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RadAgent: A tool-using AI agent for stepwise interpretation of chest computed tomography
Announce Type: replace Abstract: Vision-language models (VLM) have markedly advanced AI-driven interpretation and reporting of complex medical imaging, such as computed tomography (CT). Yet, existing methods largely relegate clinicians to passive observers of final outputs, offering no interpretable reasoning trace for them to inspect, validate, or refine. To address this, we introduce RadAgent, a tool-using AI agent that generates CT reports through a stepwise and interpretable process.
NetVAD: Foundation-Model Representation Learning for Identifier-Free Unsupervised Intrusion Detection
Announce Type: new Abstract: Detecting zero-day exploits in production networks requires robust Intrusion Detection Systems (IDS). However, current unsupervised models struggle to match the performance of supervised classifiers, which are trained for specific attacks only. To bridge this gap, we leverage the emerging capabilities of Network Foundation Models.
Digging Up Citations: FOSSIL, a Dataset and Workflow for Reference Extraction in Law and the Humanities
arXiv:2606.01109v1 Announce Type: new Abstract: Citation extraction tools are designed for the structured end-of-document bibliographies of the natural sciences, but law and humanities scholarship cites references primarily in footnotes, where bibliographic data is interleaved with commentary and cross-references and varies widely across languages and styles. To address the scarcity of suitable gold-standard resources, we present FOSSIL (Footnote-based Open-access SSH Scientific Instance...
DPAgent-in-the-Middle: Agentic Defense and Repair Against AI-Groomed Deceptive Patterns
arXiv:2606.06914v1 Announce Type: new Abstract: Privacy deceptive patterns in web interfaces systematically manipulate users into disclosing personal data, yet existing defenses are fragmented, static, and increasingly vulnerable to manipulation by large language models. Moreover, data voids, areas of information scarcity within the web ecosystem, create fertile ground for adversaries to inject misleading content that can be scraped and learned by AI systems, thereby amplifying both...
Peacemaker at ATE-IT: Automatic term extraction from Italian text for waste management data using encoder model
arXiv:2606.01469v1 Announce Type: new Abstract: The development of automatic term extraction has become increasingly important in modern technology. Automatic term extraction can be found in virtually every search engine that is currently available to users. Recent advancements have provided promising results for the extraction of automatic terms; however, accurate labeling is difficult because of several factors, such as the limited number of annotated documents available for training and...
MemoryDocDataSet: A Benchmark for Joint Conversational Memory and Long Document Reasoning
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Only one of these foods will be served at the MLB All-Star Game in Philly, so let's break down the competitors
Philadelphia is the birthplace of this fine nation we call home, so it's fitting that as part of America's 250th birthday, the MLB All-Star Game is taking place in the City of Brotherly Love. But as much as Philly is known for history, it's equally known for food. Cheesesteaks, TastyKakes, pretzels, roast pork sandwiches and anything from Wawa.
Spatial-Temporal Decoupled Adapter for Micro-gesture Online Recognition
Announce Type: new Abstract: Micro-gesture online recognition aims to temporally localize and classify subtle gestures in untrimmed videos. Owing to their extremely short duration, low motion amplitude, and ambiguous visual cues, capturing discriminative spatiotemporal representations remains highly challenging. Existing parameter-efficient adapters typically employ a single branch to model spatial and temporal cues jointly, which may fail to capture the fine-grained patterns of micro-gestures.
Horse Eye Blink Detection and Classification for Equine Affective State Assessment
Announce Type: new Abstract: Automated detection of equine facial action units (AUs) is a promising yet under-explored avenue for pain and affective state assessment in horses. Half and full-blink movements are recognised indicators of pain and stress, but as micro-expressions, their subtle, fine-grained nature makes them easily missed by the naked eye and only discernible through frame-by-frame video inspection, making reliable automated detection from video a particularly demanding task....
ArrythML: An Autoencoder-Based TinyML Approach for On-Device Arrhythmia Detection on Resource-Constrained Embedded Systems
arXiv:2606.02256v1 Announce Type: new Abstract: Our work presents a method for ECG segmentation and arrhythmia detection using Tiny Machine Learning (TinyML) models for real-time, on-device inference on resource-constrained embedded systems. We develop INT8 quantized autoencoder-based TinyML models with minimal layers and parameters for embedded deployment.