arXiv Machine Learning

Multilayer Q-Matrix-Embedded Neural Network for Cognitive Diagnosis (M-QCDNet): Structure-Aware Deep Learning Architecture for Psychometric Interpretability

arXiv:2607. 01278v1 Announce Type: new Abstract: The research proposes a multilayer Q-matrix-embedded neural network for cognitive diagnosis (M-QCDNet), which integrates the structural interpretability of cognitive diagnostic models (CDMs) with the deep learning neural network (NN).

arXiv Machine Learning

Jun 8

Interpreting Learning Under Competing Models: Joint and Stepwise Approaches for Dynamic Cognitive Diagnosis

arXiv:2606. 06804v1 Announce Type: new Abstract: Digital learning environments record learners' responses to individual items, making it possible to study the development of specific skills rather than overall scores.

By Yawen Ma, Sahoko Ishida, Kate Cain, Gabriel Wallin

rag safety

arXiv AI

Jun 29

From Black-Box to Clinical Insight: A Multi-Stage Explainable Framework for Speech-Based Cognitive Impairment Detection

arXiv:2606. 27973v1 Announce Type: cross Abstract: Speech-based cognitive impairment detection offers a noninvasive, accessible alternative to costly biomarker assays, yet transformer-based models remain clinically uninterpretable.

By Yasaman Haghbin, Sina Rashidi, Ali Zolnour, Fatemeh Taherinezhad, Ali Fartoot, Hossein Azadmaleki, James M Noble, Maryam Dadkhah, Maryam Zolnoori

llms multimodal benchmarks safety

arXiv AI

Jun 2

Brain-Atlas-Guided Generative Counterfactual Attention for Explainable Cognitive Decline Diagnosis Using Multimodal Connectomes

arXiv:2606. 01237v1 Announce Type: new Abstract: Mild cognitive impairment (MCI) and subjective cognitive decline (SCD) are closely associated with the early Alzheimer's disease continuum, where accurate and explainable diagnosis is important for early risk assessment and intervention.

By Xiongri Shen, Jiaqi Wang, Zhenxi Song, Yi Zhong, Leilei Zhao, Xin He, Baiying Lei, Zhiguo Zhang

llms multimodal benchmarks safety

arXiv AI

Jul 3

MMIR-TCM: Memory-Integrated Multimodal Inference and Retrieval for TCM Clinical Decision Support

arXiv:2607. 01814v1 Announce Type: new Abstract: Traditional Chinese Medicine (TCM) diagnosis, particularly through tongue inspection, faces persistent challenges in subjectivity and reproducibility.

By Lihui Luo, Joongwon Chae, Ziyan Chen, Yang Liu, Siyi Cheng, Weihan Gao, Zelin Zeng, Xiaoming Yin, Samaneh Beheshti Kashi, Dongmei Yu, Lian Zhang, Jing Sui, Zeming Liang, Jiansong Ji, Peter E. Lobie, Peiwu Qin

llms rag computer-vision fine-tuning multimodal benchmarks

arXiv AI

1d ago

Do Assessment Instruments Measure the Same Thing for Humans and LLMs? A Latent Structure Analysis

arXiv:2608. 15630v1 Announce Type: cross Abstract: The rapid development and growing deployment of large language models (LLMs) have made it increasingly important to understand their capabilities.

By Alona Strugatski, Licol Zeinfeld, Giora Alexandron

llms multimodal

arXiv AI

Aug 10

Aftab: A Comprehensive Benchmark of CNN Encoders and Advanced Value Functions in Parallelized Q-Networks

arXiv:2608. 07335v1 Announce Type: cross Abstract: Recent advancements in deep reinforcement learning have increasingly favored simplified, highly parallelized paradigms.

By Taha Shieenavaz, Shabnam Zareshahraki, Loris Nanni

reinforcement-learning benchmarks

arXiv AI

Jun 2

MedSynapse-V: Bridging Visual Perception and Clinical Intuition via Latent Memory Evolution

arXiv:2604. 26283v3 Announce Type: replace-cross Abstract: High-precision medical diagnosis relies not only on static imaging features but also on the implicit diagnostic memory experts instantly invoke during image interpretation.

By Chunzheng Zhu, Jiaqi Zeng, Junyu Jiang, Jianxin Lin, Yijun Wang

agents nlp reinforcement-learning efficiency benchmarks safety

arXiv AI

Jun 9

Baichuan-M4: A Clinical-Grade Medical Agent System for Continuous Care

arXiv:2606. 08982v1 Announce Type: new Abstract: Baichuan-M4 is Baichuan Intelligence's clinical-grade medical large model, designed for \emph{continuous care} rather than single-turn medical question answering.

By Aiyuan Yang, Chengfeng Dou, Da Pan, Dian Wang, Fan Yang, Fei Deng, Fei Li, Guangwei Ai, Hui Liu, Hongda Zhang, Jinyang Tai, Kai Lu, Lijun Liu, Linwei Chen, Linyu Li, Meiqing Guo, Peidong Guo, Qiang Ju, Rihui Xin, Shuai Wang, XinKai Ma, Xudong Chen, Yichuan Mo, Canbin Piao, Leyi Pan, Yihe Luo, Zian Wang

agents computer-vision nlp reinforcement-learning multimodal safety

arXiv Machine Learning

Jun 29

Parameter-Efficient Continuous-Variable Photonic Quantum Neural Networks for Edge Quantum AI: Demonstration in Oral Cancer Detection

arXiv:2606. 28252v1 Announce Type: cross Abstract: Early detection of oral cancer markedly improves clinical outcomes, yet specialized diagnostic tools remain scarce in low-resource settings.

By Akshay Bhagwan Sonawane, Sophie Choe, Lakshman Tamil

computer-vision efficiency

arXiv AI

Jul 3

Enhancing Fitness Intelligence through Domain-Specific LLM Post-Training

arXiv:2607. 02118v1 Announce Type: new Abstract: Scientific Fitness Coaching (SFC) is typically delivered by human professionals, making it costly and inaccessible to many.

By Xingtao Zhao, Tian Yang, Han Jiang

llms reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Jun 5

LEVANTE-bench: Multi-Scale Comparison of VLMs to Children Using Cognitive Tasks (or, "Is Your VLM Smarter Than a 5th Grader?")

arXiv:2606. 05497v1 Announce Type: new Abstract: Given the inherently multimodal nature of human experience, vision-language models (VLMs) hold substantial promise for modeling human cognition as it grows and develops with experience.

By Alvin Wei Ming Tan, David Cardinal, Tania Lorido-Botran, Laura Bravo-Sanchez, Sunny Yu, Michael C. Frank

llms multimodal benchmarks safety

arXiv AI

Jun 11

Multimodal Ordinal Modeling of Alzheimer's Disease Severity Using Structural MRI and Clinical Data

arXiv:2606. 11794v1 Announce Type: cross Abstract: Neurodegenerative diseases such as Alzheimer's disease (AD) require accurate and scalable tools for assessing disease severity, yet current clinical staging remains time-intensive and prone to variability.

By Boris-Stephan Rauchmann, Jonathan Laib, Buse Ercik, Robert Perneczky, Sergio Altares-L\'opez

multimodal

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