Real-Time AI Service Economy: A Framework for Agentic Computing Across the Continuum
arXiv:2603.05614v1 Announce Type: new Abstract: Real-time AI services increasingly operate across the device-edge-cloud continuum, where autonomous AI agents generate latency-sensitive workloads, orchestrate multi-stage processing pipelines, and compete for shared resources under policy and governance constraints. This article shows that the...
Omni-C: Compressing Heterogeneous Modalities into a Single Dense Encoder
arXiv:2603.05528v1 Announce Type: cross Abstract: Recent multimodal systems often rely on separate expert modality encoders which cause linearly scaling complexity and computational overhead with added modalities. While unified Omni-models address this via Mixture-of-Expert (MoE) architectures with specialized experts and routing,...
SAHOO: Safeguarded Alignment for High-Order Optimization Objectives in Recursive Self-Improvement
arXiv:2603.06333v1 Announce Type: new Abstract: Recursive self-improvement is moving from theory to practice: modern systems can critique, revise, and evaluate their own outputs, yet iterative self-modification risks subtle alignment drift. We introduce SAHOO, a practical framework to monitor and control...
DreamCAD: Scaling Multi-modal CAD Generation using Differentiable Parametric Surfaces
arXiv:2603.05607v1 Announce Type: cross Abstract: Computer-Aided Design (CAD) relies on structured and editable geometric representations, yet existing generative methods are constrained by small annotated datasets with explicit design histories or boundary representation (BRep) labels. Meanwhile, millions of unannotated 3D meshes...
Talk Freely, Execute Strictly: Schema-Gated Agentic AI for Flexible and Reproducible Scientific Workflows
arXiv:2603.06394v1 Announce Type: new Abstract: Large language models (LLMs) can now translate a researcher's plain-language goal into executable computation, yet scientific workflows demand determinism, provenance, and governance that are difficult to guarantee when an LLM decides what runs. Semi-structured interviews...
CBR-to-SQL: Rethinking Retrieval-based Text-to-SQL using Case-based Reasoning in the Healthcare Domain
arXiv:2603.05569v1 Announce Type: cross Abstract: Extracting insights from Electronic Health Record (EHR) databases often requires SQL expertise, creating a barrier for healthcare decision-making and research. While a promising approach is to use Large Language Models (LLMs) to translate natural language...
Artificial Intelligence for Climate Adaptation: Reinforcement Learning for Climate Change-Resilient Transport
arXiv:2603.06278v1 Announce Type: new Abstract: Climate change is expected to intensify rainfall and, consequently, pluvial flooding, leading to increased disruptions in urban transportation systems over the coming decades. Designing effective adaptation strategies is challenging due to the long-term, sequential nature...
On the Value of Tokeniser Pretraining in Physics Foundation Models
arXiv:2603.05598v1 Announce Type: cross Abstract: We investigate the impact of tokeniser pretraining on the accuracy and efficiency of physics emulation. Modern high-resolution simulations produce vast volumes of data spanning diverse physical regimes and scales. Training foundation models to learn the...
Offline Materials Optimization with CliqueFlowmer
arXiv:2603.06082v1 Announce Type: new Abstract: Recent advances in deep learning inspired neural network-based approaches to computational materials discovery (CMD). A plethora of problems in this field involve finding materials that optimize a target property. Nevertheless, the increasingly popular generative modeling...
JAWS: Enhancing Long-term Rollout of Neural Operators via Spatially-Adaptive Jacobian Regularization
arXiv:2603.05538v1 Announce Type: cross Abstract: Data-driven surrogate models improve the efficiency of simulating continuous dynamical systems, yet their autoregressive rollouts are often limited by instability and spectral blow-up. While global regularization techniques can enforce contractive dynamics, they uniformly damp high-frequency...
When Rubrics Fail: Error Enumeration as Reward in Reference-Free RL Post-Training for Virtual Try-On
arXiv:2603.05659v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) and Rubrics as Rewards (RaR) have driven strong gains in domains with clear correctness signals and even in subjective domains by synthesizing evaluation criteria from ideal reference answers. But...
SecureRAG-RTL: A Retrieval-Augmented, Multi-Agent, Zero-Shot LLM-Driven Framework for Hardware Vulnerability Detection
arXiv:2603.05689v1 Announce Type: cross Abstract: Large language models (LLMs) have shown remarkable capabilities in natural language processing tasks, yet their application in hardware security verification remains limited due to scarcity of publicly available hardware description language (HDL) datasets. This knowledge...
Longitudinal Lesion Inpainting in Brain MRI via 3D Region Aware Diffusion
arXiv:2603.05693v1 Announce Type: cross Abstract: Accurate longitudinal analysis of brain MRI is often hindered by evolving lesions, which bias automated neuroimaging pipelines. While deep generative models have shown promise in inpainting these lesions, most existing methods operate cross-sectionally or lack...
FreeTxt-Vi: A Benchmarked Vietnamese-English Toolkit for Segmentation, Sentiment, and Summarisation
arXiv:2603.05690v1 Announce Type: new Abstract: FreeTxt-Vi is a free and open source web based toolkit for creating and analysing bilingual Vietnamese English text collections. Positioned at the intersection of corpus linguistics and natural language processing NLP it enables users to...
Structured Multidimensional Representation Learning for Large Language Models
arXiv:2603.05727v1 Announce Type: new Abstract: Transformer architectures achieve state-of-the-art performance across a wide range of pattern recognition and natural language processing tasks, but their scaling is accompanied by substantial parameter growth and redundancy in the embedding dimension. In this work,...
