arXiv:2512.07094v2 Announce Type: replace Abstract: Agentic LLM frameworks promise autonomous behavior via task decomposition, tool use, and iterative planning, but most deployed systems remain brittle. They lack runtime introspection, cannot diagnose their own failure modes, and do not improve over time without human intervention. In practice, many agent stacks degrade into decorated chains of LLM […]
Frequency Locking to Environmental Forcing Suppresses Oscillatory Extinction in Phage-Bacteria Interactions
arXiv:2512.08224v1 Announce Type: cross Abstract: Bacteriophage-bacteria interactions are central to microbial ecology, influencing evolution, biogeochemical cycles, and pathogen behavior. Most theoretical models assume static environments and passive bacterial hosts, neglecting the joint effects of bacterial traits and environmental fluctuations on coexistence dynamics. This limitation hinders the prediction of microbial persistence in dynamic ecosystems such as […]
needLR: Long-read structural variant annotation with population-scale frequency estimation
arXiv:2512.08175v1 Announce Type: new Abstract: Summary: We present needLR, a structural variant (SV) annotation tool that can be used for filtering and prioritization of candidate pathogenic SVs from long-read sequencing data using population allele frequencies, annotations for genomic context, and gene-phenotype associations. When using population data from 500 presumably healthy individuals to evaluate nine test […]
Residual-SwinCA-Net: A Channel-Aware Integrated Residual CNN-Swin Transformer for Malignant Lesion Segmentation in BUSI
arXiv:2512.08243v1 Announce Type: cross Abstract: A novel deep hybrid Residual-SwinCA-Net segmentation framework is proposed in the study for addressing such challenges by extracting locally correlated and robust features, incorporating residual CNN modules. Furthermore, for learning global dependencies, Swin Transformer blocks are customized using internal residual pathways, which reinforce gradient stability, refine local patterns, and facilitate […]
The Missing Point in Vision Transformers for Universal Image Segmentation
arXiv:2505.19795v2 Announce Type: replace-cross Abstract: Image segmentation remains a challenging task in computer vision, demanding robust mask generation and precise classification. Recent mask-based approaches yield high-quality masks by capturing global context. However, accurately classifying these masks, especially in the presence of ambiguous boundaries and imbalanced class distributions, remains an open challenge. In this work, we […]
Systematization of Knowledge: Security and Safety in the Model Context Protocol Ecosystem
arXiv:2512.08290v1 Announce Type: cross Abstract: The Model Context Protocol (MCP) has emerged as the de facto standard for connecting Large Language Models (LLMs) to external data and tools, effectively functioning as the “USB-C for Agentic AI.” While this decoupling of context and execution solves critical interoperability challenges, it introduces a profound new threat landscape where […]
Spatially-extended Flow Phixer (SpeF-Phixer): A Spatially Extended $varphi$-Mixing Framework for Gene Regulatory Causal Inference in Spatial Gene Field
arXiv:2512.08202v1 Announce Type: new Abstract: Background and objective: Spatial transcriptomics provides rich spatial context but lacks sufficient resolution for large-scale causal inference. We developed SpeF-Phixer, a spatially extended phi-mixing framework integrating whole-slide image (WSI)-derived spatial cell distributions with mapped scRNA-seq expression fields to infer directed gene regulatory triplets with spatial coherence. Methods: Using CD103/CD8-immunostained colorectal […]
Interpreting Structured Perturbations in Image Protection Methods for Diffusion Models
arXiv:2512.08329v1 Announce Type: cross Abstract: Recent image protection mechanisms such as Glaze and Nightshade introduce imperceptible, adversarially designed perturbations intended to disrupt downstream text-to-image generative models. While their empirical effectiveness is known, the internal structure, detectability, and representational behavior of these perturbations remain poorly understood. This study provides a systematic, explainable AI analysis using a […]
Amortized Bayesian Meta-Learning for Low-Rank Adaptation of Large Language Models
arXiv:2508.14285v2 Announce Type: replace-cross Abstract: Fine-tuning large language models (LLMs) with low-rank adaptation (LoRA) is a cost-effective way to incorporate information from a specific dataset. However, it is often unclear how well the fine-tuned LLM will generalize, i.e., how well it will perform on unseen datasets. Methods have been proposed to improve generalization by optimizing […]
Biothreat Benchmark Generation Framework for Evaluating Frontier AI Models II: Benchmark Generation Process
arXiv:2512.08451v1 Announce Type: cross Abstract: The potential for rapidly-evolving frontier artificial intelligence (AI) models, especially large language models (LLMs), to facilitate bioterrorism or access to biological weapons has generated significant policy, academic, and public concern. Both model developers and policymakers seek to quantify and mitigate any risk, with an important element of such efforts being […]