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Supreme Court updates recusals process
The Supreme Court on Tuesday revealed that it has put new software in place to “assist in identifying potential conflicts” of interest for the justices. In a press release issued […]The postSupreme Court updates recusals processappeared first onSCOTUSblog.
The anticipated criminal law decisions and arguments for the rest of this term
ScotusCrim is a recurring series by Rory Little focusing on intersections between the Supreme Court and criminal law. Today’s column is my busman’s holiday project: providing nerd-like numbers and information […]The postThe anticipated criminal law decisions and arguments for the...
Supreme Court to hear arguments on confiscations by Cuban government
It has been more than 65 years since Cuba’s communist government came to power and confiscated large swaths of assets owned by U.S. businesses in Cuba. On Monday, the Supreme […]The postSupreme Court to hear arguments on confiscations by Cuban...
SCOTUStoday for Wednesday, February 18
Justice Anthony Kennedy joined the court on this day in 1988. He served for slightly more than 30 years, retiring on July 31, 2018. SCOTUS Quick Hits Morning Reads A […]The postSCOTUStoday for Wednesday, February 18appeared first onSCOTUSblog.
Is your startup’s check engine light on? Google Cloud’s VP explains what to do
Startup founders are being pushed to move faster than ever, using AI while facing tighter funding, rising infrastructure costs, and more pressure to show real traction early. Cloud credits, access to GPUs, and foundation models have made it easier to...
Google Cloud’s VP for startups on reading your ‘check engine light’ before it’s too late
Startup founders are being pushed to move faster than ever, using AI while facing tighter funding, rising infrastructure costs, and more pressure to show real traction early. Cloud credits, access to GPUs, and foundation models have made it easier to...
OpenAI pushes into higher education as India seeks to scale AI skills
OpenAI says its India education partnerships aim to reach more than 100,000 students, faculty, and staff over the next year.
Open Rubric System: Scaling Reinforcement Learning with Pairwise Adaptive Rubric
arXiv:2602.14069v1 Announce Type: new Abstract: Scalar reward models compress multi-dimensional human preferences into a single opaque score, creating an information bottleneck that often leads to brittleness and reward hacking in open-ended alignment. We argue that robust alignment for non-verifiable tasks...
Annotation-Efficient Vision-Language Model Adaptation to the Polish Language Using the LLaVA Framework
arXiv:2602.14073v1 Announce Type: new Abstract: Most vision-language models (VLMs) are trained on English-centric data, limiting their performance in other languages and cultural contexts. This restricts their usability for non-English-speaking users and hinders the development of multimodal systems that reflect diverse...
GTS: Inference-Time Scaling of Latent Reasoning with a Learnable Gaussian Thought Sampler
arXiv:2602.14077v1 Announce Type: new Abstract: Inference-time scaling (ITS) in latent reasoning models typically introduces stochasticity through heuristic perturbations, such as dropout or fixed Gaussian noise. While these methods increase trajectory diversity, their exploration behavior is not explicitly modeled and can...
Empty Shelves or Lost Keys? Recall Is the Bottleneck for Parametric Factuality
arXiv:2602.14080v1 Announce Type: new Abstract: Standard factuality evaluations of LLMs treat all errors alike, obscuring whether failures arise from missing knowledge (empty shelves) or from limited access to encoded facts (lost keys). We propose a behavioral framework that profiles factual...
Character-aware Transformers Learn an Irregular Morphological Pattern Yet None Generalize Like Humans
arXiv:2602.14100v1 Announce Type: new Abstract: Whether neural networks can serve as cognitive models of morphological learning remains an open question. Recent work has shown that encoder-decoder models can acquire irregular patterns, but evidence that they generalize these patterns like humans...
Index Light, Reason Deep: Deferred Visual Ingestion for Visual-Dense Document Question Answering
arXiv:2602.14162v1 Announce Type: new Abstract: Existing multimodal document question answering methods universally adopt a supply-side ingestion strategy: running a Vision-Language Model (VLM) on every page during indexing to generate comprehensive descriptions, then answering questions through text retrieval. However, this "pre-ingestion"...
