IntroductionElectrocardiograms (ECGs) have long served as the standard method for cardiac monitoring. While ECGs are highly accurate and widely validated, they require direct skin contact, are sensitive to motion artifacts, and are not always practical for continuous or unobtrusive monitoring, limiting their generalization to real-world, dynamic environments. However, radar-based technologies offer a novel, non-invasive alternative […]
Open LLM-based actionable incidental finding extraction from [18F]fluorodeoxyglucose PET-CT radiology reports
IntroductionWe developed an open, large language model (LLM)-based pipeline to extract actionable incidental findings (AIFs) from [18F]fluorodeoxyglucose positron emission tomography-computed tomography ([18F]FDG PET-CT) reports. This imaging modality often uncovers AIFs, which can affect a patient’s treatment. The pipeline classifies reports for the presence of AIFs, extracts the relevant sentences, and stores the results in structured […]
Reassessing prediction in the brain: Pre-onset neural encoding during natural listening does not reflect pre-activation
arXiv:2412.19622v2 Announce Type: replace Abstract: Predictive processing theories propose that the brain continuously anticipates upcoming input. However, direct neural evidence for predictive pre-activation during natural language comprehension remains limited and debated. Previous studies using large language model (LLM)-based encoding models with fMRI and ECoG have reported pre-onset signals that appear to encode upcoming words, but […]
CharCom: Composable Identity Control for Multi-Character Story Illustration
arXiv:2510.10135v2 Announce Type: replace Abstract: Ensuring character identity consistency across varying prompts remains a fundamental limitation in diffusion-based text-to-image generation. We propose CharCom, a modular and parameter-efficient framework that achieves character-consistent story illustration through composable LoRA adapters, enabling efficient per-character customization without retraining the base model. Built on a frozen diffusion backbone, CharCom dynamically composes […]
ConCISE: A Reference-Free Conciseness Evaluation Metric for LLM-Generated Answers
arXiv:2511.16846v1 Announce Type: cross Abstract: Large language models (LLMs) frequently generate responses that are lengthy and verbose, filled with redundant or unnecessary details. This diminishes clarity and user satisfaction, and it increases costs for model developers, especially with well-known proprietary models that charge based on the number of output tokens. In this paper, we introduce […]
Reinforced Generation of Combinatorial Structures: Applications to Complexity Theory
arXiv:2509.18057v5 Announce Type: replace-cross Abstract: Can AI based methods help us make advances in complexity theory? We provide evidence towards answering this in the affirmative, using AlphaEvolve (an LLM code mutation agent) to obtain new results in three settings: a) We improve a recent result of Kunisky and Yu to obtain near-optimal upper and (conditional) […]
The Cooperative Network Architecture: Learning Structured Networks as Representation of Sensory Patterns
arXiv:2407.05650v5 Announce Type: replace-cross Abstract: We introduce the Cooperative Network Architecture (CNA), a model that represents sensory signals using structured, recurrently connected networks of neurons, termed “nets.” Nets are dynamically assembled from overlapping net fragments, which are learned based on statistical regularities in sensory input. This architecture offers robustness to noise, deformation, and generalization to […]
Wideband RF Radiance Field Modeling Using Frequency-embedded 3D Gaussian Splatting
arXiv:2505.20714v2 Announce Type: replace-cross Abstract: Indoor environments typically contain diverse RF signals distributed across multiple frequency bands, including NB-IoT, Wi-Fi, and millimeter-wave. Consequently, wideband RF modeling is essential for practical applications such as joint deployment of heterogeneous RF systems, cross-band communication, and distributed RF sensing. Although 3D Gaussian Splatting (3DGS) techniques effectively reconstruct RF radiance […]
CATCODER: Repository-Level Code Generation with Relevant Code and Type Context
arXiv:2406.03283v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks. However, repository-level code generation presents unique challenges, particularly due to the need to utilize information spread across multiple files within a repository. Specifically, successful generation depends on a solid grasp of both general, context-agnostic knowledge and specific, context-dependent […]
LLM-DSE: Searching Accelerator Parameters with LLM Agents
arXiv:2505.12188v3 Announce Type: replace-cross Abstract: Even though high-level synthesis (HLS) tools mitigate the challenges of programming domain-specific accelerators (DSAs) by raising the abstraction level, optimizing hardware directive parameters remains a significant hurdle. Existing heuristic and learning-based methods struggle with adaptability and sample efficiency. We present LLM-DSE, a multi-agent framework designed specifically for optimizing HLS directives. […]
Enhancing Quranic Learning: A Multimodal Deep Learning Approach for Arabic Phoneme Recognition
arXiv:2511.17477v1 Announce Type: cross Abstract: Recent advances in multimodal deep learning have greatly enhanced the capability of systems for speech analysis and pronunciation assessment. Accurate pronunciation detection remains a key challenge in Arabic, particularly in the context of Quranic recitation, where subtle phonetic differences can alter meaning. Addressing this challenge, the present study proposes a […]
Can AI Perceive Physical Danger and Intervene?
arXiv:2509.21651v2 Announce Type: replace Abstract: When AI interacts with the physical world — as a robot or an assistive agent — new safety challenges emerge beyond those of purely “digital AI”. In such interactions, the potential for physical harm is direct and immediate. How well do state-of-the-art foundation models understand common-sense facts about physical safety, […]