Court allows Steve Bannon to move forward on dismissal of criminal charges against him
The Supreme Court on Monday morning added one new case, involving challenges to veterans’ benefit laws, to its docket for the 2026-27 term. The justices also sent the case of […]The postCourt allows Steve Bannon to move forward on dismissal...
GeoBrowse: A Geolocation Benchmark for Agentic Tool Use with Expert-Annotated Reasoning Traces
arXiv:2604.04017v1 Announce Type: new Abstract: Deep research agents integrate fragmented evidence through multi-step tool use. BrowseComp offers a text-only testbed for such agents, but existing multimodal benchmarks rarely require both weak visual cues composition and BrowseComp-style multi-hop verification. Geolocation is...
Iran threatens ‘Stargate’ AI data centers
Iran said it will target U.S.-linked data centers with new missile strikes, as the war between the U.S. and Iran escalates.
Simple yet Effective: Low-Rank Spatial Attention for Neural Operators
arXiv:2604.03582v1 Announce Type: new Abstract: Neural operators have emerged as data-driven surrogates for solving partial differential equations (PDEs), and their success hinges on efficiently modeling the long-range, global coupling among spatial points induced by the underlying physics. In many PDE...
LPC-SM: Local Predictive Coding and Sparse Memory for Long-Context Language Modeling
arXiv:2604.03263v1 Announce Type: new Abstract: Most current long-context language models still rely on attention to handle both local interaction and long-range state, which leaves relatively little room to test alternative decompositions of sequence modeling. We propose LPC-SM, a hybrid autoregressive...
CountsDiff: A Diffusion Model on the Natural Numbers for Generation and Imputation of Count-Based Data
arXiv:2604.03779v1 Announce Type: new Abstract: Diffusion models have excelled at generative tasks for both continuous and token-based domains, but their application to discrete ordinal data remains underdeveloped. We present CountsDiff, a diffusion framework designed to natively model distributions on the...
Emergent Inference-Time Semantic Contamination via In-Context Priming
arXiv:2604.04043v1 Announce Type: new Abstract: Recent work has shown that fine-tuning large language models (LLMs) on insecure code or culturally loaded numeric codes can induce emergent misalignment, causing models to produce harmful content in unrelated downstream tasks. The authors of...
Text Summarization With Graph Attention Networks
arXiv:2604.03583v1 Announce Type: new Abstract: This study aimed to leverage graph information, particularly Rhetorical Structure Theory (RST) and Co-reference (Coref) graphs, to enhance the performance of our baseline summarization models. Specifically, we experimented with a Graph Attention Network architecture to...
Understanding When Poisson Log-Normal Models Outperform Penalized Poisson Regression for Microbiome Count Data
arXiv:2604.03853v1 Announce Type: new Abstract: Multivariate count models are often justified by their ability to capture latent dependence, but researchers receive little guidance on when this added structure improves on simpler penalized marginal Poisson regression. We study this question using...
Episode 42: Russia, Imperial Continuities and Histories of International Law - EJIL: The Podcast!
Ticket savings of up to $500 this week for TechCrunch Disrupt 2026
Starting today, you have 5 days to save nearly $500 on your ticket to TechCrunch Disrupt 2026. This offer disappears Friday, April 10, at 11:59 p.m. PT. Register here to secure these low rates.
Earth Embeddings Reveal Diverse Urban Signals from Space
arXiv:2604.03456v1 Announce Type: new Abstract: Conventional urban indicators derived from censuses, surveys, and administrative records are often costly, spatially inconsistent, and slow to update. Recent geospatial foundation models enable Earth embeddings, compact satellite image representations transferable across downstream tasks, but...
SKILLFOUNDRY: Building Self-Evolving Agent Skill Libraries from Heterogeneous Scientific Resources
arXiv:2604.03964v1 Announce Type: new Abstract: Modern scientific ecosystems are rich in procedural knowledge across repositories, APIs, scripts, notebooks, documentation, databases, and papers, yet much of this knowledge remains fragmented across heterogeneous artifacts that agents cannot readily operationalize. This gap between...
Supervised Dimensionality Reduction Revisited: Why LDA on Frozen CNN Features Deserves a Second Look
arXiv:2604.03928v1 Announce Type: new Abstract: Effective ride-hailing dispatch requires anticipating demand patterns that vary substantially across time-of-day, day-of-week, season, and special events. We propose a regime-calibrated approach that (i) segments historical trip data into demand regimes, (ii) matches the current...
Reanalyzing L2 Preposition Learning with Bayesian Mixed Effects and a Pretrained Language Model
arXiv:2302.08150v2 Announce Type: cross Abstract: We use both Bayesian and neural models to dissect a data set of Chinese learners' pre- and post-interventional responses to two tests measuring their understanding of English prepositions. The results mostly replicate previous findings from...
