large language models

The Pocket Tutor Has a Price: Scaffolding Self-Paced Learning with AI

This work argues that self-paced settings strip away the implicit scaffolding of the classroom, so unguided LLM access defaults to cognitive offloading, metacognitive disengagement, and fragmented learning that erodes durable competence and argues that resourcing the protective environment is, in effect, educational an...

Analysis of Prediction Accuracy of Different Chatbots for Big Five Personality Traits in High School Students

Chatbots based on large language models (LLMs) are commonly used by teenagers for daily communication and introspection. All the previous studies merely compare personality outputs created by a certain AI with no numeric assessment of differences from the self-reported personality scores of students. Questionnaire data...

AI Alignment Training Paradigms and Cross-Model Differences in AI Persuasive Behavior

Reinforcement learning from human feedback (RLHF) and constitutional artificial intelligence (CAI) are two ways to shape model behavior. Among them, CAI is built around avoiding different categories of harm, including racism, sexism, toxic, dangerous and illegal content. This article integrates the existing empirical r...

Peer Review Report For: Core open science practices in major medical journals: development of an automatized tool based on a Large Language Model [version 1; peer review: 1 approved, 1 approved with reservations]

Applications, Safety Challenges and Future Directions of AI Agents in Cardiovascular Prediction: A Review of Technological Evolution, System Architecture and Clinical Translation

Cardiovascular disease remains a major contributor to mortality and long-term morbidity worldwide. For this reason, risk prediction, early detection, and decision support have become central tasks in digital health research. Cardiovascular prediction has developed from statistical scores to machine learning and deep le...

Advances, Risks, And Implementation Challenges of Artificial Intelligence as A Force Multiplier for Clinical Decision Making and Health System Efficiency

The central argument is that the value of clinical artificial intelligence is determined less by model accuracy in isolation than by the quality of the sociotechnical system into which a model is placed: governance, workflow integration, monitoring, and the preservation of meaningful human oversight.

An Empirical Investigation of Whether Role Asymmetry Can Overcome Reasoning Blind Spots in Homogeneous Models through Adaptive Routing and Local Repair

These findings provide reproducible empirical evidence for systematic reasoning blind spots in self-criticism by homogeneous models, and indicate that role asymmetry implemented only through prompting cannot reliably overcome cognitive homogeneity.

Risk and Return under Open-Source LLM Shocks: A CAPM Analysis of U.S. AI-Related Sector ETFs

This paper examines the impact of two landmark open-source releases—DeepSeek-R1 and Kimi K3—on the returns and risk of five U.S. AI-related sector ETFs, finding that both shocks were highly concentrated in the computing-power layer, while the application layer remained relatively resilient.

From transformer to large language models for automated generation of hospital discharge summaries: a systematic review

Neurosymbolic code auditing

Code auditing examines whether an implementation satisfies target properties such as reliability, security, performance, and design intent. Classical static analysis provides rigorous foundations for automated auditing, but it remains difficult to apply broadly in real-world software. The emergence of large language mo...

Attack Chain Reconstruction Method Based on LLM Prior-Knowledge Guidance and Heterogeneous Graph Reasoning

This paper proposes a heterogeneous graph attack chain reconstruction method based on LLM prior-knowledge guidance that verifies the effectiveness of the cooperation between semantic understanding and structural reasoning.

History of Algorithmic Music Generation: From Mozart to Modern AI

Algorithmic Music Generation is an old topic. From very basic systems of controlled randomness to advanced data-driven systems. This paper summarizes the general history of algorithmic music generation, as well as three key eras: the random combination era, the rule-based algorithm generation era and the deep learning...