Google introduces a new video generation model as OpenAI shutters Sora app - Android Authority
<a href="https://news.google.com/rss/articles/CBMibkFVX3lxTE13dV9TSjJRUG5Nb1NqektpUWlQcTEzTTBqVDFPaEZrNDRFa2tTcVR5c2hKU0lBMHVEcFM1QkFvX3JjNnVMMjdTTE9YOUkyYTkyelE2THpMUmdJa3JlNy1YeE9LQ1N5TmlibjdaanJ3?oc=5" target="_blank">Google introduces a new video generation model as OpenAI shutters Sora app</a> <font color="#6f6f6f">Android Authority</font>
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ParetoBandit: Budget-Paced Adaptive Routing for Non-Stationary LLM Serving
arXiv:2604.00136v1 Announce Type: new Abstract: Production LLM serving often relies on multi-model portfolios spanning a ~530x cost range, where routing decisions trade off quality against cost. This trade-off is non-stationary: providers revise pricing, model quality can regress silently, and new models must be integrated without downtime. We present ParetoBandit, an open-source adaptive router built on cost-aware contextual bandits that is the first to simultaneously enforce dollar-denominated budgets, adapt online to such shifts, and onboard new models at runtime. ParetoBandit closes these gaps through three mechanisms. An online primal-dual budget pacer enforces a per-request cost ceiling over an open-ended stream, replacing offline penalty tuning with closed-loop control. Geometric fo
Common TF-IDF variants arise as key components in the test statistic of a penalized likelihood-ratio test for word burstiness
arXiv:2604.00672v1 Announce Type: cross Abstract: TF-IDF is a classical formula that is widely used for identifying important terms within documents. We show that TF-IDF-like scores arise naturally from the test statistic of a penalized likelihood-ratio test setup capturing word burstiness (also known as word over-dispersion). In our framework, the alternative hypothesis captures word burstiness by modeling a collection of documents according to a family of beta-binomial distributions with a gamma penalty term on the precision parameter. In contrast, the null hypothesis assumes that words are binomially distributed in collection documents, a modeling approach that fails to account for word burstiness. We find that a term-weighting scheme given rise to by this test statistic performs compar
Excite, Attend and Segment (EASe): Domain-Agnostic Fine-Grained Mask Discovery with Feature Calibration and Self-Supervised Upsampling
arXiv:2604.00276v1 Announce Type: new Abstract: Unsupervised segmentation approaches have increasingly leveraged foundation models (FM) to improve salient object discovery. However, these methods often falter in scenes with complex, multi-component morphologies, where fine-grained structural detail is indispensable. Many state-of-the-art unsupervised segmentation pipelines rely on mask discovery approaches that utilize coarse, patch-level representations. These coarse representations inherently suppress the fine-grained detail required to resolve such complex morphologies. To overcome this limitation, we propose Excite, Attend and Segment (EASe), an unsupervised domain-agnostic semantic segmentation framework for easy fine-grained mask discovery across challenging real-world scenes. EASe u
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Mistral AI raises 1.7B€ to accelerate technological progress with AI - Mistral AI
<a href="https://news.google.com/rss/articles/CBMilwFBVV95cUxOX0MyVnhVZWVXekVfQkZpSVN6MGJBbVRvTW1TTmZHdnBSZWUtRXBvQi1uVk5QVmRxaVJRSDZJdlo2dURXQXZORWRaUzdmcVc1STlNTzFjOVA5d2c3aWFOQ2VTRTFWZU1GMW9xLW5EelppQXpaZnN2TDZ3bXZodDU4UXBIZWZmS25IUE9ZX0M2YzZ0X0wxZUQ4?oc=5" target="_blank">Mistral AI raises 1.7B€ to accelerate technological progress with AI</a> <font color="#6f6f6f">Mistral AI</font>
Excite, Attend and Segment (EASe): Domain-Agnostic Fine-Grained Mask Discovery with Feature Calibration and Self-Supervised Upsampling
arXiv:2604.00276v1 Announce Type: new Abstract: Unsupervised segmentation approaches have increasingly leveraged foundation models (FM) to improve salient object discovery. However, these methods often falter in scenes with complex, multi-component morphologies, where fine-grained structural detail is indispensable. Many state-of-the-art unsupervised segmentation pipelines rely on mask discovery approaches that utilize coarse, patch-level representations. These coarse representations inherently suppress the fine-grained detail required to resolve such complex morphologies. To overcome this limitation, we propose Excite, Attend and Segment (EASe), an unsupervised domain-agnostic semantic segmentation framework for easy fine-grained mask discovery across challenging real-world scenes. EASe u
Predicting Wave Reflection and Transmission in Heterogeneous Media via Fourier Operator-Based Transformer Modeling
arXiv:2604.00132v1 Announce Type: new Abstract: We develop a machine learning (ML) surrogate model to approximate solutions to Maxwell's equations in one dimension, focusing on scenarios involving a material interface that reflects and transmits electro-magnetic waves. Derived from high-fidelity Finite Volume (FV) simulations, our training data includes variations of the initial conditions, as well as variations in one material's speed of light, allowing for the model to learn a range of wave-material interaction behaviors. The ML model autoregressively learns both the physical and frequency embeddings in a vision transformer-based framework. By incorporating Fourier transforms in the latent space, the wave number spectra of the solutions aligns closely with the simulation data. Prediction
OmniSch: A Multimodal PCB Schematic Benchmark For Structured Diagram Visual Reasoning
arXiv:2604.00270v1 Announce Type: new Abstract: Recent large multimodal models (LMMs) have made rapid progress in visual grounding, document understanding, and diagram reasoning tasks. However, their ability to convert Printed Circuit Board (PCB) schematic diagrams into machine-readable spatially weighted netlist graphs, jointly capturing component attributes, connectivity, and geometry, remains largely underexplored, despite such graph representations are the backbone of practical electronic design automation (EDA) workflows. To bridge this gap, we introduce OmniSch, the first comprehensive benchmark designed to assess LMMs on schematic understanding and spatial netlist graph construction. OmniSch contains 1,854 real-world schematic diagrams and includes four tasks: (1) visual grounding f
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