Latest AI and machine learning research in schizophrenia for healthcare professionals.
The deployment of large language models (LLMs) for science carries an intrinsic risk: hallucination of citations, fabricated drug approvals or clinical trials, and unsupported experimental outcomes. Here we describe the testing and deployment of a novel systematic, multi-layer approach called the Validation as a System (VaaS) pipeline, iteratively developed during the construction of an open-sourc...
Accurate prediction of drug-target binding affinity across multiple pharmacological endpoints remains challenging, as most deep learning methodologies focus on a single metric and face a trade-off between incorporating structural information and computational throughput. Here we present OmniBind, a multitask framework that resolves both constraints by encoding protein tertiary structures as discre...
Self-supervised Vision Transformers (ViTs) like DINO show an emergent ability to discover objects, typically observed in [CLS] token attention maps of...
Recent work has questioned whether large language models (LLMs) can perform genuine in-context learning (ICL) for scientific experimental design, with...
Accurate uncertainty quantification is crucial for making reliable decisions in various supervised learning scenarios, particularly when dealing with ...
Large vision-language models (LVLMs) tend to hallucinate, especially when visual inputs are corrupted at test time. We show that such corruptions act ...
Large vision-language models (LVLMs) are increasingly being applied to multi-view image inputs captured from diverse viewpoints. However, despite this...
Object hallucination in Large Vision-Language Models (LVLMs) severely compromises their reliability in real-world applications, posing a critical barr...
Vision-language models (VLMs) adapted to the medical domain have shown strong performance on visual question answering benchmarks, yet their robustnes...
Artistic font generation aims to synthesize stylized glyphs based on a reference style. However, existing approaches suffer from limited style diversi...
Multimodal Large Language Models (MLLMs) suffer from hallucinations. Existing hallucination evaluation benchmarks are often limited by over-simplified...
Background: Conversational AI safety systems are primarily evaluated using message-level content monitoring, which assesses inputs and outputs in isol...
When VLMs answer correctly, do they genuinely rely on visual information or exploit language shortcuts? We introduce the Tri-Layer Diagnostic Framewor...
Visual-Language Models (VLMs) have achieved remarkable progress in image captioning, visual question answering, and visual reasoning. Yet they remain ...
Rapid behavioral adaptation requires the brain to solve a fundamental computational dilemma: how to flexibly update learned rules while maintaining st...
We present Omni-I2C, a comprehensive benchmark designed to evaluate the capability of Large Multimodal Models (LMMs) in converting complex, structured...
Multimodal large language models (MLLMs) struggle with hallucinations, particularly with fine-grained queries, a challenge underrepresented by existin...
Vision-Language Models (VLMs) offer significant potential in computational pathology by enabling interpretable image analysis, automated reporting, an...
Large vision-language models (LVLMs) have become increasingly strong but remain prone to hallucinations in multimodal tasks, which significantly narro...
Objective. We establish a principled method for inferring mental health related psychometric variables from neural and behavioral data using the Impli...