Knowledge catalogue
arXiv:2605.08488v1 Announce Type: cross Abstract: We develop a unified Lyapunov-integral quadratic constraint (IQC) framework for establishing uniform stability of first-order accelerated optimization
arXiv:2605.09232v1 Announce Type: cross Abstract: The increasing deployment of Internet-of-Things (IoT) devices has accelerated the use of distributed learning frameworks, where data remains local whi
arXiv:2605.03423v1 Announce Type: new Abstract: This paper proposes a novel adaptive dual-path framework for covert semantic communication (SemCom), which integrates covert information transmission wi
arXiv:2605.04085v1 Announce Type: cross Abstract: Objectives: Large language models (LLMs) are increasingly used for clinical text summarization, yet structured methods to assess associated patient sa
arXiv:2510.25224v3 Announce Type: replace Abstract: While Large Language Models (LLMs) are increasingly used in agentic frameworks to assist individual users, there is a growing need for agents that c
arXiv:2604.21090v1 Announce Type: cross Abstract: AI governance programmes increasingly rely on natural language prompts to constrain and direct AI agent behaviour. These prompts function as executabl
arXiv:2604.19779v1 Announce Type: new Abstract: Environmental, Social, and Governance (ESG) reports are central to investment decision-making, yet their length, heterogeneous content, and lack of stan
arXiv:2604.20441v1 Announce Type: new Abstract: Background: Agent skills are increasingly deployed as modular, reusable capability units in AI agent systems. Medical research agent skills require safe
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arXiv:2604.17622v1 Announce Type: new Abstract: Credit risk default prediction remains a cornerstone of risk management in the financial industry. The task involves estimating the likelihood that a bo
arXiv:2604.13367v1 Announce Type: new Abstract: Radiotherapy-induced normal tissue injury is a clinically important complication, and accurate segmentation of injury regions from medical images could
arXiv:2604.13345v1 Announce Type: new Abstract: The paper presents design and prototype implementation of an edge based object detection system within the new paradigm of AI agents orchestration. It g
arXiv:2604.12418v1 Announce Type: cross Abstract: Autonomous vehicles are increasingly deployed in safety-critical applications, where sensing failures or cyberphysical attacks can lead to unsafe oper
arXiv:2604.10973v1 Announce Type: new Abstract: Reasoning over tabular data is a crucial capability for tasks like question answering and fact verification, as it requires models to comprehend both fr
arXiv:2604.11529v1 Announce Type: new Abstract: Foundation models have transformed natural language processing and computer vision, and a rapidly growing literature on time-series foundation models (T
arXiv:2604.08903v1 Announce Type: new Abstract: Pansharpening aims to generate high-resolution multispectral (HRMS) images by fusing low-resolution multispectral (LRMS) and high-resolution panchromati
arXiv:2408.05086v3 Announce Type: replace Abstract: Neural language models (LMs) have been shown to capture complex linguistic patterns, yet their utility in understanding human language and more broa
arXiv:2604.07553v1 Announce Type: new Abstract: This study presents a framework for generating the gold-standard summary fully automatically and reproducibly based on multiple human summaries of Turki
arXiv:2610.13840v1 Announce Type: new Abstract: Comics are one of the great visual records of the twentieth century: hundreds of thousands of pages in which a mass readership saw its leaders, its wars
arXiv:2506.06410v4 Announce Type: replace-cross Abstract: We introduce Delphos, a deep reinforcement learning framework for assisting discrete choice model specification process. Delphos aims to suppo
arXiv:2610.07473v1 Announce Type: new Abstract: Large language models show promise in clinical reasoning, but psychiatric interviewing requires guiding an evolving conversation. Their ability to carry
arXiv:2610.07276v1 Announce Type: cross Abstract: Deployment-time safety of language models is commonly implemented through runtime guardrails such as input moderation, routing, retrieval verification
arXiv:2610.04368v1 Announce Type: new Abstract: Extreme winter weather has repeatedly disrupted gas-fired power generation in the United States, yet the plant-level data needed to systematically quant
arXiv:2610.02970v1 Announce Type: new Abstract: Large language models (LLMs) have shown promising potential for biomedical named entity recognition (BioNER) through instruction following and in-contex
arXiv:2610.03625v1 Announce Type: new Abstract: Relational databases are among the most widely deployed forms of structured knowledge, and natural language access to them requires grounding language o
arXiv:2610.02116v1 Announce Type: new Abstract: A comparative explainability framework is presented to audit DeBERTa-v3 under zero-shot classification of medical abstracts. The work addresses the disa
arXiv:2610.01119v1 Announce Type: new Abstract: Designing high-performance microwave absorbers requires specialized expertise in electromagnetic theory, materials science and simulation programming, a
arXiv:2508.17692v2 Announce Type: replace Abstract: Recent advances in LLM-based agents highlight the importance of their reasoning frameworks, which guide the problem-solving process in diverse ways.
