arXiv:2511.03376v1 Announce Type: cross Abstract: We present a framework that combines Large Language Models with computational image analytics for non-invasive, zero-shot prediction of IDH mutation status in brain gliomas. For each subject, coregistered multi-parametric MRI scans and multi-class tumor segmentation maps were processed to extract interpretable semantic (visual) attributes and quantitative features, serialized in a […]
Voost: A Unified and Scalable Diffusion Transformer for Bidirectional Virtual Try-On and Try-Off
arXiv:2508.04825v2 Announce Type: replace-cross Abstract: Virtual try-on aims to synthesize a realistic image of a person wearing a target garment, but accurately modeling garment-body correspondence remains a persistent challenge, especially under pose and appearance variation. In this paper, we propose Voost – a unified and scalable framework that jointly learns virtual try-on and try-off with […]
CareMedEval dataset: Evaluating Critical Appraisal and Reasoning in the Biomedical Field
arXiv:2511.03441v1 Announce Type: cross Abstract: Critical appraisal of scientific literature is an essential skill in the biomedical field. While large language models (LLMs) can offer promising support in this task, their reliability remains limited, particularly for critical reasoning in specialized domains. We introduce CareMedEval, an original dataset designed to evaluate LLMs on biomedical critical appraisal […]
Toward Autonomous Engineering Design: A Knowledge-Guided Multi-Agent Framework
arXiv:2511.03179v1 Announce Type: new Abstract: The engineering design process often demands expertise from multiple domains, leading to complex collaborations and iterative refinements. Traditional methods can be resource-intensive and prone to inefficiencies. To address this, we formalize the engineering design process through a multi-agent AI framework that integrates structured design and review loops. The framework introduces […]
Efficient Neural Networks with Discrete Cosine Transform Activations
arXiv:2511.03531v1 Announce Type: cross Abstract: In this paper, we extend our previous work on the Expressive Neural Network (ENN), a multilayer perceptron with adaptive activation functions parametrized using the Discrete Cosine Transform (DCT). Building upon previous work that demonstrated the strong expressiveness of ENNs with compact architectures, we now emphasize their efficiency, interpretability and pruning […]
DE3S: Dual-Enhanced Soft-Sparse-Shape Learning for Medical Early Time-Series Classification
arXiv:2510.12214v2 Announce Type: replace-cross Abstract: Early Time Series Classification (ETSC) is critical in time-sensitive medical applications such as sepsis, yet it presents an inherent trade-off between accuracy and earliness. This trade-off arises from two core challenges: 1) models should effectively model inherently weak and noisy early-stage snippets, and 2) they should resolve the complex, dual […]
AILA–First Experiments with Localist Language Models
arXiv:2511.03559v1 Announce Type: cross Abstract: This paper presents the first empirical demonstration of controllable locality in transformer language models, a novel architectural framework that enables continuous control over the degree of representation localization through a tunable locality dial parameter. Unlike traditional language models that rely exclusively on distributed representations, our approach allows dynamic interpolation between […]
Adobe Summit Concierge Evaluation with Human in the Loop
arXiv:2511.03186v1 Announce Type: new Abstract: Generative AI assistants offer significant potential to enhance productivity, streamline information access, and improve user experience in enterprise contexts. In this work, we present Summit Concierge, a domain-specific AI assistant developed for Adobe Summit. The assistant handles a wide range of event-related queries and operates under real-world constraints such as […]
PerfDojo: Automated ML Library Generation for Heterogeneous Architectures
arXiv:2511.03586v1 Announce Type: cross Abstract: The increasing complexity of machine learning models and the proliferation of diverse hardware architectures (CPUs, GPUs, accelerators) make achieving optimal performance a significant challenge. Heterogeneity in instruction sets, specialized kernel requirements for different data types and model features (e.g., sparsity, quantization), and architecture-specific optimizations complicate performance tuning. Manual optimization is […]
Quantifying truth and authenticity in AI-assisted candidate evaluation: A multi-domain pilot analysis
arXiv:2511.00774v2 Announce Type: replace-cross Abstract: This paper presents a retrospective analysis of anonymized candidate-evaluation data collected during pilot hiring campaigns conducted through AlteraSF, an AI-native resume-verification platform. The system evaluates resume claims, generates context-sensitive verification questions, and measures performance along quantitative axes of factual validity and job fit, complemented by qualitative integrity detection. Across six […]