Forecasting Anomaly Precursors via Uncertainty-Aware Time-Series Ensembles
arXiv:2602.17028v1 Announce Type: new Abstract: Detecting anomalies in time-series data is critical in domains such as industrial operations, finance, and cybersecurity, where early identification of abnormal patterns is essential for ensuring system reliability and enabling preventive maintenance. However, most existing...
FCC asks stations for "pro-America" programming, like daily Pledge of Allegiance
Brendan Carr wants "patriotic" shows for Trump's yearlong America 250 celebration.
The creator economy’s ad revenue problem and India’s AI ambitions
The creator economy is evolving fast, and ad revenue alone isn’t cutting it anymore. YouTubers are launching product lines, acquiring startups, and building actual business empires. In fact, MrBeast’s company bought fintech startup Step, and his chocolate business is outearning...
Anthropic-funded group backs candidate attacked by rival AI super PAC
Dueling pro-AI PACs have centered around backing or targeting one New York congressional bid: Alex Bores, whose RAISE Act requires AI developers to disclose safety protocols and report serious system misuse.
Peak XV raises $1.3B, doubles down on AI as global VC rivalry in India heats up
Peak XV says most of its new capital will target India as the firm prioritizes AI, fintech and cross-border bets while navigating recent partner departures.
UAE’s G42 teams up with Cerebras to deploy 8 exaflops of compute in India
Abu Dhabi-based tech company G42 has partnered with U.S.-based chipmaker Cerebras to deploy 8 exaflops of compute through a new system in India.
Reranker Optimization via Geodesic Distances on k-NN Manifolds
arXiv:2602.15860v1 Announce Type: new Abstract: Current neural reranking approaches for retrieval-augmented generation (RAG) rely on cross-encoders or large language models (LLMs), requiring substantial computational resources and exhibiting latencies of 3-5 seconds per query. We propose Maniscope, a geometric reranking method...
VDLM: Variable Diffusion LMs via Robust Latent-to-Text Rendering
arXiv:2602.15870v1 Announce Type: new Abstract: Autoregressive language models decode left-to-right with irreversible commitments, limiting revision during multi-step reasoning. We propose \textbf{VDLM}, a modular variable diffusion language model that separates semantic planning from text rendering. VDLM applies LLaDA-style masked diffusion over...
The Validity of Coreference-based Evaluations of Natural Language Understanding
arXiv:2602.16200v1 Announce Type: new Abstract: In this thesis, I refine our understanding as to what conclusions we can reach from coreference-based evaluations by expanding existing evaluation practices and considering the extent to which evaluation results are either converging or conflicting....
Fast KV Compaction via Attention Matching
arXiv:2602.16284v1 Announce Type: new Abstract: Scaling language models to long contexts is often bottlenecked by the size of the key-value (KV) cache. In deployed settings, long contexts are typically managed through compaction in token space via summarization. However, summarization can...
Can courts excuse late removals to federal court?
As many law students learn in their civil procedure course, when a plaintiff files suit in state court asserting a claim over which a federal district court would have jurisdiction, […]The postCan courts excuse late removals to federal court?appeared first...
Reddit is testing a new AI search feature for shopping
A small group of users in the U.S. will start to see search results that include interactive product carousels with pricing, images, and direct where-to-buy links.
ER-MIA: Black-Box Adversarial Memory Injection Attacks on Long-Term Memory-Augmented Large Language Models
arXiv:2602.15344v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly augmented with long-term memory systems to overcome finite context windows and enable persistent reasoning across interactions. However, recent research finds that LLMs become more vulnerable because memory provides extra...
CEPAE: Conditional Entropy-Penalized Autoencoders for Time Series Counterfactuals
arXiv:2602.15546v1 Announce Type: new Abstract: The ability to accurately perform counterfactual inference on time series is crucial for decision-making in fields like finance, healthcare, and marketing, as it allows us to understand the impact of events or treatments on outcomes...
Law of informational cosmic spacetime: E = i mc2
Combining mass and energy with information in the inseparable quantum equivalence of E=i mc2 has opened a new informational medicine to help patients maintain truth, good health and beauty (Klimek R., Threefold Material-InformationalEnergetic Reality:E=i mc2 ,Biocosmology–neo-Aristotelism, Vol. 4(4), 2014: 408-409;...
Statement Regarding API Security Incident | OpenReview
CVPR 2026 Compute Reporting Form - Author Guidelines
Verizon acknowledges "pain" of new unlock policy, suggests change is coming
Report: Verizon's goal is "immediate unlock for all payment methods really soon."
Microsoft says Office bug exposed customers’ confidential emails to Copilot AI
Microsoft said the bug meant that its Copilot AI chatbot was reading and summarizing paying customers' confidential emails, bypassing data-protection policies.
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...
Why is Normalization Preferred? A Worst-Case Complexity Theory for Stochastically Preconditioned SGD under Heavy-Tailed Noise
arXiv:2602.13413v1 Announce Type: new Abstract: We develop a worst-case complexity theory for stochastically preconditioned stochastic gradient descent (SPSGD) and its accelerated variants under heavy-tailed noise, a setting that encompasses widely used adaptive methods such as Adam, RMSProp, and Shampoo. We...