arXiv:2511.17198v1 Announce Type: new Abstract: LLM-driven agents, particularly those using general frameworks like ReAct or human-inspired role-playing, often struggle in specialized domains that necessitate rigorously structured workflows. Fields such as remote sensing, requiring specialized tools (e.g., correction, spectral indices calculation), and multi-step procedures (e.g., numerous intermediate products and optional steps), significantly challenge generalized approaches. To […]
SweeperBot: Making 3D Browsing Accessible through View Analysis and Visual Question Answering
arXiv:2511.14567v3 Announce Type: replace-cross Abstract: Accessing 3D models remains challenging for Screen Reader (SR) users. While some existing 3D viewers allow creators to provide alternative text, they often lack sufficient detail about the 3D models. Grounded on a formative study, this paper introduces SweeperBot, a system that enables SR users to leverage visual question answering […]
REMSA: An LLM Agent for Foundation Model Selection in Remote Sensing
arXiv:2511.17442v1 Announce Type: cross Abstract: Foundation Models (FMs) are increasingly used in remote sensing (RS) for tasks such as environmental monitoring, disaster assessment, and land-use mapping. These models include unimodal vision encoders trained on a single data modality and multimodal architectures trained on combinations of SAR, multispectral, hyperspectral, and image-text data. They support diverse RS […]
Agentifying Agentic AI
arXiv:2511.17332v1 Announce Type: new Abstract: Agentic AI seeks to endow systems with sustained autonomy, reasoning, and interaction capabilities. To realize this vision, its assumptions about agency must be complemented by explicit models of cognition, cooperation, and governance. This paper argues that the conceptual tools developed within the Autonomous Agents and Multi-Agent Systems (AAMAS) community, such […]
Observer-Aware Probabilistic Planning Under Partial Observability
arXiv:2502.10568v2 Announce Type: replace Abstract: In this article, we are interested in planning problems where the agent is aware of the presence of an observer, and where this observer is in a partial observability situation. The agent has to choose its strategy so as to optimize the information transmitted by observations. Building on observer-aware Markov […]
A novel approach to classification of ECG arrhythmia types with latent ODEs
arXiv:2511.16933v1 Announce Type: cross Abstract: 12-lead ECGs with high sampling frequency are the clinical gold standard for arrhythmia detection, but their short-term, spot-check nature often misses intermittent events. Wearable ECGs enable long-term monitoring but suffer from irregular, lower sampling frequencies due to battery constraints, making morphology analysis challenging. We present an end-to-end classification pipeline to […]
Integrated Open-Source Framework for Simulation of Transcatheter Pulmonary Valves in Native Right Ventricular Outflow Tracts
arXiv:2507.06337v3 Announce Type: replace Abstract: Background – Pulmonary insufficiency is a consequence of transannular patch repair in Tetralogy of Fallot (ToF), leading to late morbidity and mortality. Transcatheter native outflow tract pulmonary valve replacement (TPVR) has become common, but assessment of patient candidacy and selection of the optimal device remains challenging. We demonstrate an integrated […]
RASTP: Representation-Aware Semantic Token Pruning for Generative Recommendation with Semantic Identifiers
arXiv:2511.16943v1 Announce Type: cross Abstract: Generative recommendation systems typically leverage Semantic Identifiers (SIDs), which represent each item as a sequence of tokens that encode semantic information. However, representing item ID with multiple SIDs significantly increases input sequence length, which is a major determinant of computational complexity and memory consumption. While existing efforts primarily focus on […]
AutoGraphAD: A novel approach using Variational Graph Autoencoders for anomalous network flow detection
arXiv:2511.17113v1 Announce Type: cross Abstract: Network Intrusion Detection Systems (NIDS) are essential tools for detecting network attacks and intrusions. While extensive research has explored the use of supervised Machine Learning for attack detection and characterisation, these methods require accurately labelled datasets, which are very costly to obtain. Moreover, existing public datasets have limited and/or outdated […]
Learning to Compress: Unlocking the Potential of Large Language Models for Text Representation
arXiv:2511.17129v1 Announce Type: cross Abstract: Text representation plays a critical role in tasks like clustering, retrieval, and other downstream applications. With the emergence of large language models (LLMs), there is increasing interest in harnessing their capabilities for this purpose. However, most of the LLMs are inherently causal and optimized for next-token prediction, making them suboptimal […]