The increasing complexity of software systems has heightened the need for efficient and accurate vulnerability detection. Large Language Models have emerged as promising tools in this domain; however, their reasoning capabilities and limitations rema...
Background and Purpose: Neurology requires integration of clinical reasoning, imaging interpretation, and current knowledge, making it an ideal field for evaluating large language models (LLMs). Methods: Using 1715 questions from the Taiwan Neurology...
Sindhi language models struggle with Romanized Sindhi as there is variation in the spellings of everyday words, and short prompts do not explicitly state the task or target language. In this study, the remaining source record of a small DeepSeek-R1 p...
The development of generative artificial intelligence technology has brought convenience to various industries, but also caused some confusion. Especially today, when the content generated by large language models is extremely similar to real text, i...
Objectives: To benchmark the performance of DeepSeek-R1 against three other advanced AI reasoning models (GPT-4o, Qwen3, Grok-3) in automatically extracting T/N staging from esophageal cancer endoscopic ultrasound (EUS) complex medical reports, and t...
Background: Generative artificial intelligence (AI) applications have gained increasing popularity in recent years and are used by an ever-increasing number of people on a day-to-day basis. While the performance of the earlier-generation generative A...
Agentic AI and Large Language Models (LLMs) are transforming how language is understood and generated while reshaping decision-making, automation, and research practices. LLMs provide underlying reasoning capabilities, and Agentic AI systems use them...
On food delivery platforms, user decisions are often driven by dynamic contextual factors such as time, intent, and lifestyle patterns. Traditional context-aware recommender systems struggle to capture such implicit signals, especially when user beha...
Empathetic and coherent responses are critical in automated chatbot-facilitated psychotherapy. This study addresses the challenge of enhancing the emotional and contextual understanding of large language models (LLMs) in psychiatric applications. We...
Objective: To evaluate the practical value of DeepSeek-R1 and Doubao-1.5-pro in the context of ovarian cancer management by examining their diagnostic and treatment-related competencies. Methods: 20 key ovarian cancer diagnosis and treatment issues w...
The increasing need for sustainable energy management in smart buildings calls for cost-effective solutions that balance energy efficiency and occupant comfort. This article presents a Large Language Model (LLM)-based recommendation system capable of...
Objectives: to assess the ability of high-performing open-weight large language models (LLMs) in extracting key radiological features from prostate MRI reports. Methods: Five LLMs (Llama3.3, DeepSeek-R1-Llama3.3, Phi4, Gemma-2, and Qwen2.5-14B) were...
Background/Objectives: Accurate intraocular lens (IOL) power calculation is vital for achieving the desired postoperative spherical equivalent (SE) in cataract surgery. Generative Artificial-Intelligence (AI) systems are increasingly being used in op...
The integration of artificial intelligence in healthcare has revolutionized research, diagnostics, and patient care. In particular, the emergence of ChatGPT and the recent rise of DeepSeek have drawn significant attention to this landscape. This empi...
Large language models (LLMs) have recently demonstrated remarkable capabilities in natural language processing, mathematical reasoning, and code generation. However, their potential for solving differential equations—fundamental to applied math...
Precise coagulant dosage control is essential for stable drinking-water treatment, yet conventional machine learning (ML) methods can be sensitive to data conditions. This study evaluates a large language model (LLM)-based in-context workflow for thi...
Large language models (LLMs) have recently shown remarkable capabilities in natural language processing. In this work, we investigate whether an advanced LLM can recognize user emotions and intentions from text, focusing on the open-source model Deep...
Time-fractional convection–diffusion equations are significant for their ability to model complex transport phenomena that deviate from classical behavior, with numerous applications in anomalous diffusion, memory effects, and nonlocality. This...
Objectives: Coronary artery bypass grafting (CABG) remains a fundamental surgical treatment for advanced coronary artery disease. With the increasing use of large language models to obtain health information, patients are increasingly turning to thes...
Satellite networks face escalating cybersecurity threats from evolving attack vectors and systemic complexities. This paper proposes SatGuard, a novel framework integrating a three-dimensional penetration testing methodology and a nonlinear risk asse...
Although LLMs demonstrate high success in logical tasks, it remains debated whether this stems from genuine reasoning or statistical memorization. This study presents a novel, multi-dimensional, perturbation- and session-based behavioral testing fram...
The growing demand for accurate and efficient Chest X-Ray (CXR) interpretation has prompted the development of AI-driven systems to alleviate radiologist workload and reduce diagnostic variability. This paper introduces the Intelligent Humanized Radi...
Background/Objectives: The integration of artificial intelligence (AI) into pharmaceutical development has the potential to accelerate early-stage formulation design. In this study, large language models (ChatGPT (GPT-4o, OpenAI) and DeepSeek (DeepSe...
The rapid development of large language models (LLMs), including ChatGPT, Gemini, and Copilot, has led to their increasing use in health communication and patient education. However, their growing popularity raises important concerns about whether th...
