arXiv:2603.18677v2 Announce Type: replace-cross Abstract: Artificial intelligence is increasingly embedded in human decision making. In some cases, it enhances human reasoning. In others, it fosters excessive cognitive dependence. This paper introduces a conceptual and mathematical framework to distinguish cognitive amplification, where AI improves hybrid human AI performance while preserving human expertise, from cognitive delegation, where […]
ATLAS: AI-Assisted Threat-to-Assertion Learning for System-on-Chip Security Verification
arXiv:2603.01170v2 Announce Type: replace-cross Abstract: This work presents ATLAS, an LLM-driven framework that bridges standardized threat modeling and property-based formal verification for System-on-Chip (SoC) security. Starting from vulnerability knowledge bases such as Common Weakness Enumeration (CWE), ATLAS identifies SoC-specific assets, maps relevant weaknesses, and generates assertion-based security properties and JasperGold scripts for verification. By combining […]
Fine-Grained Perspectives: Modeling Explanations with Annotator-Specific Rationales
arXiv:2604.21667v1 Announce Type: cross Abstract: Beyond exploring disaggregated labels for modeling perspectives, annotator rationales provide fine-grained signals of individual perspectives. In this work, we propose a framework for jointly modeling annotator-specific label prediction and corresponding explanations, fine-tuned on the annotators’ provided rationales. Using a dataset with disaggregated natural language inference (NLI) annotations and annotator-provided explanations, […]
Causal Stance
arXiv:2604.05004v3 Announce Type: replace-cross Abstract: What is the meaning of physical causal closure? Jaegwon Kim explicitly adopts a conception of causation according to which physical causation is effectively identified with deterministic physical lawfulness, and equates it with physical determinism. While this conception is internally coherent, it differs from currently dominant theories of causation. Physics and […]
Generalizing Numerical Reasoning in Table Data through Operation Sketches and Self-Supervised Learning
arXiv:2604.21495v1 Announce Type: cross Abstract: Numerical reasoning over expert-domain tables often exhibits high in-domain accuracy but limited robustness to domain shift. Models trained with supervised fine-tuning (SFT) on specific datasets tend to rely on header-operation shortcuts rather than structural reasoning. We introduce TaNOS, a continual pre-training framework comprising three components: (i) header anonymization to reduce […]
Basic syntax from speech: Spontaneous concatenation in unsupervised deep neural networks
arXiv:2305.01626v4 Announce Type: replace-cross Abstract: Computational models of syntax are predominantly text-based. Here we propose that the most basic first step in the evolution of syntax can be modeled directly from raw speech in a fully unsupervised way. We focus on one of the most ubiquitous and elementary suboperations of syntax — concatenation. We introduce […]
Retrofit: Continual Learning with Controlled Forgetting for Binary Security Detection and Analysis
arXiv:2511.11439v2 Announce Type: replace-cross Abstract: Binary security has increasingly relied on deep learning to reason about malware behavior and program semantics. However, the performance often degrades as threat landscapes evolve and code representations shift. While continual learning (CL) offers a natural solution through sequential updates, most existing approaches rely on data replay or unconstrained updates, […]
Differentiating Physical and Psychological Stress Using Wearable Physiological Signals and Salivary Cortisol
arXiv:2604.12671v2 Announce Type: replace Abstract: Objective: This study aimed to assess how wearable physiological signals, alone and combined with salivary cortisol, distinguish physical and psychological stress and their recovery states. Methods: Six healthy adults completed three laboratory sessions on separate days: rest, physical stress (high-intensity cycling), or psychological stress (modified Trier Social Stress Test). Heart […]
Federated Learning for Surgical Vision in Appendicitis Classification: Results of the FedSurg EndoVis 2024 Challenge
arXiv:2510.04772v2 Announce Type: replace-cross Abstract: Developing generalizable surgical AI requires multi-institutional data, yet patient privacy constraints preclude direct data sharing, making Federated Learning (FL) a natural candidate solution. The application of FL to complex, spatiotemporal surgical video data remains largely unbenchmarked. We present the FedSurg Challenge, the first international benchmarking initiative dedicated to FL in […]
Seeing Further and Wider: Joint Spatio-Temporal Enlargement for Micro-Video Popularity Prediction
arXiv:2604.20311v2 Announce Type: replace-cross Abstract: Micro-video popularity prediction (MVPP) aims to forecast the future popularity of videos on online media, which is essential for applications such as content recommendation and traffic allocation. In real-world scenarios, it is critical for MVPP approaches to understand both the temporal dynamics of a given video (temporal) and its historical […]
Differentially Private De-identification of Dutch Clinical Notes: A Comparative Evaluation
arXiv:2604.21421v1 Announce Type: cross Abstract: Protecting patient privacy in clinical narratives is essential for enabling secondary use of healthcare data under regulations such as GDPR and HIPAA. While manual de-identification remains the gold standard, it is costly and slow, motivating the need for automated methods that combine privacy guarantees with high utility. Most automated text […]
MOMO: A framework for seamless physical, verbal, and graphical robot skill learning and adaptation
arXiv:2604.20468v2 Announce Type: replace-cross Abstract: Industrial robot applications require increasingly flexible systems that non-expert users can easily adapt for varying tasks and environments. However, different adaptations benefit from different interaction modalities. We present an interactive framework that enables robot skill adaptation through three complementary modalities: kinesthetic touch for precise spatial corrections, natural language for high-level […]