arXiv:2603.06597v2 Announce Type: replace-cross Abstract: This paper proposes a two-time scale neurodynamic duplex approach to solve distributionally robust geometric joint chance-constrained optimization problems. The probability distributions of the row vectors are not known in advance and belong to a certain distributional uncertainty set. In our paper, we study three uncertainty sets for the unknown distributions. […]
Human-AI Ensembles Improve Deepfake Detection in Low-to-Medium Quality Videos
arXiv:2603.14658v1 Announce Type: cross Abstract: Deepfake detection is widely framed as a machine learning problem, yet how humans and AI detectors compare under realistic conditions remains poorly understood. We evaluate 200 human participants and 95 state-of-the-art AI detectors across two datasets: DF40, a standard benchmark, and CharadesDF, a novel dataset of videos of everyday activities. […]
GraphSeek: Next-Generation Graph Analytics with LLMs
arXiv:2602.11052v2 Announce Type: replace-cross Abstract: Graphs are foundational across domains but remain hard to use without deep expertise. LLMs promise accessible natural language (NL) graph analytics, yet they fail to process industry-scale property graphs effectively and efficiently: such datasets are large, highly heterogeneous, structurally complex, and evolve dynamically. To address this, we devise a novel […]
Information-Theoretic Constraints for Continual Vision-Language-Action Alignment
arXiv:2603.13335v1 Announce Type: cross Abstract: When deployed in open-ended robotic environments, Vision–Language–Action (VLA) models need to continually acquire new skills, yet suffer from severe catastrophic forgetting. We observe that this degradation is related to the deterioration of cross-modal information structure, where dependencies among visual observations, language instructions, and actions progressively diffuse during continual adaptation. But […]
Self-Supervised Multi-Stage Domain Unlearning for White-Matter Lesion Segmentation
arXiv:2603.13328v1 Announce Type: cross Abstract: Inter-scanner variability of magnetic resonance imaging has an adverse impact on the diagnostic and prognostic quality of the scans and necessitates the development of models robust to domain shift inflicted by the unseen scanner data. Review of recent advances in domain adaptation showed that efficacy of strategies involving modifications or […]
A comprehensive multimodal dataset and benchmark for ulcerative colitis scoring in endoscopy
arXiv:2603.14559v1 Announce Type: cross Abstract: Ulcerative colitis (UC) is a chronic mucosal inflammatory condition that places patients at increased risk of colorectal cancer. Colonoscopic surveillance remains the gold standard for assessing disease activity, and reporting typically relies on standardised endoscopic scoring metrics. The most widely used is the Mayo Endoscopic Score (MES), with some centres […]
A Coin Flip for Safety: LLM Judges Fail to Reliably Measure Adversarial Robustness
arXiv:2603.06594v2 Announce Type: replace-cross Abstract: Automated enquoteLLM-as-a-Judge frameworks have become the de facto standard for scalable evaluation across natural language processing. For instance, in safety evaluation, these judges are relied upon to evaluate harmfulness in order to benchmark the robustness of safety against adversarial attacks. However, we show that existing validation protocols fail to account […]
HEARTS: Benchmarking LLM Reasoning on Health Time Series
arXiv:2603.06638v2 Announce Type: replace-cross Abstract: The rise of large language models (LLMs) has shifted time series analysis from narrow analytics to general-purpose reasoning. Yet, existing benchmarks cover only a small set of health time series modalities and tasks, failing to reflect the diverse domains and extensive temporal dependencies inherent in real-world physiological modeling. To bridge […]
The Scenic Route to Deception: Dark Patterns and Explainability Pitfalls in Conversational Navigation
arXiv:2603.14586v1 Announce Type: cross Abstract: As pedestrian navigation increasingly experiments with Generative AI, and in particular Large Language Models, the nature of routing risks transforming from a verifiable geometric task into an opaque, persuasive dialogue. While conversational interfaces promise personalisation, they introduce risks of manipulation and misplaced trust. We categorise these risks using a 2×2 […]
DAST: A Dual-Stream Voice Anonymization Attacker with Staged Training
arXiv:2603.12840v2 Announce Type: replace-cross Abstract: Voice anonymization masks vocal traits while preserving linguistic content, which may still leak speaker-specific patterns. To assess and strengthen privacy evaluation, we propose a dual-stream attacker that fuses spectral and self-supervised learning features via parallel encoders with a three-stage training strategy. Stage I establishes foundational speaker-discriminative representations. Stage II leverages […]
PolyGLU: State-Conditional Activation Routing in Transformer Feed-Forward Networks
arXiv:2603.13347v1 Announce Type: cross Abstract: Biological neural systems employ diverse neurotransmitters — glutamate, GABA, dopamine, acetylcholine — to implement distinct signal-processing modalities within shared neural circuits. In contrast, modern transformers apply a single fixed activation function across all feed-forward neurons. We introduce PolyGLU (Polychromatic Gated Linear Unit), a drop-in replacement for SwiGLU that enables each […]
Towards On-Policy SFT: Distribution Discriminant Theory and its Applications in LLM Training
arXiv:2602.12222v2 Announce Type: replace-cross Abstract: Supervised fine-tuning (SFT) is computationally efficient but often yields inferior generalization compared to reinforcement learning (RL). This gap is primarily driven by RL’s use of on-policy data. We propose a framework to bridge this chasm by enabling On-Policy SFT. We first present textbftextitDistribution Discriminant Theory (DDT), which explains and quantifies […]