Autonomous driving in dense urban environments requires accurate trajectory forecasting supported by interpretable contextual evidence. This study presents a multimodal framework that performs driving scene context (DSC)-aware trajectory prediction w...
With the exponential growth of multimodal data, the limitations of traditional unimodal models in cross-modal understanding and complex scenario reasoning have become increasingly evident. Built upon the foundation of Large Language Models (LLMs), Mu...
Background: Domain-specific large language models (LLMs) like Ortho GPT have potential advantages over general-purpose models in medical education, offering improved factual accuracy and contextual relevance. This study evaluates the performance of O...
Sentencing is among the most contested tasks in criminal adjudication, as judges must balance retribution, rehabilitation, deterrence, and proportionality within broad judicial discretion. In Taiwan, Article 57 of the Criminal Code mandates considera...
As large language models (LLMs) and vision–language models (VLMs) become increasingly used in robotics area, a crucial question arises: to what extent do these models replicate human-like cognitive processes, particularly within socially intera...
Large language models (LLMs) have demonstrated remarkable capabilities in various natural language processing (NLP) tasks, but their effectiveness in real-world consumer complaint classification without fine-tuning remains uncertain. Zero-shot classi...
This article presents a novel methodology for auditing urban regulatory frameworks through the application of artificial intelligence (AI) using the case of Greater Santiago as an empirical laboratory. Based on the semantic analysis of 31 communal zo...
Flood control briefings are critical emergency response documents that provide timely decision support for urban safety and regional development under climate change challenges. However, existing large language models (LLMs) face significant difficul...
Biomedical named entity recognition (NER) and relation extraction (RE) remain challenging because biomedical texts contain ambiguous abbreviations, complex entity boundaries, domain-specific terminology, and implicit relations. This study proposes a...
Pressure injury management requires coordinated decisions on wound staging, debridement, and dressing selection, yet most computational studies focus on isolated tasks using a single data modality. This study developed a multimodal, multi-task framew...
Reliable structural safety risk diagnosis and control of prestressed concrete bridges is essential for safe operation. Unfortunately, current practice relies heavily on inspectors’ engineering knowledge, field experience, and subjective judgmen...
Public health emergencies impose lasting mental health burdens worldwide. Conservation of resources (COR) theory suggests resource loss increases depressive symptoms, yet how multiple resources relate to depressive symptoms remains unclear. Using 848...
Recent advances in large language models (LLMs) have raised questions about their potential role in portfolio allocation beyond traditional sentiment analyses. This study investigated whether LLMs, when prompted directly, can autonomously generate po...
Named entity recognition in natural disaster emergencies is a critical foundational task for emergency management. However, existing methods face challenges including complex entity types, frequent emergence of new terminology, model knowledge obsole...
The trustworthiness of AI-powered network security auditing depends not only on detection accuracy but on the faithfulness of the explanations that support compliance verdicts. In IoT network security, Large Language Models (LLMs) are increasingly ut...
A growing challenge in mobile robotics is the reliance on complex graphical interfaces and rigid control pipelines, which limit accessibility for non-expert users. This work introduces a latency-aware benchmarking framework that enables natural-langu...
To address the issues of fragmented species information, low knowledge extraction efficiency, and insufficient utilization in the aquaculture domain, the main objective of this study is to construct the first knowledge graph for the Golden Pomfret aq...
Accurate calculation of embodied carbon emissions in buildings (ECE) is crucial to achieving global carbon neutrality. However, fragmented data, inconsistent regional standards, and low computational efficiency have long hindered existing methods. Th...
Zero-shot large language model (LLM) classifiers are often deployed behind interfaces that expose no token probabilities, so practitioners sample the model several times and use the winning label’s vote share as confidence. We study the simples...
This study presents a prototype for the structured analysis of PubMed case-report titles, keywords and abstracts, combining domain fine-tuning, retrieval-augmented generation (RAG) and five specialist-role agents with a synthesis agent. DeepSeek-R1-D...
Background: While large language models (LLMs) have demonstrated proficiency in medical examinations, their comparative performance on European gastroenterology assessments remains underexplored, particularly regarding architectural differences betwe...
This study aimed to compare the performance of various Large Language Models (LLMs) in answering multiple-choice questions from the last six editions (2017 to 2024) of Revalida exam. The evaluation focused on models capable of processing content in B...
Recent developments in natural language processing, particularly large language models (LLMs), create new opportunities for literary analysis in underexplored languages like Romanian. This study investigates stylistic heterogeneity and genre blending...
This paper presents an approach integrating Large Language Models (LLMs), specifically GPT-4 and the open-source DeepSeek-R1, into Geographic Information System (GIS) workflows to enhance the accessibility, flexibility, and efficiency of spatial anal...
Background/Objectives: Digitalization of medical knowledge has improved access to information but also increased the spread of imprecise content. Repeated exposure to incorrect descriptions may lead to their normalization over time. This is particula...
Large language models (LLMs) have shown remarkable progress in general reasoning and understanding, but their ability to perform formal logical reasoning remains under-explored. In this paper, we introduce DLReasonSuite, a novel benchmark designed to...
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