We can still parse using syntactic rules
arXiv:2602.14238v1 Announce Type: new Abstract: This research introduces a new parsing approach, based on earlier syntactic work on context free grammar (CFG) and generalized phrase structure grammar (GPSG). The approach comprises both a new parsing algorithm and a set of...
STATe-of-Thoughts: Structured Action Templates for Tree-of-Thoughts
arXiv:2602.14265v1 Announce Type: new Abstract: Inference-Time-Compute (ITC) methods like Best-of-N and Tree-of-Thoughts are meant to produce output candidates that are both high-quality and diverse, but their use of high-temperature sampling often fails to achieve meaningful output diversity. Moreover, existing ITC...
BLUEPRINT Rebuilding a Legacy: Multimodal Retrieval for Complex Engineering Drawings and Documents
arXiv:2602.13345v1 Announce Type: new Abstract: Decades of engineering drawings and technical records remain locked in legacy archives with inconsistent or missing metadata, making retrieval difficult and often manual. We present Blueprint, a layout-aware multimodal retrieval system designed for large-scale engineering...
Exploring the Performance of ML/DL Architectures on the MNIST-1D Dataset
arXiv:2602.13348v1 Announce Type: new Abstract: Small datasets like MNIST have historically been instrumental in advancing machine learning research by providing a controlled environment for rapid experimentation and model evaluation. However, their simplicity often limits their utility for distinguishing between advanced...
The Speed-up Factor: A Quantitative Multi-Iteration Active Learning Performance Metric
arXiv:2602.13359v1 Announce Type: new Abstract: Machine learning models excel with abundant annotated data, but annotation is often costly and time-intensive. Active learning (AL) aims to improve the performance-to-annotation ratio by using query methods (QMs) to iteratively select the most informative...
Accelerated Discovery of Cryoprotectant Cocktails via Multi-Objective Bayesian Optimization
arXiv:2602.13398v1 Announce Type: new Abstract: Designing cryoprotectant agent (CPA) cocktails for vitrification is challenging because formulations must be concentrated enough to suppress ice formation yet non-toxic enough to preserve cell viability. This tradeoff creates a large, multi-objective design space in...
High-Resolution Climate Projections Using Diffusion-Based Downscaling of a Lightweight Climate Emulator
arXiv:2602.13416v1 Announce Type: new Abstract: The proliferation of data-driven models in weather and climate sciences has marked a significant paradigm shift, with advanced models demonstrating exceptional skill in medium-range forecasting. However, these models are often limited by long-term instabilities, climatological...
Federated Learning of Nonlinear Temporal Dynamics with Graph Attention-based Cross-Client Interpretability
arXiv:2602.13485v1 Announce Type: new Abstract: Networks of modern industrial systems are increasingly monitored by distributed sensors, where each system comprises multiple subsystems generating high dimensional time series data. These subsystems are often interdependent, making it important to understand how temporal...
Preventing Rank Collapse in Federated Low-Rank Adaptation with Client Heterogeneity
arXiv:2602.13486v1 Announce Type: new Abstract: Federated low-rank adaptation (FedLoRA) has facilitated communication-efficient and privacy-preserving fine-tuning of foundation models for downstream tasks. In practical federated learning scenarios, client heterogeneity in system resources and data distributions motivates heterogeneous LoRA ranks across clients....
$\gamma$-weakly $\theta$-up-concavity: Linearizable Non-Convex Optimization with Applications to DR-Submodular and OSS Functions
arXiv:2602.13506v1 Announce Type: new Abstract: Optimizing monotone non-convex functions is a fundamental challenge across machine learning and combinatorial optimization. We introduce and study $\gamma$-weakly $\theta$-up-concavity, a novel first-order condition that characterizes a broad class of such functions. This condition provides...