arXiv:2510.10971v2 Announce Type: replace-cross Abstract: Hate speech remains prevalent in human society and continues to evolve in its forms and expressions. Modern advancements in internet and online anonymity accelerate its rapid spread and complicate its detection. However, hate speech datasets exhibit diverse characteristics primarily because they are constructed from different sources and platforms, each reflecting […]
Rectified Schr”odinger Bridge Matching for Few-Step Visual Navigation
arXiv:2604.05673v2 Announce Type: replace-cross Abstract: Visual navigation is a core challenge in Embodied AI, requiring autonomous agents to translate high-dimensional sensory observations into continuous, long-horizon action trajectories. While generative policies based on diffusion models and Schr”odinger Bridges (SB) effectively capture multimodal action distributions, they require dozens of integration steps due to high-variance stochastic transport, posing […]
UbiQVision: Quantifying Uncertainty in XAI for Image Recognition
arXiv:2512.20288v2 Announce Type: replace-cross Abstract: Recent advances in deep learning have led to its widespread adoption across diverse domains, including medical imaging. This progress is driven by increasingly sophisticated model architectures, such as ResNets, Vision Transformers, and Hybrid Convolutional Neural Networks, that offer enhanced performance at the cost of greater complexity. This complexity often compromises […]
Beyond Single Plots: A Benchmark for Question Answering on Multi-Charts
arXiv:2604.21344v1 Announce Type: cross Abstract: Charts are widely used to present complex information. Deriving meaningful insights in real-world contexts often requires interpreting multiple related charts together. Research on understanding multi-chart images has not been extensively explored. We introduce PolyChartQA, a mid-scale dataset specifically designed for question answering over multi-chart images. PolyChartQA comprises 534 multi-chart images […]
Generalization Properties of Score-matching Diffusion Models for Intrinsically Low-dimensional Data
arXiv:2603.03700v2 Announce Type: replace-cross Abstract: Despite the remarkable empirical success of score-based diffusion models, their statistical guarantees remain underdeveloped. Existing analyses often provide pessimistic convergence rates that do not reflect the intrinsic low-dimensional structure common in real data, such as that arising in natural images. In this work, we study the statistical convergence of score-based […]
VLA-Forget: Vision-Language-Action Unlearning for Embodied Foundation Models
arXiv:2604.03956v2 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models are emerging as embodied foundation models for robotic manipulation, but their deployment introduces a new unlearning challenge: removing unsafe, spurious, or privacy-sensitive behaviors without degrading perception, language grounding, and action control. In OpenVLA-style policies, behavior is produced through a fused visual encoder, a cross-modal projector, and a […]
Stream2LLM: Overlap Context Streaming and Prefill for Reduced Time-to-First-Token (TTFT)
arXiv:2604.16395v2 Announce Type: replace-cross Abstract: Context retrieval systems for LLM inference face a critical challenge: high retrieval latency creates a fundamental tension between waiting for complete context (poor time-to-first-token) and proceeding without it (reduced quality). Streaming context incrementally–overlapping retrieval with inference–can mitigate this latency, but doing so with concurrent requests introduces new challenges: requests contend […]
Stabilising Generative Models of Attitude Change
arXiv:2604.19791v2 Announce Type: replace Abstract: Attitude change – the process by which individuals revise their evaluative stances – has been explained by a set of influential but competing verbal theories. These accounts often function as mechanism sketches: rich in conceptual detail, yet lacking the technical specifications and operational constraints required to run as executable systems. […]
Exploiting LLM-as-a-Judge Disposition on Free Text Legal QA via Prompt Optimization
arXiv:2604.20726v2 Announce Type: replace-cross Abstract: This work explores the role of prompt design and judge selection in LLM-as-a-Judge evaluations of free text legal question answering. We examine whether automatic task prompt optimization improves over human-centered design, whether optimization effectiveness varies by judge feedback style, and whether optimized prompts transfer across judges. We systematically address these […]
A Scale-Adaptive Framework for Joint Spatiotemporal Super-Resolution with Diffusion Models
arXiv:2604.21903v1 Announce Type: cross Abstract: Deep-learning video super-resolution has progressed rapidly, but climate applications typically super-resolve (increase resolution) either space or time, and joint spatiotemporal models are often designed for a single pair of super-resolution (SR) factors (upscaling spatial and temporal ratio between the low-resolution sequence and the high-resolution sequence), limiting transfer across spatial resolutions […]
Hybrid Deep Learning Approach for Coupled Demand Forecasting and Supply Chain Optimization
arXiv:2604.21567v1 Announce Type: cross Abstract: Supply chain resilience and efficiency are vital in industries characterized by volatile demand and uncertain supply, such as textiles and personal protective equipment (PPE). Traditional forecasting and optimization approaches often operate in isolation, limiting their real-world effectiveness. This paper proposes a Hybrid AI Framework for Demand-Supply Forecasting and Optimization (HAF-DS), […]
The Specification Trap: Why Static Value Alignment Alone Is Insufficient for Robust Alignment
arXiv:2512.03048v5 Announce Type: replace Abstract: Static content-based AI value alignment is insufficient for robust alignment under capability scaling, distributional shift, and increasing autonomy. This holds for any approach that treats alignment as optimizing toward a fixed formal value-object, whether reward function, utility function, constitutional principles, or learned preference representation. Three philosophical results create compounding difficulties: […]