This paper argues that norms, not individual choices, govern how young people use social media and proposes designing an independent platform to build trusted connections.

The study finds exploratory evidence that gender moderates how youth perceive privacy risks and benefits, influencing their protective behavior when using smart voice assistants.

This study proposes a negotiation framework, using composite indices (RBTI and CATI), to explain how youth navigate competing privacy pressures when using smart voice assistants, finding that high usa…

The paper introduces PHTV-Scout, a novel framework that analyzes Douyin and Kwai data, revealing a high prevalence of potentially harmful teen videos, particularly CSE imagery, and demonstrating that…

BuddyBench introduces a novel, privacy-constrained multi-task benchmark that integrates longitudinal learning trajectories, standardized clinical assessments, and randomized trial data to advance pedi…

KidsNanny is a two-stage multimodal content moderation pipeline that achieves high accuracy and efficiency in detecting child safety threats, particularly excelling in text-embedded content.

The paper analyzes current and proposed age verification methods and proposes an alternative using open standards and cryptography to achieve secure, privacy-preserving age checking.

This paper introduces BOUTEF, a large-scale multilingual corpus for fake news in North Africa, and finds that fake news leverages emotionally charged, hybrid linguistic practices to enhance virality,…

The paper introduces a generalized zero-shot benchmark for facial age estimation that ethically excludes children's data during training, demonstrating that current state-of-the-art models fail signif…

This paper investigates neural activity during five auditory conditions using EEG recordings from a 5-year-old participant, revealing condition-specific modulation of neural oscillatory activity and d…

The paper introduces ChildEval, a large-scale benchmark designed to systematically evaluate how well large language models can infer and follow complex, child-specific preferences during long-context…

The paper introduces PIGMENT, a physics-informed foundation model that enables reliable quantitative mapping of brain microstructure from extremely sparse or challenging diffusion MRI scans.

This paper introduces PRIME, a method for restoring learning capacity in multi-agent reinforce learning by verifying neuron dormancy and gradient silence before resetting.

The paper introduces a novel production-based evaluation showing that child-directed speech (CDS) significantly improves a BabyLM's ability to generate grammatically correct language, even if standard…

This paper demonstrates a proof-of-concept method using top-view video to detect 'Pen-Up' states in handwriting, showing it can reliably complement traditional digitizing tablets for developmental dis…

This study analyzes global usage patterns of generative AI among early adopters, finding that usage varies significantly by country income, with schooling being the primary use in low-income countries…

The paper introduces MOMENT, a framework for selecting and estimating random-effects covariance matrices and fixed-effects coefficients using moment-based methods, inducing sparsity through a positive…

The paper tracks the developmental emergence of attention circuits in 1B-class language models, finding that the formation of induction and attention-sink circuits are distinct, temporally separated t…

The paper establishes bijections between tree records, girth of connected endofunctions, and genesis sequences, deriving generating functions for tree and forest record numbers using Cayley's tree fun…