Let's Talk, Not Type: An Oral-First Multi-Agent Architecture for Guaran\'i
arXiv:2603.05743v1 Announce Type: new Abstract: Although artificial intelligence (AI) and Human-Computer Interaction (HCI) systems are often presented as universal solutions, their design remains predominantly text-first, underserving primarily oral languages and indigenous communities. This position paper uses Guaran\'i, an official and...
NERdME: a Named Entity Recognition Dataset for Indexing Research Artifacts in Code Repositories
arXiv:2603.05750v1 Announce Type: new Abstract: Existing scholarly information extraction (SIE) datasets focus on scientific papers and overlook implementation-level details in code repositories. README files describe datasets, source code, and other implementation-level artifacts, however, their free-form Markdown offers little semantic structure,...
PVminerLLM: Structured Extraction of Patient Voice from Patient-Generated Text using Large Language Models
arXiv:2603.05776v1 Announce Type: new Abstract: Motivation: Patient-generated text contains critical information about patients' lived experiences, social circumstances, and engagement in care, including factors that strongly influence adherence, care coordination, and health equity. However, these patient voice signals are rarely available...
Tutor Move Taxonomy: A Theory-Aligned Framework for Analyzing Instructional Moves in Tutoring
arXiv:2603.05778v1 Announce Type: new Abstract: Understanding what makes tutoring effective requires methods for systematically analyzing tutors' instructional actions during learning interactions. This paper presents a tutor move taxonomy designed to support large-scale analysis of tutoring dialogue within the National Tutoring...
ReflexiCoder: Teaching Large Language Models to Self-Reflect on Generated Code and Self-Correct It via Reinforcement Learning
arXiv:2603.05863v1 Announce Type: new Abstract: While Large Language Models (LLMs) have revolutionized code generation, standard "System 1" approaches, generating solutions in a single forward pass, often hit a performance ceiling when faced with complex algorithmic tasks. Existing iterative refinement strategies...
ROSE: Reordered SparseGPT for More Accurate One-Shot Large Language Models Pruning
arXiv:2603.05878v1 Announce Type: new Abstract: Pruning is widely recognized as an effective method for reducing the parameters of large language models (LLMs), potentially leading to more efficient deployment and inference. One classic and prominent path of LLM one-shot pruning is...
Confidence Before Answering: A Paradigm Shift for Efficient LLM Uncertainty Estimation
arXiv:2603.05881v1 Announce Type: new Abstract: Reliable deployment of large language models (LLMs) requires accurate uncertainty estimation. Existing methods are predominantly answer-first, producing confidence only after generating an answer, which measure the correctness of a specific response and limits practical usability....
Lost in Stories: Consistency Bugs in Long Story Generation by LLMs
arXiv:2603.05890v1 Announce Type: new Abstract: What happens when a storyteller forgets its own story? Large Language Models (LLMs) can now generate narratives spanning tens of thousands of words, but they often fail to maintain consistency throughout. When generating long-form narratives,...
Learning Next Action Predictors from Human-Computer Interaction
arXiv:2603.05923v1 Announce Type: new Abstract: Truly proactive AI systems must anticipate what we will do next. This foresight demands far richer information than the sparse signals we type into our prompts -- it demands reasoning over the entire context of...
Addressing the Ecological Fallacy in Larger LMs with Human Context
arXiv:2603.05928v1 Announce Type: new Abstract: Language model training and inference ignore a fundamental linguistic fact -- there is a dependence between multiple sequences of text written by the same person. Prior work has shown that addressing this form of \textit{ecological...
ViewFusion: Structured Spatial Thinking Chains for Multi-View Reasoning
arXiv:2603.06024v1 Announce Type: new Abstract: Multi-view spatial reasoning remains difficult for current vision-language models. Even when multiple viewpoints are available, models often underutilize cross-view relations and instead rely on single-image shortcuts, leading to fragile performance on viewpoint transformation and occlusion-sensitive...
Evaluating Austrian A-Level German Essays with Large Language Models for Automated Essay Scoring
arXiv:2603.06066v1 Announce Type: new Abstract: Automated Essay Scoring (AES) has been explored for decades with the goal to support teachers by reducing grading workload and mitigating subjective biases. While early systems relied on handcrafted features and statistical models, recent advances...
A Causal Graph Approach to Oppositional Narrative Analysis
arXiv:2603.06135v1 Announce Type: new Abstract: Current methods for textual analysis rely on data annotated within predefined ontologies, often embedding human bias within black-box models. Despite achieving near-perfect performance, these approaches exploit unstructured, linear pattern recognition rather than modeling the structured...
CRIMSON: A Clinically-Grounded LLM-Based Metric for Generative Radiology Report Evaluation
arXiv:2603.06183v1 Announce Type: new Abstract: We introduce CRIMSON, a clinically grounded evaluation framework for chest X-ray report generation that assesses reports based on diagnostic correctness, contextual relevance, and patient safety. Unlike prior metrics, CRIMSON incorporates full clinical context, including patient...
MAPO: Mixed Advantage Policy Optimization for Long-Horizon Multi-Turn Dialogue
arXiv:2603.06194v1 Announce Type: new Abstract: Subjective multi-turn dialogue tasks, such as emotional support, require conversational policies that adapt to evolving user states and optimize long-horizon interaction quality. However, reinforcement learning (RL) for such settings remains challenging due to the absence...