arXiv:2510.11743v1 Announce Type: new Abstract: Synthetic molecular communication (MC) in the cardiovascular system (CVS) is a key enabler for many envisioned medical applications in the human body, such as targeted drug delivery, early cancer detection, and continuous health monitoring. The design of MC systems for such applications requires suitable models for the signaling molecule propagation […]
Credal Transformer: A Principled Approach for Quantifying and Mitigating Hallucinations in Large Language Models
arXiv:2510.12137v1 Announce Type: cross Abstract: Large Language Models (LLMs) hallucinate, generating factually incorrect yet confident assertions. We argue this stems from the Transformer’s Softmax function, which creates “Artificial Certainty” by collapsing ambiguous attention scores into a single probability distribution, discarding uncertainty information at each layer. To fix this, we introduce the Credal Transformer, which replaces […]
DMSC: Dynamic Multi-Scale Coordination Framework for Time Series Forecasting
arXiv:2508.02753v3 Announce Type: replace-cross Abstract: Time Series Forecasting (TSF) faces persistent challenges in modeling intricate temporal dependencies across different scales. Despite recent advances leveraging different decomposition operations and novel architectures based on CNN, MLP or Transformer, existing methods still struggle with static decomposition strategies, fragmented dependency modeling, and inflexible fusion mechanisms, limiting their ability to […]
MoRA: On-the-fly Molecule-aware Low-Rank Adaptation Framework for LLM-based Multi-Modal Molecular Assistant
arXiv:2510.12245v1 Announce Type: cross Abstract: Effectively integrating molecular graph structures with Large Language Models (LLMs) is a key challenge in drug discovery. Most existing multi-modal alignment methods typically process these structures by fine-tuning the LLM or adding a static adapter simultaneously. However, these approaches have two main limitations: (1) it optimizes a shared parameter space […]
Genomic Influence of a Key Transcription Factor in Male Glandular Malignancy
arXiv:2510.11959v1 Announce Type: new Abstract: Prostate cancer (PCa) remains a significant global health concern among men, particularly due to the lethality of its more aggressive variants. Despite therapeutic advancements that have enhanced survival for many patients, high grade PCa continues to contribute substantially to cancer related mortality. Emerging evidence points to the MYB proto-oncogene as […]
Chinese ModernBERT with Whole-Word Masking
arXiv:2510.12285v1 Announce Type: cross Abstract: Encoder-only Transformers have advanced along three axes — architecture, data, and systems — yielding Pareto gains in accuracy, speed, and memory efficiency. Yet these improvements have not fully transferred to Chinese, where tokenization and morphology differ markedly from English. We introduce Chinese ModernBERT, a from-scratch Chinese encoder that couples: (i) […]
LiteVPNet: A Lightweight Network for Video Encoding Control in Quality-Critical Applications
arXiv:2510.12379v1 Announce Type: cross Abstract: In the last decade, video workflows in the cinema production ecosystem have presented new use cases for video streaming technology. These new workflows, e.g. in On-set Virtual Production, present the challenge of requiring precise quality control and energy efficiency. Existing approaches to transcoding often fall short of these requirements, either […]
Holistic Agent Leaderboard: The Missing Infrastructure for AI Agent Evaluation
arXiv:2510.11977v1 Announce Type: new Abstract: AI agents have been developed for complex real-world tasks from coding to customer service. But AI agent evaluations suffer from many challenges that undermine our understanding of how well agents really work. We introduce the Holistic Agent Leaderboard (HAL) to address these challenges. We make three main contributions. First, we […]
BoN Appetit Team at LeWiDi-2025: Best-of-N Test-time Scaling Can Not Stomach Annotation Disagreements (Yet)
arXiv:2510.12516v1 Announce Type: cross Abstract: Test-time scaling is a family of techniques to improve LLM outputs at inference time by performing extra computation. To the best of our knowledge, test-time scaling has been limited to domains with verifiably correct answers, like mathematics and coding. We transfer test-time scaling to the LeWiDi-2025 tasks to evaluate annotation […]
Aixel: A Unified, Adaptive and Extensible System for AI-powered Data Analysis
arXiv:2510.12642v1 Announce Type: cross Abstract: A growing trend in modern data analysis is the integration of data management with learning, guided by accuracy, latency, and cost requirements. In practice, applications draw data of different formats from many sources. In the meanwhile, the objectives and budgets change over time. Existing systems handle these applications across databases, […]
Learning-To-Measure: In-context Active Feature Acquisition
arXiv:2510.12624v1 Announce Type: cross Abstract: Active feature acquisition (AFA) is a sequential decision-making problem where the goal is to improve model performance for test instances by adaptively selecting which features to acquire. In practice, AFA methods often learn from retrospective data with systematic missingness in the features and limited task-specific labels. Most prior work addresses […]
CGBench: Benchmarking Language Model Scientific Reasoning for Clinical Genetics Research
arXiv:2510.11985v1 Announce Type: new Abstract: Variant and gene interpretation are fundamental to personalized medicine and translational biomedicine. However, traditional approaches are manual and labor-intensive. Generative language models (LMs) can facilitate this process, accelerating the translation of fundamental research into clinically-actionable insights. While existing benchmarks have attempted to quantify the capabilities of LMs for interpreting scientific […]