Learning Rewards, Not Labels: Adversarial Inverse Reinforcement Learning for Machinery Fault Detection
arXiv:2602.22297v1 Announce Type: new Abstract: Reinforcement learning (RL) offers significant promise for machinery fault detection (MFD). However, most existing RL-based MFD approaches do not fully exploit RL's sequential decision-making strengths, often treating MFD as a simple guessing game (Contextual Bandits)....
AviaSafe: A Physics-Informed Data-Driven Model for Aviation Safety-Critical Cloud Forecasts
arXiv:2602.22298v1 Announce Type: new Abstract: Current AI weather forecasting models predict conventional atmospheric variables but cannot distinguish between cloud microphysical species critical for aviation safety. We introduce AviaSafe, a hierarchical, physics-informed neural forecaster that produces global, six-hourly predictions of these...
Training Agents to Self-Report Misbehavior
arXiv:2602.22303v1 Announce Type: new Abstract: Frontier AI agents may pursue hidden goals while concealing their pursuit from oversight. Alignment training aims to prevent such behavior by reinforcing the correct goals, but alignment may not always succeed and can lead to...
A 1/R Law for Kurtosis Contrast in Balanced Mixtures
arXiv:2602.22334v1 Announce Type: new Abstract: Kurtosis-based Independent Component Analysis (ICA) weakens in wide, balanced mixtures. We prove a sharp redundancy law: for a standardized projection with effective width $R_{\mathrm{eff}}$ (participation ratio), the population excess kurtosis obeys $|\kappa(y)|=O(\kappa_{\max}/R_{\mathrm{eff}})$, yielding the order-tight...
Learning geometry-dependent lead-field operators for forward ECG modeling
arXiv:2602.22367v1 Announce Type: new Abstract: Modern forward electrocardiogram (ECG) computational models rely on an accurate representation of the torso domain. The lead-field method enables fast ECG simulations while preserving full geometric fidelity. Achieving high anatomical accuracy in torso representation is,...
A Learning-Based Hybrid Decision Framework for Matching Systems with User Departure Detection
arXiv:2602.22412v1 Announce Type: new Abstract: In matching markets such as kidney exchanges and freight exchanges, delayed matching has been shown to improve overall market efficiency. The benefits of delay are highly sensitive to participants' sojourn times and departure behavior, and...
Revisiting Chebyshev Polynomial and Anisotropic RBF Models for Tabular Regression
arXiv:2602.22422v1 Announce Type: new Abstract: Smooth-basis models such as Chebyshev polynomial regressors and radial basis function (RBF) networks are well established in numerical analysis. Their continuously differentiable prediction surfaces suit surrogate optimisation, sensitivity analysis, and other settings where the response...
Calibrated Test-Time Guidance for Bayesian Inference
arXiv:2602.22428v1 Announce Type: new Abstract: Test-time guidance is a widely used mechanism for steering pretrained diffusion models toward outcomes specified by a reward function. Existing approaches, however, focus on maximizing reward rather than sampling from the true Bayesian posterior, leading...
Efficient Continual Learning in Language Models via Thalamically Routed Cortical Columns
arXiv:2602.22479v1 Announce Type: new Abstract: Continual learning is a core requirement for deployed language models, yet standard training and fine-tuning pipelines remain brittle under non-stationary data. Online updates often induce catastrophic forgetting, while methods that improve stability frequently increase latency,...
Sharp Convergence Rates for Masked Diffusion Models
arXiv:2602.22505v1 Announce Type: new Abstract: Discrete diffusion models have achieved strong empirical performance in text and other symbolic domains, with masked (absorbing-rate) variants emerging as competitive alternatives to autoregressive models. Among existing samplers, the Euler method remains the standard choice...
Space Syntax-guided Post-training for Residential Floor Plan Generation
arXiv:2602.22507v1 Announce Type: new Abstract: Pre-trained generative models for residential floor plans are typically optimized to fit large-scale data distributions, which can under-emphasize critical architectural priors such as the configurational dominance and connectivity of domestic public spaces (e.g., living rooms...
Coarse-to-Fine Learning of Dynamic Causal Structures
arXiv:2602.22532v1 Announce Type: new Abstract: Learning the dynamic causal structure of time series is a challenging problem. Most existing approaches rely on distributional or structural invariance to uncover underlying causal dynamics, assuming stationary or partially stationary causality. However, these assumptions...
United States v. Hemani: an animated explainer
SCOTUSblog is thrilled to introduce the first in a series of animated videos, done in partnership with Briefly, on some of the most important upcoming cases of the 2025-26 term. Today’s […]The postUnited States v. Hemani: an animated explainerappeared first...
How strong is New York's "illegal gambling" case against Valve's loot boxes?
Lawyers tell Ars the state has a tough road ahead, even as Valve is uniquely vulnerable.
Who’s really running AI? Inside the billion-dollar battle over regulation with Alex Bores
The Pentagon is playing chicken with Anthropic over who gets to control how the military uses AI while communities across the country are blocking data center construction. As the AI debate has been flattened to “doomers versus boomers,” one state...
