arXiv:2510.23783v1 Announce Type: new Abstract: DNA cytosine methylation is a critical epigenetic mark regulating gene expression and thus playing an important role in development and differentiation across eukaryotes. Existing tools for high-throughput methylation analysis often lack cross-species flexibility or require command-line expertise. We present Methylator, a novel, end-to-end DNA methylation analysis framework integrated into the […]
PRO: Enabling Precise and Robust Text Watermark for Open-Source LLMs
arXiv:2510.23891v1 Announce Type: cross Abstract: Text watermarking for large language models (LLMs) enables model owners to verify text origin and protect intellectual property. While watermarking methods for closed-source LLMs are relatively mature, extending them to open-source models remains challenging, as developers cannot control the decoding process. Consequently, owners of open-source LLMs lack practical means to […]
Data Leakage and Deceptive Performance: A Critical Examination of Credit Card Fraud Detection Methodologies
arXiv:2506.02703v2 Announce Type: replace-cross Abstract: The art and science of Quranic recitation (Tajweed), a discipline governed by meticulous phonetic, rhythmic, and theological principles, confronts substantial educational challenges in today’s digital age. Although modern technology offers unparalleled opportunities for learning, existing automated systems for evaluating recitation have struggled to gain broad acceptance or demonstrate educational effectiveness. […]
Scalable GPU-Based Integrity Verification for Large Machine Learning Models
arXiv:2510.23938v1 Announce Type: cross Abstract: We present a security framework that strengthens distributed machine learning by standardizing integrity protections across CPU and GPU platforms and significantly reducing verification overheads. Our approach co-locates integrity verification directly with large ML model execution on GPU accelerators, resolving the fundamental mismatch between how large ML workloads typically run (primarily […]
Why Foundation Models in Pathology Are Failing
arXiv:2510.23807v1 Announce Type: new Abstract: In non-medical domains, foundation models (FMs) have revolutionized computer vision and language processing through large-scale self-supervised and multimodal learning. Consequently, their rapid adoption in computational pathology was expected to deliver comparable breakthroughs in cancer diagnosis, prognostication, and multimodal retrieval. However, recent systematic evaluations reveal fundamental weaknesses: low diagnostic accuracy, poor […]
Diffusion Adaptive Text Embedding for Text-to-Image Diffusion Models
arXiv:2510.23974v1 Announce Type: cross Abstract: Text-to-image diffusion models rely on text embeddings from a pre-trained text encoder, but these embeddings remain fixed across all diffusion timesteps, limiting their adaptability to the generative process. We propose Diffusion Adaptive Text Embedding (DATE), which dynamically updates text embeddings at each diffusion timestep based on intermediate perturbed data. We […]
OmniVinci: Enhancing Architecture and Data for Omni-Modal Understanding LLM
arXiv:2510.15870v2 Announce Type: replace-cross Abstract: Advancing machine intelligence requires developing the ability to perceive across multiple modalities, much as humans sense the world. We introduce OmniVinci, an initiative to build a strong, open-source, omni-modal LLM. We carefully study the design choices across model architecture and data curation. For model architecture, we present three key innovations: […]
SpecKD: Speculative Decoding for Effective Knowledge Distillation of LLMs
arXiv:2510.24021v1 Announce Type: cross Abstract: Knowledge Distillation (KD) has become a cornerstone technique for compressing Large Language Models (LLMs) into smaller, more efficient student models. However, conventional KD approaches typically apply the distillation loss uniformly across all tokens, regardless of the teacher’s confidence. This indiscriminate mimicry can introduce noise, as the student is forced to […]
ReCAP: Recursive Context-Aware Reasoning and Planning for Large Language Model Agents
arXiv:2510.23822v1 Announce Type: new Abstract: Long-horizon tasks requiring multi-step reasoning and dynamic re-planning remain challenging for large language models (LLMs). Sequential prompting methods are prone to context drift, loss of goal information, and recurrent failure cycles, while hierarchical prompting methods often weaken cross-level continuity or incur substantial runtime overhead. We introduce ReCAP (Recursive Context-Aware Reasoning […]
SynAD: Enhancing Real-World End-to-End Autonomous Driving Models through Synthetic Data Integration
arXiv:2510.24052v1 Announce Type: cross Abstract: Recent advancements in deep learning and the availability of high-quality real-world driving datasets have propelled end-to-end autonomous driving. Despite this progress, relying solely on real-world data limits the variety of driving scenarios for training. Synthetic scenario generation has emerged as a promising solution to enrich the diversity of training data; […]