DLT-Corpus: A Large-Scale Text Collection for the Distributed Ledger Technology Domain
arXiv:2602.22045v1 Announce Type: new Abstract: We introduce DLT-Corpus, the largest domain-specific text collection for Distributed Ledger Technology (DLT) research to date: 2.98 billion tokens from 22.12 million documents spanning scientific literature (37,440 publications), United States Patent and Trademark Office (USPTO)...
Robust AI Evaluation through Maximal Lotteries
arXiv:2602.21297v1 Announce Type: new Abstract: The standard way to evaluate language models on subjective tasks is through pairwise comparisons: an annotator chooses the "better" of two responses to a prompt. Leaderboards aggregate these comparisons into a single Bradley-Terry (BT) ranking,...
HiPPO Zoo: Explicit Memory Mechanisms for Interpretable State Space Models
arXiv:2602.21340v1 Announce Type: new Abstract: Representing the past in a compressed, efficient, and informative manner is a central problem for systems trained on sequential data. The HiPPO framework, originally proposed by Gu & Dao et al., provides a principled approach...
Defensive Generation
arXiv:2602.21390v1 Announce Type: new Abstract: We study the problem of efficiently producing, in an online fashion, generative models of scalar, multiclass, and vector-valued outcomes that cannot be falsified on the basis of the observed data and a pre-specified collection of...
Generative Bayesian Computation as a Scalable Alternative to Gaussian Process Surrogates
arXiv:2602.21408v1 Announce Type: new Abstract: Gaussian process (GP) surrogates are the default tool for emulating expensive computer experiments, but cubic cost, stationarity assumptions, and Gaussian predictive distributions limit their reach. We propose Generative Bayesian Computation (GBC) via Implicit Quantile Networks...
On the Structural Non-Preservation of Epistemic Behaviour under Policy Transformation
arXiv:2602.21424v1 Announce Type: new Abstract: Reinforcement learning (RL) agents under partial observability often condition actions on internally accumulated information such as memory or inferred latent context. We formalise such information-conditioned interaction patterns as behavioural dependency: variation in action selection with...
Provably Safe Generative Sampling with Constricting Barrier Functions
arXiv:2602.21429v1 Announce Type: new Abstract: Flow-based generative models, such as diffusion models and flow matching models, have achieved remarkable success in learning complex data distributions. However, a critical gap remains for their deployment in safety-critical domains: the lack of formal...
Effects of Training Data Quality on Classifier Performance
arXiv:2602.21462v1 Announce Type: new Abstract: We describe extensive numerical experiments assessing and quantifying how classifier performance depends on the quality of the training data, a frequently neglected component of the analysis of classifiers. More specifically, in the scientific context of...
D-Flow SGLD: Source-Space Posterior Sampling for Scientific Inverse Problems with Flow Matching
arXiv:2602.21469v1 Announce Type: new Abstract: Data assimilation and scientific inverse problems require reconstructing high-dimensional physical states from sparse and noisy observations, ideally with uncertainty-aware posterior samples that remain faithful to learned priors and governing physics. While training-free conditional generation is...
Learning Recursive Multi-Scale Representations for Irregular Multivariate Time Series Forecasting
arXiv:2602.21498v1 Announce Type: new Abstract: Irregular Multivariate Time Series (IMTS) are characterized by uneven intervals between consecutive timestamps, which carry sampling pattern information valuable and informative for learning temporal and variable dependencies. In addition, IMTS often exhibit diverse dependencies across...
WaterVIB: Learning Minimal Sufficient Watermark Representations via Variational Information Bottleneck
arXiv:2602.21508v1 Announce Type: new Abstract: Robust watermarking is critical for intellectual property protection, whereas existing methods face a severe vulnerability against regeneration-based AIGC attacks. We identify that existing methods fail because they entangle the watermark with high-frequency cover texture, which...
Muon+: Towards Better Muon via One Additional Normalization Step
arXiv:2602.21545v1 Announce Type: new Abstract: The Muon optimizer has demonstrated promising performance in pre-training large language models through gradient (or momentum) orthogonalization. In this work, we propose a simple yet effective enhancement to Muon, namely Muon+, which introduces an additional...
Mamba Meets Scheduling: Learning to Solve Flexible Job Shop Scheduling with Efficient Sequence Modeling
arXiv:2602.21546v1 Announce Type: new Abstract: The Flexible Job Shop Problem (FJSP) is a well-studied combinatorial optimization problem with extensive applications for manufacturing and production scheduling. It involves assigning jobs to various machines to optimize criteria, such as minimizing total completion...
