US decides SpaceX is like an airline, exempting it from Labor Relations Act
US labels SpaceX a common carrier by air, will regulate firm under railway law.
Netflix
With nearly 150 million subscribers around the world, Netflix has a commanding lead in the streaming wars. But it’s also facing heavy competition from deep-pocketed conglomerates like Disney, Apple, and AT&T, and an ongoing wave of narrow, targeted streaming sites...
Wearable
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Policy
Tech is reshaping the world — and not always for the better. Whether it’s the rules for Apple’s App Store or Facebook’s plan for fighting misinformation, tech platform policies can have enormous ripple effects on the rest of society. They’re...
HBO
Originally a private cable network, then a premium cable channel, then a mini-network of specialized and dedicated channels, HBO has evolved into a powerhouse of original content production. Game of Thrones is its most obvious success story. But series like...
Film
Cinema isn’t just about the latest Disney/Pixar project or Star Wars spin-off. Memorable storytelling is happening all over the film industry, from Hollywood’s box-office-busting superhero smashes to small, innovative indie experiments. The Verge’s film section is here to help you...
Creators
YouTube, Instagram, SoundCloud, and other online platforms are changing the way people create and consume media. The Verge’s Creators section covers the people using these platforms, what they’re making, and how those platforms are changing (for better and worse) in...
Airbnb says a third of its customer support is now handled by AI in the US and Canada
Airbnb was poised to introduce an app that doesn't just search for you, but one that "knows you." CEO Brian Chesky said, "It will help guests plan their entire trip, help hosts better run their businesses, and help the company...
Understanding the Regulation of the Use of Artificial Intelligence Under International Law
The development of artificial intelligence (AI) has revolutionized various aspects of human life, from the economic sector to the government system. While it brings significant benefits, AI also poses legal and ethical risks that have not been fully addressed in...
Agentic AI for Commercial Insurance Underwriting with Adversarial Self-Critique
arXiv:2602.13213v1 Announce Type: new Abstract: Commercial insurance underwriting is a labor-intensive process that requires manual review of extensive documentation to assess risk and determine policy pricing. While AI offers substantial efficiency improvements, existing solutions lack comprehensive reasoning capabilities and internal...
When to Think Fast and Slow? AMOR: Entropy-Based Metacognitive Gate for Dynamic SSM-Attention Switching
arXiv:2602.13215v1 Announce Type: new Abstract: Transformers allocate uniform computation to every position, regardless of difficulty. State Space Models (SSMs) offer efficient alternatives but struggle with precise information retrieval over a long horizon. Inspired by dual-process theories of cognition (Kahneman, 2011),...
A Geometric Taxonomy of Hallucinations in LLMs
arXiv:2602.13224v1 Announce Type: new Abstract: The term "hallucination" in large language models conflates distinct phenomena with different geometric signatures in embedding space. We propose a taxonomy identifying three types: unfaithfulness (failure to engage with provided context), confabulation (invention of semantically...
Intelligence as Trajectory-Dominant Pareto Optimization
arXiv:2602.13230v1 Announce Type: new Abstract: Despite recent advances in artificial intelligence, many systems exhibit stagnation in long-horizon adaptability despite continued performance optimization. This work argues that such limitations do not primarily arise from insufficient learning, data, or model capacity, but...
AST-PAC: AST-guided Membership Inference for Code
arXiv:2602.13240v1 Announce Type: new Abstract: Code Large Language Models are frequently trained on massive datasets containing restrictively licensed source code. This creates urgent data governance and copyright challenges. Membership Inference Attacks (MIAs) can serve as an auditing mechanism to detect...
MAPLE: A Sub-Agent Architecture for Memory, Learning, and Personalization in Agentic AI Systems
arXiv:2602.13258v1 Announce Type: new Abstract: Large language model (LLM) agents have emerged as powerful tools for complex tasks, yet their ability to adapt to individual users remains fundamentally limited. We argue this limitation stems from a critical architectural conflation: current...
Human-Centered Explainable AI for Security Enhancement: A Deep Intrusion Detection Framework
arXiv:2602.13271v1 Announce Type: new Abstract: The increasing complexity and frequency of cyber-threats demand intrusion detection systems (IDS) that are not only accurate but also interpretable. This paper presented a novel IDS framework that integrated Explainable Artificial Intelligence (XAI) to enhance...
TemporalBench: A Benchmark for Evaluating LLM-Based Agents on Contextual and Event-Informed Time Series Tasks
arXiv:2602.13272v1 Announce Type: new Abstract: It is unclear whether strong forecasting performance reflects genuine temporal understanding or the ability to reason under contextual and event-driven conditions. We introduce TemporalBench, a multi-domain benchmark designed to evaluate temporal reasoning behavior under progressively...
