arXiv:2605.02916v1 Announce Type: new
Abstract: The development of AI-assisted Early Intensive Behavioral Intervention (EIBI) for Autism Spectrum Disorder (ASD) is severely constrained by data scarcity. Furthermore, while Applied Behavior Analysis (AB…
arXiv:2605.02913v1 Announce Type: new
Abstract: Reinforcement learning (RL) has become a central post-training tool for improving the reasoning abilities of large language models (LLMs). In these systems, the rollout, the trajectory sampled from a pro…
arXiv:2605.01482v1 Announce Type: new
Abstract: Multi-Hop Fact Verification (MHFV) necessitates complex reasoning across disparate evidence, posing significant challenges for Large Language Models (LLMs) which often suffer from hallucinations and frac…
arXiv:2605.02906v1 Announce Type: new
Abstract: In the field of software operations, Large Language Models (LLMs) have attracted increasing attention. However, existing research has not yet achieved efficient and effective end-to-end intelligent opera…
arXiv:2605.02917v1 Announce Type: cross
Abstract: Supervised deep learning models for automated CTG analysis are typically constrained by narrowly curated labelled datasets and limited patient cohorts, leaving substantial volumes of physiologically in…
arXiv:2605.01507v1 Announce Type: new
Abstract: Prior studies report that partial driving automation can increase the cognitive demands on human drivers. This effect largely arises from human drivers’ lack of transparent insight into the vehicle’s int…
arXiv:2508.14994v3 Announce Type: replace-cross
Abstract: In hazardous and remote environments, robotic systems perform critical tasks demanding improved safety and efficiency. Among these, quadruped robots with manipulator arms offer mobility and ver…
arXiv:2605.02187v1 Announce Type: cross
Abstract: Bring-Your-Own-Key (BYOK) agent architectures let users route LLM traffic through third-party relays, creating a critical integrity gap: a malicious relay can modify an aligned LLM response after gener…
arXiv:2605.02918v1 Announce Type: cross
Abstract: Variational autoencoders are widely used for unsupervised anomaly detection. Model selection however remains an open-question: to remain fully unsupervised, hyperparameters are often chosen to minimize…
arXiv:2605.01566v1 Announce Type: new
Abstract: Advances in inference methods have enabled language models to improve their predictions without additional training. These methods often prioritize raw performance over cost-effective compute usage. Howe…