Extending Sequence Length is Not All You Need: Effective Integration of Multimodal Signals for Gene Expression Prediction
arXiv:2602.21550v1 Announce Type: new Abstract: Gene expression prediction, which predicts mRNA expression levels from DNA sequences, presents significant challenges. Previous works often focus on extending input sequence length to locate distal enhancers, which may influence target genes from hundreds of...
From Basis to Basis: Gaussian Particle Representation for Interpretable PDE Operators
arXiv:2602.21551v1 Announce Type: new Abstract: Learning PDE dynamics for fluids increasingly relies on neural operators and Transformer-based models, yet these approaches often lack interpretability and struggle with localized, high-frequency structures while incurring quadratic cost in spatial samples. We propose representing...
Training-free Composition of Pre-trained GFlowNets for Multi-Objective Generation
arXiv:2602.21565v1 Announce Type: new Abstract: Generative Flow Networks (GFlowNets) learn to sample diverse candidates in proportion to a reward function, making them well-suited for scientific discovery, where exploring multiple promising solutions is crucial. Further extending GFlowNets to multi-objective settings has...
NGDB-Zoo: Towards Efficient and Scalable Neural Graph Databases Training
arXiv:2602.21597v1 Announce Type: new Abstract: Neural Graph Databases (NGDBs) facilitate complex logical reasoning over incomplete knowledge structures, yet their training efficiency and expressivity are constrained by rigid query-level batching and structure-exclusive embeddings. We present NGDB-Zoo, a unified framework that resolves...
Deep Clustering based Boundary-Decoder Net for Inter and Intra Layer Stress Prediction of Heterogeneous Integrated IC Chip
arXiv:2602.21601v1 Announce Type: new Abstract: High stress occurs when 3D heterogeneous IC packages are subjected to thermal cycling at extreme temperatures. Stress mainly occurs at the interface between different materials. We investigate stress image using latent space representation which is...
Beach blasts and unusually dangerous weapons
The Relist Watch column examines cert petitions that the Supreme Court has “relisted” for its upcoming conference. A short explanation of relists is available here. With the rest of the current […]The postBeach blasts and unusually dangerous weaponsappeared first onSCOTUSblog.
Court to hear argument on whether and when drug users may possess firearms
The Supreme Court will hear oral arguments on Monday in United States v. Hemani, the second gun-rights case of the 2025-26 term. In January, the Trump administration supported Hawaii gun […]The postCourt to hear argument on whether and when drug...
AI’s Future May Be Quantum
Stephanie Seoyun Hwang, J.D. Class of 2028 While most people recognize AI as a transformative force, fewer are aware of one of the key technologies fueling its progress: quantum computing. In fact, many governments and tech industry actors see it...
Third Time’s the Charm? The Fate of the EU–U.S. Data Privacy Framework
Ksheeraja Satish, LL.M. Class of 2026 Transatlantic transfers of personal data are fundamental to the global digital economy. However, the legal history of these transfer mechanisms has been one of successive invalidations. Each time the European Union (EU) and the...
xAI spent $7M building wall that barely muffles annoying power plant noise
“Temu sound wall” not enough to quell fury over xAI’s power plant.
Jack Dorsey just halved the size of Block’s employee base — and he says your company is next
Jack Dorsey has long been an open admirer of Elon Musk. Now, it seems, he may have been taking notes.
Anthropic CEO stands firm as Pentagon deadline looms
Anthropic CEO Dario Amodei said Thursday that he "cannot in good conscience accede" to the Pentagon's demands to give the military unrestricted access to its AI systems.
So, we’re getting Prada Meta AI glasses, right?
Mark Zuckerberg was at Prada's fashion week event in Milan, leaving everyone to wonder if we're getting Meta AI glasses under the Prada brand.
Sophia Space raises $10M seed to demo novel space computers
The company's modular computer tiles offer a new vision for space data centers.
Mistral AI inks a deal with global consulting giant Accenture
Mistral AI lands a partnership with Accenture, the consultant that has also recently announced partnerships with rivals OpenAI and Anthropic.
Read AI launches an email-based ‘digital twin’ to help you with schedules and answers
Read AI is launching Ada, which can reply with your availability and extract answers from the company knowledge base and the web.
Bumble adds AI-powered photo feedback and profile guidance tools
Bumble and other popular dating apps, like Match Group's Tinder and Hinge, have all embraced AI-powered features.