Learning Evolving Latent Strategies for Multi-Agent Language Systems without Model Fine-Tuning

arXiv:2512.20629v1 Announce Type: cross Abstract: This study proposes a multi-agent language framework that enables continual strategy evolution without fine-tuning the language model’s parameters. The core

LookPlanGraph: Embodied Instruction Following Method with VLM Graph Augmentation

arXiv:2512.21243v1 Announce Type: cross Abstract: Methods that use Large Language Models (LLM) as planners for embodied instruction following tasks have become widespread. To successfully complete

Intersectional Fairness in Vision-Language Models for Medical Image Disease Classification

arXiv:2512.15249v2 Announce Type: replace-cross Abstract: Medical artificial intelligence (AI) systems, particularly multimodal vision-language models (VLM), often exhibit intersectional biases where models are systematically less confident

One Tool Is Enough: Reinforcement Learning for Repository-Level LLM Agents

arXiv:2512.20957v1 Announce Type: cross Abstract: Locating the files and functions requiring modification in large open-source software (OSS) repositories is challenging due to their scale and

Hearing to Translate: The Effectiveness of Speech Modality Integration into LLMs

arXiv:2512.16378v2 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) expand beyond text, integrating speech as a native modality has given rise to SpeechLLMs, which

Developing and Evaluating Guidelines to Prevent Overdependence on Digital Therapeutics in Children and Adolescents: Randomized Controlled Trial

December 24, 2025

Background: Digital therapeutics (DTx) for children and adolescents with mental health problems have been developed in the health care industry. Despite reports of side effects from DTx for children and adolescents, there have been no guidelines to address the prevention of DTx overdependence among young users. Objective: This study aimed to identify the requirements for guidelines to prevent DTx overdependence in children and adolescents and to develop and evaluate these guidelines. Methods: We conducted 2 phases. This study first involved a phase I survey to develop guidelines, including assessments of smartphone usage and mental health conditions. The second phase evaluated the guidelines’ effectiveness, reliability, necessity, and satisfaction using a visual analog scale through a randomized controlled trial. Participants—45 children and adolescents aged 9-16 years and 42 caregivers—were randomly assigned to the experimental and control groups. Results: Phase I revealed that blocking mobile applications and notifications (mean 8.5, SD 1.8) and parental monitoring (mean 8.5, SD 2.1) were effective preventive features. Caregivers, children, and adolescents expressed concerns about the side effects and overdependence of DTx and decreased effects due to nonindividualized guidelines in subjective responses to the phase I survey. Based on these insights, personalized guidelines for phase II were developed, in which overall mean visual analog scale scores for guideline evaluation were higher in the experimental group, except for necessity among caregivers (mean 8.5, SD 1.3 versus mean 8.7, SD 1.2). Conclusions: Both caregivers and children and adolescents demonstrated the need for guidelines to prevent overdependence on DTx distinct from smartphone usage. Tailored guidelines may be acceptable for use in real-world therapeutic protocols. Guidelines to prevent overdependence on DTx in children and adolescents and to achieve a balance between their benefits and risks need to be established. Trial Registration: Clinical Research Information Service (CRiS) of the Republic of Korea KCT0008893; https://cris.nih.go.kr/cris/search/detailSearch.do?seq=25609

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