs with the finite-temperature conditions of practical inference and biological computation. We use Monte Carlo simulations to map the...
Justices will hear argument on Trump administration’s removal of protected status for Syrian and Haitian nationals
The Supreme Court announced on Monday afternoon that it will hear oral argument on whether the Trump administration can end a program that allows several thousand Syrians and approximately 350,000 […]The postJustices will hear argument on Trump administration’s removal of...
Haitian nationals ask court to deny Trump administration’s request to remove their protected status
A group of Haitian nationals urged the Supreme Court on Monday to leave in place a ruling by a federal judge in Washington, D.C., that allows them to stay in […]The postHaitian nationals ask court to deny Trump administration’s request...
Elon Musk's xAI sued for turning three girls' real photos into AI CSAM
Discord user led cops to Grok-generated CSAM of real girls, lawsuit says.
Trump and his FCC chair demand more positive news coverage of Iran war
Carr makes evidence-free claim of "hoaxes and news distortions." Trump is thrilled.
No accountability: Bills would ban liability lawsuits for climate change
This is the latest front in the battle over climate lawsuits.
Elon Musk’s xAI faces child porn lawsuit from minors Grok allegedly undressed
The three plaintiffs are seeking to represent anyone who had real images of them as a minor altered into sexual content by Grok.
The dictionary sues OpenAI
Encyclopedia Britannica and Merriam-Webster say that OpenAI violated the copyright of almost 100,000 articles by using them for LLM training.
Context is all you need: Towards autonomous model-based process design using agentic AI in flowsheet simulations
arXiv:2603.12813v1 Announce Type: new Abstract: Agentic AI systems integrating large language models (LLMs) with reasoning and tooluse capabilities are transforming various domains - in particular, software development. In contrast, their application in chemical process flowsheet modelling remains largely unexplored. In...
A Geometrically-Grounded Drive for MDL-Based Optimization in Deep Learning
arXiv:2603.12304v1 Announce Type: cross Abstract: This paper introduces a novel optimization framework that fundamentally integrates the Minimum Description Length (MDL) principle into the training dynamics of deep neural networks. Moving beyond its conventional role as a model selection criterion, we...
Detecting Miscitation on the Scholarly Web through LLM-Augmented Text-Rich Graph Learning
arXiv:2603.12290v1 Announce Type: cross Abstract: Scholarly web is a vast network of knowledge connected by citations. However, this system is increasingly compromised by miscitation, where references do not support or even contradict the claims they are cited for. Current miscitation...
Generating Expressive and Customizable Evals for Timeseries Data Analysis Agents with AgentFuel
arXiv:2603.12483v1 Announce Type: new Abstract: Across many domains (e.g., IoT, observability, telecommunications, cybersecurity), there is an emerging adoption of conversational data analysis agents that enable users to "talk to your data" to extract insights. Such data analysis agents operate on...
Efficient and Interpretable Multi-Agent LLM Routing via Ant Colony Optimization
arXiv:2603.12933v1 Announce Type: new Abstract: Large Language Model (LLM)-driven Multi-Agent Systems (MAS) have demonstrated strong capability in complex reasoning and tool use, and heterogeneous agent pools further broaden the quality--cost trade-off space. Despite these advances, real-world deployment is often constrained...
The DIME Architecture: A Unified Operational Algorithm for Neural Representation, Dynamics, Control and Integration
arXiv:2603.12286v1 Announce Type: cross Abstract: Modern neuroscience has accumulated extensive evidence on perception, memory, prediction, valuation, and consciousness, yet still lacks an explicit operational architecture capable of integrating these phenomena within a unified computational framework. Existing theories address specific aspects...
Developing and evaluating a chatbot to support maternal health care
arXiv:2603.13168v1 Announce Type: new Abstract: The ability to provide trustworthy maternal health information using phone-based chatbots can have a significant impact, particularly in low-resource settings where users have low health literacy and limited access to care. However, deploying such systems...
An ethical framework for conversational AI in higher education: toward an evidence-based ethical governance
Budget-Sensitive Discovery Scoring: A Formally Verified Framework for Evaluating AI-Guided Scientific Selection
arXiv:2603.12349v1 Announce Type: cross Abstract: Scientific discovery increasingly relies on AI systems to select candidates for expensive experimental validation, yet no principled, budget-aware evaluation framework exists for comparing selection strategies -- a gap intensified by large language models (LLMs), which...
SPARROW: Learning Spatial Precision and Temporal Referential Consistency in Pixel-Grounded Video MLLMs
arXiv:2603.12382v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have advanced from image-level reasoning to pixel-level grounding, but extending these capabilities to videos remains challenging as models must achieve spatial precision and temporally consistent reference tracking. Existing video MLLMs...
Operationalising Cyber Risk Management Using AI: Connecting Cyber Incidents to MITRE ATT&CK Techniques, Security Controls, and Metrics
arXiv:2603.12455v1 Announce Type: cross Abstract: The escalating frequency of cyber-attacks poses significant challenges for organisations, particularly small enterprises constrained by limited in-house expertise, insufficient knowledge, and financial resources. This research presents a novel framework that leverages Natural Language Processing to...
Shattering the Shortcut: A Topology-Regularized Benchmark for Multi-hop Medical Reasoning in LLMs
arXiv:2603.12458v1 Announce Type: cross Abstract: While Large Language Models (LLMs) achieve expert-level performance on standard medical benchmarks through single-hop factual recall, they severely struggle with the complex, multi-hop diagnostic reasoning required in real-world clinical settings. A primary obstacle is "shortcut...
The Perfection Paradox: From Architect to Curator in AI-Assisted API Design
arXiv:2603.12475v1 Announce Type: cross Abstract: Enterprise API design is often bottlenecked by the tension between rapid feature delivery and the rigorous maintenance of usability standards. We present an industrial case study evaluating an AI-assisted design workflow trained on API Improvement...