Low-Rank Compression of Pretrained Models via Randomized Subspace Iteration
arXiv:2604.02659v1 Announce Type: new Abstract: The massive scale of pretrained models has made efficient compression essential for practical deployment. Low-rank decomposition based on the singular value decomposition (SVD) provides a principled approach for model reduction, but its exact computation is...
Chart-RL: Policy Optimization Reinforcement Learning for Enhanced Visual Reasoning in Chart Question Answering with Vision Language Models
arXiv:2604.03157v1 Announce Type: new Abstract: The recent advancements in Vision Language Models (VLMs) have demonstrated progress toward true intelligence requiring robust reasoning capabilities. Beyond pattern recognition, linguistic reasoning must integrate with visual comprehension, particularly for Chart Question Answering (CQA) tasks...
LiME: Lightweight Mixture of Experts for Efficient Multimodal Multi-task Learning
arXiv:2604.02338v1 Announce Type: new Abstract: MoE-PEFT methods combine Mixture of Experts with parameter-efficient fine-tuning for multi-task adaptation, but require separate adapters per expert causing trainable parameters to scale linearly with expert count and limiting applicability to adapter-based architectures. We propose...
InfoSeeker: A Scalable Hierarchical Parallel Agent Framework for Web Information Seeking
arXiv:2604.02971v1 Announce Type: new Abstract: Recent agentic search systems have made substantial progress by emphasising deep, multi-step reasoning. However, this focus often overlooks the challenges of wide-scale information synthesis, where agents must aggregate large volumes of heterogeneous evidence across many...
Overcoming the "Impracticality" of RAG: Proposing a Real-World Benchmark and Multi-Dimensional Diagnostic Framework
arXiv:2604.02640v1 Announce Type: new Abstract: Performance evaluation of Retrieval-Augmented Generation (RAG) systems within enterprise environments is governed by multi-dimensional and composite factors extending far beyond simple final accuracy checks. These factors include reasoning complexity, retrieval difficulty, the diverse structure of...
CIPHER: Conformer-based Inference of Phonemes from High-density EEG
arXiv:2604.02362v1 Announce Type: cross Abstract: Decoding speech information from scalp EEG remains difficult due to low SNR and spatial blurring. We present CIPHER (Conformer-based Inference of Phonemes from High-density EEG Representations), a dual-pathway model using (i) ERP features and (ii)...
On the Geometric Structure of Layer Updates in Deep Language Models
arXiv:2604.02459v1 Announce Type: new Abstract: We study the geometric structure of layer updates in deep language models. Rather than analyzing what information is encoded in intermediate representations, we ask how representations change from one layer to the next. We show...
Tech companies are trying to neuter Colorado’s landmark right-to-repair law
A state bill is a glimpse of how corporations are limiting people's ability to make their own fixes and upgrades.
Supreme Court issues statement that Justice Alito was hospitalized approximately two weeks ago
Justice Samuel Alito was hospitalized on March 20 “[o]ut of an abundance of caution” and at the recommendation of his security detail, the Supreme Court’s Public Information Officer, Patricia McCabe, […]The postSupreme Court issues statement that Justice Alito was hospitalized...
Anthropic is having a moment in the private markets; SpaceX could spoil the party
Glen Anderson, president of Rainmaker Securities, says the secondary market for private shares has never been more active — with Anthropic the hottest trade around, OpenAI losing ground, and SpaceX's looming IPO poised to reshape the landscape for everyone.
Anthropic is having a month
A human really borks things at Anthropic for the second time this week.
Polish phonology and morphology through the lens of distributional semantics
arXiv:2604.00174v1 Announce Type: new Abstract: This study investigates the relationship between the phonological and morphological structure of Polish words and their meanings using Distributional Semantics. In the present analysis, we ask whether there is a relationship between the form properties...
With its new app store, Ring bets on AI to go beyond home security
Ring's app store will allow the company to target broader use cases beyond security, like elder care or business needs.
DDCL: Deep Dual Competitive Learning: A Differentiable End-to-End Framework for Unsupervised Prototype-Based Representation Learning
arXiv:2604.01740v1 Announce Type: new Abstract: A persistent structural weakness in deep clustering is the disconnect between feature learning and cluster assignment. Most architectures invoke an external clustering step, typically k-means, to produce pseudo-labels that guide training, preventing the backbone from...
Beyond Logit Adjustment: A Residual Decomposition Framework for Long-Tailed Reranking
arXiv:2604.01506v1 Announce Type: new Abstract: Long-tailed classification, where a small number of frequent classes dominate many rare ones, remains challenging because models systematically favor frequent classes at inference time. Existing post-hoc methods such as logit adjustment address this by adding...