arXiv:2604.00505v3 Announce Type: replace-cross Abstract: Overparameterized neural networks often show a benign overfitting property in the sense of achieving excellent generalization behavior despite the number of parameters exceeding the number of training examples. A promising direction to explain benign overfitting is to relate generalization to the norm of distance from initialization, motivated by the empirical […]
Spotlights and Blindspots: Evaluating Machine-Generated Text Detection
arXiv:2604.16607v2 Announce Type: replace-cross Abstract: With the rise of generative language models, machine-generated text detection has become a critical challenge. A wide variety of models is available, but inconsistent datasets, evaluation metrics, and assessment strategies obscure comparisons of model effectiveness. To address this, we evaluate 15 different detection models from six distinct systems, as well […]
Indirect Prey-taxis VS a Shortwave External Signal in Multiple Dimensions
arXiv:2604.20469v1 Announce Type: cross Abstract: We address a short-wave asymptotic for one class of quasi-linear second order PDE systems involving the cross-diffusion described by the so-called Patlak–Keller–Segel law. It is common to employ these equations for modelling the predator–prey community with the prey-taxis that means the interactions of two species of particles or cells or […]
LayerTracer: A Joint Task-Particle and Vulnerable-Layer Analysis framework for Arbitrary Large Language Model Architectures
arXiv:2604.20556v1 Announce Type: cross Abstract: Currently, Large Language Models (LLMs) feature a diversified architectural landscape, including traditional Transformer, GateDeltaNet, and Mamba. However, the evolutionary laws of hierarchical representations, task knowledge formation positions, and network robustness bottleneck mechanisms in various LLM architectures remain unclear, posing core challenges for hybrid architecture design and model optimization. This paper […]
A Field Guide to Decision Making
arXiv:2604.20669v1 Announce Type: cross Abstract: High-consequence decision making demands peak performance from individuals in positions of responsibility. Such executive authority bears the obligation to act despite uncertainty, limited resources, time constraints, and accountability risks. Tools and strategies to motivate confidence and foster risk tolerance must confront informational noise and can provide qualified accountability. Machine intelligence […]
Anchor-and-Resume Concession Under Dynamic Pricing for LLM-Augmented Freight Negotiation
arXiv:2604.20732v1 Announce Type: cross Abstract: Freight brokerages negotiate thousands of carrier rates daily under dynamic pricing conditions where models frequently revise targets mid-conversation. Classical time-dependent concession frameworks use a fixed shape parameter $beta$ that cannot adapt to these updates. Deriving $beta$ from the live spread enables adaptation but introduces a new problem: a pricing shift […]
Closing the Domain Gap in Biomedical Imaging by In-Context Control Samples
arXiv:2604.20824v1 Announce Type: cross Abstract: The central problem in biomedical imaging are batch effects: systematic technical variations unrelated to the biological signal of interest. These batch effects critically undermine experimental reproducibility and are the primary cause of failure of deep learning systems on new experimental batches, preventing their practical use in the real world. Despite […]
A novel attention mechanism for noise-adaptive and robust segmentation of microtubules in microscopy images
arXiv:2507.07800v3 Announce Type: replace Abstract: Segmenting cytoskeletal filaments in microscopy images is essential for studying their roles in cellular processes. However, this task is highly challenging due to the fine, densely packed, and intertwined nature of these structures. Imaging limitations further complicate analysis. While deep learning has advanced segmentation of large, well-defined biological structures, its […]
Foundation Models in Biomedical Imaging: Turning Hype into Reality
arXiv:2512.15808v2 Announce Type: replace Abstract: Foundation models (FMs) are driving a prominent shift in biomedical imaging from task-specific models to unified backbone models for diverse tasks. This opens an avenue to integrate imaging, pathology, clinical records, and genomics data into a composite system. However, this vision contrasts sharply with modern medicine’s trajectory toward more granular […]
Model Space Reasoning as Search in Feedback Space for Planning Domain Generation
arXiv:2604.08712v3 Announce Type: replace Abstract: The generation of planning domains from natural language descriptions remains an open problem even with the advent of large language models and reasoning models. Recent work suggests that while LLMs have the ability to assist with domain generation, they are still far from producing high quality domains that can be […]
PersonalHomeBench: Evaluating Agents in Personalized Smart Homes
arXiv:2604.16813v2 Announce Type: replace Abstract: Agentic AI systems are rapidly advancing toward real-world applications, yet their readiness in complex and personalized environments remains insufficiently characterized. To address this gap, we introduce PersonalHomeBench, a benchmark for evaluating foundation models as agentic assistants in personalized smart home environments. The benchmark is constructed through an iterative process that […]
PR-CAD: Progressive Refinement for Unified Controllable and Faithful Text-to-CAD Generation with Large Language Models
arXiv:2604.19773v1 Announce Type: cross Abstract: The construction of CAD models has traditionally relied on labor-intensive manual operations and specialized expertise. Recent advances in large language models (LLMs) have inspired research into text-to-CAD generation. However, existing approaches typically treat generation and editing as disjoint tasks, limiting their practicality. We propose PR-CAD, a progressive refinement framework that […]