arXiv:2605.07051v1 Announce Type: new
Abstract: Large Language Models (LLMs) have shown good performance on various science educational benchmarks, demonstrating their potential for use in science and mathematics education. Yet, LLMs tend to be evalua…
arXiv:2605.07919v1 Announce Type: new
Abstract: Medical vision–language models (VLMs) are usually evaluated on intact image–question pairs, but trustworthy clinical use requires a stronger property: a model must recognise when the evidential basis f…
arXiv:2605.07517v1 Announce Type: cross
Abstract: Retrieval-Augmented Generation (RAG) enhances the factual grounding of Large Language Models by conditioning their outputs on external documents. However, standard embedding-based retrievers treat natu…
arXiv:2605.08084v1 Announce Type: new
Abstract: The pursuit of autonomous driving has produced one of the richest sensor data collections in all of robotics. However, its scale and diversity remain largely untapped. Each dataset adopts different 2D an…
arXiv:2605.07053v1 Announce Type: new
Abstract: Benchmarks like GSM8K are popular measures of mathematical reasoning, but leaderboard gains can overstate true capability due to memorization of fixed test sets. Most robustness variants apply surface-le…
arXiv:2605.07039v1 Announce Type: new
Abstract: Large language models have become drivers of evolutionary search, but most systems rely on a fixed, prompt-elicited policy to sample next candidates. This limits adaptation in practical engineering and r…
arXiv:2604.16579v2 Announce Type: replace-cross
Abstract: Automated multimodal depression estimation in unconstrained environments is inherently challenged by naturalistic noise and complex behavioral variability. Prevailing deterministic methods, how…
arXiv:2604.26509v3 Announce Type: replace
Abstract: Embodied AI and robotic systems increasingly depend on scalable, diverse, and physically grounded 3D content for simulation-based training and real-world deployment. While 3D generative modeling has …
arXiv:2605.06820v1 Announce Type: cross
Abstract: Deep learning-based organs/structures-at-risk(OARs) auto-contouring models can improve radiotherapy workflows, but models trained on adult data often underperform in pediatric patients. Developing robu…
arXiv:2603.06859v2 Announce Type: replace-cross
Abstract: Removing an agent from a cooperative team to measure its contribution seems natural, yet in multi-agent LLM systems this evaluation distorts the result it claims to measure. This failure is not…