BEAGLE: Behavior-Enforced Agent for Grounded Learner Emulation
arXiv:2602.13280v1 Announce Type: new Abstract: Simulating student learning behaviors in open-ended problem-solving environments holds potential for education research, from training adaptive tutoring systems to stress-testing pedagogical interventions. However, collecting authentic data is challenging due to privacy concerns and the high...
DiffusionRollout: Uncertainty-Aware Rollout Planning in Long-Horizon PDE Solving
arXiv:2602.13616v1 Announce Type: new Abstract: We propose DiffusionRollout, a novel selective rollout planning strategy for autoregressive diffusion models, aimed at mitigating error accumulation in long-horizon predictions of physical systems governed by partial differential equations (PDEs). Building on the recently validated...
Multimodal Consistency-Guided Reference-Free Data Selection for ASR Accent Adaptation
arXiv:2602.13263v1 Announce Type: new Abstract: Automatic speech recognition (ASR) systems often degrade on accented speech because acoustic-phonetic and prosodic shifts induce a mismatch to training data, making labeled accent adaptation costly. However, common pseudo-label selection heuristics are largely text-centric (e.g.,...
LLM-Powered Automatic Translation and Urgency in Crisis Scenarios
arXiv:2602.13452v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly proposed for crisis preparedness and response, particularly for multilingual communication. However, their suitability for high-stakes crisis contexts remains insufficiently evaluated. This work examines the performance of state-of-the-art LLMs and...
Think Deep, Not Just Long: Measuring LLM Reasoning Effort via Deep-Thinking Tokens
arXiv:2602.13517v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated impressive reasoning capabilities by scaling test-time compute via long Chain-of-Thought (CoT). However, recent findings suggest that raw token counts are unreliable proxies for reasoning quality: increased generation length does...
LLM-Confidence Reranker: A Training-Free Approach for Enhancing Retrieval-Augmented Generation Systems
arXiv:2602.13571v1 Announce Type: new Abstract: Large language models (LLMs) have revolutionized natural language processing, yet hallucinations in knowledge-intensive tasks remain a critical challenge. Retrieval-augmented generation (RAG) addresses this by integrating external knowledge, but its efficacy depends on accurate document retrieval...
Elo-Evolve: A Co-evolutionary Framework for Language Model Alignment
arXiv:2602.13575v1 Announce Type: new Abstract: Current alignment methods for Large Language Models (LLMs) rely on compressing vast amounts of human preference data into static, absolute reward functions, leading to data scarcity, noise sensitivity, and training instability. We introduce Elo-Evolve, a...
Tutoring Large Language Models to be Domain-adaptive, Precise, and Safe
arXiv:2602.13860v1 Announce Type: new Abstract: The overarching research direction of this work is the development of a ''Responsible Intelligence'' framework designed to reconcile the immense generative power of Large Language Models (LLMs) with the stringent requirements of real-world deployment. As...
ADAB: Arabic Dataset for Automated Politeness Benchmarking -- A Large-Scale Resource for Computational Sociopragmatics
arXiv:2602.13870v1 Announce Type: new Abstract: The growing importance of culturally-aware natural language processing systems has led to an increasing demand for resources that capture sociopragmatic phenomena across diverse languages. Nevertheless, Arabic-language resources for politeness detection remain under-explored, despite the rich...
Pre-Editorial Normalization for Automatically Transcribed Medieval Manuscripts in Old French and Latin
arXiv:2602.13905v1 Announce Type: new Abstract: Recent advances in Automatic Text Recognition (ATR) have improved access to historical archives, yet a methodological divide persists between palaeographic transcriptions and normalized digital editions. While ATR models trained on more palaeographically-oriented datasets such as...
The Sufficiency-Conciseness Trade-off in LLM Self-Explanation from an Information Bottleneck Perspective
arXiv:2602.14002v1 Announce Type: new Abstract: Large Language Models increasingly rely on self-explanations, such as chain of thought reasoning, to improve performance on multi step question answering. While these explanations enhance accuracy, they are often verbose and costly to generate, raising...
GRRM: Group Relative Reward Modeling for Machine Translation
arXiv:2602.14028v1 Announce Type: new Abstract: While Group Relative Policy Optimization (GRPO) offers a powerful framework for LLM post-training, its effectiveness in open-ended domains like Machine Translation hinges on accurate intra-group ranking. We identify that standard Scalar Quality Metrics (SQM) fall...
Context Shapes LLMs Retrieval-Augmented Fact-Checking Effectiveness
arXiv:2602.14044v1 Announce Type: new Abstract: Large language models (LLMs) show strong reasoning abilities across diverse tasks, yet their performance on extended contexts remains inconsistent. While prior research has emphasized mid-context degradation in question answering, this study examines the impact of...