AI music generator Suno hits 2M paid subscribers and $300M in annual recurring revenue
Suno lets users create music using natural language prompts, making it possible for people with little experience to generate audio with little effort.
Perplexity’s new Computer is another bet that users need many AI models
Perplexity Computer, in the company’s words, "unifies every current AI capability into a single system."
Last 24 hours to get TechCrunch Disrupt 2026 tickets at the lowest rates of the year
The lowest rates of the year for TechCrunch Disrupt 2026 end after today. Prices go up at 11:59 p.m. PT. Don't miss connecting with 10,000 founders, investors, and operators, and key takeaways from 250+ industry leaders. Register now to save...
OpenAI raises $110B in one of the largest private funding rounds in history
The new funding consists of a $50 billion investment from Amazon as well as $30 billion each from Nvidia and SoftBank, against a $730 billion valuation.
ECHOSAT: Estimating Canopy Height Over Space And Time
arXiv:2602.21421v1 Announce Type: cross Abstract: Forest monitoring is critical for climate change mitigation. However, existing global tree height maps provide only static snapshots and do not capture temporal forest dynamics, which are essential for accurate carbon accounting. We introduce ECHOSAT,...
Disaster Question Answering with LoRA Efficiency and Accurate End Position
arXiv:2602.21212v1 Announce Type: new Abstract: Natural disasters such as earthquakes, torrential rainfall, floods, and volcanic eruptions occur with extremely low frequency and affect limited geographic areas. When individuals face disaster situations, they often experience confusion and lack the domain-specific knowledge...
TRACE: Trajectory-Aware Comprehensive Evaluation for Deep Research Agents
arXiv:2602.21230v1 Announce Type: new Abstract: The evaluation of Deep Research Agents is a critical challenge, as conventional outcome-based metrics fail to capture the nuances of their complex reasoning. Current evaluation faces two primary challenges: 1) a reliance on singular metrics...
ToolMATH: A Math Tool Benchmark for Realistic Long-Horizon Multi-Tool Reasoning
arXiv:2602.21265v1 Announce Type: new Abstract: We introduce \ToolMATH, a math-grounded benchmark that evaluates tool-augmented language models in realistic multi-tool environments where the output depends on calling schema-specified tools and sustaining multi-step execution. It turns math problems into a controlled, correctness-checkable...
Beyond Subtokens: A Rich Character Embedding for Low-resource and Morphologically Complex Languages
arXiv:2602.21377v1 Announce Type: new Abstract: Tokenization and sub-tokenization based models like word2vec, BERT and the GPTs are the state-of-the-art in natural language processing. Typically, these approaches have limitations with respect to their input representation. They fail to fully capture orthographic...
Enhancing Multilingual Embeddings via Multi-Way Parallel Text Alignment
arXiv:2602.21543v1 Announce Type: new Abstract: Multilingual pretraining typically lacks explicit alignment signals, leading to suboptimal cross-lingual alignment in the representation space. In this work, we show that training standard pretrained models for cross-lingual alignment with a multi-way parallel corpus in...
MixSarc: A Bangla-English Code-Mixed Corpus for Implicit Meaning Identification
arXiv:2602.21608v1 Announce Type: new Abstract: Bangla-English code-mixing is widespread across South Asian social media, yet resources for implicit meaning identification in this setting remain scarce. Existing sentiment and sarcasm models largely focus on monolingual English or high-resource languages and struggle...
When More Is Less: A Systematic Analysis of Spatial and Commonsense Information for Visual Spatial Reasoning
arXiv:2602.21619v1 Announce Type: new Abstract: Visual spatial reasoning (VSR) remains challenging for modern vision-language models (VLMs), despite advances in multimodal architectures. A common strategy is to inject additional information at inference time, such as explicit spatial cues, external commonsense knowledge,...
Mitigating Structural Noise in Low-Resource S2TT: An Optimized Cascaded Nepali-English Pipeline with Punctuation Restoration
arXiv:2602.21647v1 Announce Type: new Abstract: This paper presents and evaluates an optimized cascaded Nepali speech-to-English text translation (S2TT) system, focusing on mitigating structural noise introduced by Automatic Speech Recognition (ASR). We first establish highly proficient ASR and NMT components: a...
Sparsity Induction for Accurate Post-Training Pruning of Large Language Models
arXiv:2602.21652v1 Announce Type: new Abstract: Large language models have demonstrated capabilities in text generation, while their increasing parameter scales present challenges in computational and memory efficiency. Post-training sparsity (PTS), which reduces model cost by removing weights from dense networks, is...
Robust Long-Form Bangla Speech Processing: Automatic Speech Recognition and Speaker Diarization
arXiv:2602.21741v1 Announce Type: new Abstract: We describe our end-to-end system for Bengali long-form speech recognition (ASR) and speaker diarization submitted to the DL Sprint 4.0 competition on Kaggle. Bengali presents substantial challenges for both tasks: a large phoneme inventory, significant...