large language models

AI chatbot-associated psychiatric symptoms: a narrative review of publicly available reports and media accounts

Abstract Background There are a growing number of cases where direct use of AI has been associated with either new or worsening psychiatric symptoms (colloquially labelled AI psychosis). Due to the novelty of the phenomenon, current literature is limited on the impacts of sustained, emotionally charged interactions wit...

Gene-level evolutionary conservation and human constraint decline at comparable rates across the age of onset of Mendelian disease

Background Gene-level constraint and conservation scores are widely used to prioritise disease genes and to benchmark genomic language models. Purifying selection acts weakly on phenotypes manifesting after reproduction, so these scores are expected to carry less information about late-onset disease. An onset-dependent...

x3d_mcp: A Model Context Protocol Server for AI-Assisted X3D Authoring

Large language models (LLMs) are increasingly capable of generating structured technical content, but producing valid Extensible 3D (X3D) scenes is brittle in practice: without grounding in the authoritative specification, LLMs hallucinate node names, misuse field types, and emit broken ROUTE connections that fail XSD...

“Oh No, the Robots Are Here”: AI identity labels, social uptake and sanctioning in mixed human–AI communities

Social media users can now encounter autonomous AI accounts that post, reply and vote alongside other users. In a longitudinal field experiment, we examined how labelling these accounts relates to whom people answer and how they evaluate AI contributions. YSocial is a Reddit-like hybrid platform for social media simula...

Beyond Mueller–Hinton Broth: A Scoping Review of Metabolic Context, Conditional Antibiotic Susceptibility, and Metabolism-Aware Antimicrobial Susceptibility Testing

Background. Standard antimicrobial susceptibility testing (AST) in Mueller–Hinton broth samples a single metabolic state, yet bacterial susceptibility in vivo depends on nutrient availability, host-derived metabolites, and spatial structure. The extent to which this mismatch produces clinically relevant errors in susce...

Token-Generation-Control-Deterministic-Logical-Timeline

Text generation by large language models is essentially a probabilistic sampling process: the model computes a probability distribution over the next token based on the input context, and then samples a token from that distribution. This probabilistic design gives large models creativity but also introduces a fundament...

A Lightweight Hybrid Framework for Detecting AI-Generated Scientific Text Using Multi-granularity Features and Language-Model Surprisal

The increasing adoption of generative artificial intelligence (Gen-AI) in scientific writing has transformed scholarly communication while also raising concerns about authorship transparency and research integrity. The growing use of large language models (LLMs) has increased the need for methods that can screen AI-gen...

PEDAGOGICAL CONDITIONS FOR USING ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN PREPARING FUTURE TEACHERS FOR PROFESSIONAL ACTIVITY

This thesis investigates the structured integration of artificial intelligence (AI) tools within higher pedagogical education under specialty 13.00.01. As educational paradigms shift globally towards digital environments, future educators must develop comprehensive digital-pedagogical competencies. The study highlights...

Prioritising substances of concern for monitoring by using knowledge graphs and public datasets

Abstract Air pollution is identified by the World Health Organisation (WHO) as the leading environmental threat to human health, with indoor exposure particularly concerning given its daily, long-term nature. Europeans spend approximately 90% of their time indoors, where poor indoor air quality (IAQ) contributes an est...

Data and Code of Solution-Focused Brief Therapy Training with a Large Language Model-Based Virtual Standardized Patient

Dataset Systematic Literature Review: AI Generatif, Deskilling, dan Pengawasan Editorial dalam Jurnalisme

Review: AI Generatif, Deskilling, dan Pengawasan Editorial dalam Jurnalisme Pembuat: Novanka, Magister Ilmu Komunikasi, Fakultas Ilmu Sosial dan Ilmu Politik, Universitas Pelita Harapan (novankawijaya@gmail.com) Delliana Fitriah, Magister Ilmu Komunikasi, Fakultas Ilmu Sosial dan Ilmu Politik, Univeristas Pelita Harapa...

ClaimArena-LLM: Auditing Adversarial Propagation of False Causal Attributions in Multi-Agent LLM Debates

Background: Multi-agent large language model (LLM) systems increasingly rely on structured interaction, debate, and final model-based evaluation, yet an unsupported causal attribution introduced by one participant may propagate through the interaction and influence subsequent decisions. This study introduces ClaimArena...