arXiv:2610.01383v1 Announce Type: new Abstract: Analogical reasoning involves identifying and preserving relational structures across domains. However, existing approaches to AI-driven multimodal anal
arXiv:2303.00897v2 Announce Type: replace Abstract: Federated learning is a distributed learning framework that takes full advantage of private data samples kept on edge devices. In real-world federat
arXiv:2609.39010v1 Announce Type: cross Abstract: Objective: Head and neck (HN) proton therapy spans six to seven weeks of anatomical change, while offline replanning takes about a week. We present an
arXiv:2603.17343v3 Announce Type: replace Abstract: The rapid proliferation of AI-Generated Images (AIGIs) poses severe misinformation risks, making AIGI detection critical yet challenging. Traditiona
arXiv:2609.37384v1 Announce Type: new Abstract: Molecular representation learning is central to computer-aided drug discovery. Molecular graphs, SMILES strings, and 3D conformations provide complement
arXiv:2609.36165v1 Announce Type: cross Abstract: In this work, we develop a second-order optimization framework for physics-informed neural networks (PINNs) applied to high-dimensional parametric par
arXiv:2609.28692v1 Announce Type: new Abstract: Artificial Intelligence agents based on large language models provide convenient natural language interfaces to scientific software, but reliability is
arXiv:2510.21736v2 Announce Type: replace-cross Abstract: This study introduces a novel control framework for adaptive cruise control (ACC) in automated driving, leveraging neural networks and physics
arXiv:2609.19234v1 Announce Type: cross Abstract: This paper presents the AR fairness metamodel, a framework designed to represent, analyze, and compare different fairness scenarios. The metamodel con
arXiv:2505.12192v2 Announce Type: replace Abstract: Early detection of PD remains particularly challenging in resource-constrained settings, where voice-based analysis has emerged as a promising non-i
arXiv:2603.16365v3 Announce Type: replace Abstract: We study alpha factor mining, the automated discovery of predictive signals from noisy, non-stationary market data-under a practical requirement tha
arXiv:2607.14097v2 Announce Type: replace Abstract: We introduce RegNetAgents, an AI-oriented multi-agent framework for structured, query-driven regulatory candidate identification across heterogeneou
arXiv:2609.15849v1 Announce Type: cross Abstract: Can LLMs reason through new information like humans, or do they merely retrieve cached opinions? This is critical for silicon sampling, where LLM pers
arXiv:2609.10339v1 Announce Type: new Abstract: A confidence-aware multimodal fusion framework (CAMF) is proposed to realize reliable human intention prediction for industrial human-robot collaboratio
arXiv:2609.05529v1 Announce Type: cross Abstract: Large language model (LLM)-based multi-agent systems (MAS) predominantly rely on centralized orchestration and lack formal verification mechanisms for
arXiv:2609.10364v1 Announce Type: new Abstract: Simultaneous assessment of medical imaging and patient records is often required in clinical diagnosis. However, standard machine learning algorithms ca
arXiv:2603.01875v4 Announce Type: replace-cross Abstract: Knowledge distillation (KD) is widely used to compress and post-train large language models (LLMs), yet many existing frameworks execute teach
arXiv:2609.08010v1 Announce Type: new Abstract: This paper presents a general framework for simulating multi-body space robots with contact. We bring efficient, large-scale robot simulation to in-spac
// From Language Models to World-Acting Systems // A critical review of agentic AI, and a framework that is genuinely useful for deciding how much authority to hand an agent. Here is how it works. The
DISCLAIMER I ramble a lot and am not good at focusing on documentation and or coherent thoughts. TLDR I did things I made a thing and it works for me. I will try to answer any questions. A few people
arXiv:2312.06315v2 Announce Type: replace Abstract: Warning: This paper contains content that may be offensive or upsetting. There has been a significant increase in the usage of large language models
arXiv:2609.02231v1 Announce Type: new Abstract: Interview assessment requires per-criterion judgments grounded in behavioral evidence, yet surging applicant volumes have made human-only evaluation cos
arXiv:2508.17635v2 Announce Type: replace Abstract: Managing chronic wounds remains a major healthcare challenge, with clinical assessment often relying on subjective and time-consuming manual documen
arXiv:2608.30745v1 Announce Type: new Abstract: The widespread adoption of encrypted traffic poses severe challenges to current security situational awareness systems based on network traffic monitori
arXiv:2608.27902v1 Announce Type: new Abstract: Landing pages are goal-oriented web interfaces that must communicate a target-specific value proposition while organizing information flow, visual hiera
arXiv:2608.27462v1 Announce Type: new Abstract: Unlike explicit attacks with obvious profanity, implicit hate speech hides malice within seemingly compliant expressions through metaphors and contextua
arXiv:2608.27010v1 Announce Type: new Abstract: In the present work an efficient border control management procedure is proposed. Compared to operational queue management systems, whose operations are
arXiv:2608.26363v1 Announce Type: new Abstract: This paper introduces a unified mathematical framework for modeling information propagation through convolutional neural networks (CNNs), with the aim o
arXiv:2601.03605v2 Announce Type: replace Abstract: Despite the significant advancements of Large Language Models (LLMs), their factuality remains a critical challenge, creating a growing need for mor
arXiv:2608.26153v1 Announce Type: new Abstract: Clinical electroencephalography (EEG) reporting remains largely manual and time-consuming, and current EEG software ecosystems do not produce the struct
arXiv:2608.26812v1 Announce Type: new Abstract: A novel Hyperspectral diffusion Equivariant Imaging (HyDiff-EI) framework for solving the hyperspectral image (HSI) inpainting problem has been presente
arXiv:2608.27031v1 Announce Type: new Abstract: Measuring alignment between documents and structured reference frameworks requires identifying conceptual evidence distributed throughout the text and r