Anthropic’s Claude popularity with paying consumers is skyrocketing
Estimates for total Claude consumer users are all over the map (we've seen figures ranging from 18 million to 30 million). Anthropic hasn't disclosed this data, but a spokesperson did tell TechCrunch that Claude paid subscriptions have more than doubled...
Why SoftBank’s new $40B loan points to a 2026 OpenAI IPO
Wall Street giants JPMorgan and Goldman Sachs are extending a 12-month, unsecured loan to the Japanese conglomerate.
Memory chip giant SK hynix could help end ‘RAMmageddon’ with blockbuster US IPO
SK hynix’s potential U.S. listing could raise $10-$14 billion to help it build more capacity, encourage others to follow, and end the 'RAMmageddon' memory shortage.
VCs are betting billions on AI’s next wave, so why is OpenAI killing Sora?
When an 82-year-old Kentucky woman was offered $26 million from an AI company that wanted to build a data center on her land, she said no. Sure, that same company can try to rezone 2,000 acres nearby anyway, but as...
OpenAI shuts down Sora while Meta gets shut out in court
When an 82-year-old Kentucky woman was offered $26 million from an AI company that wanted to build a data center on her land, she said no. Sure, that same company can try to rezone 2,000 acres nearby anyway, but as...
David Sacks is done as AI czar — here’s what he’s doing instead
Sacks will be much further from the power center in Washington than since the outset of this second Trump administration.
Anthropic wins injunction against Trump administration over Defense Department saga
A federal judge has ordered that the Trump administration rescind recent restrictions it placed on the AI company.
Wikipedia cracks down on the use of AI in article writing
The site, whose policies are subject to change, has struggled with the issue of AI-generated writing.
Data centers get ready — the Senate wants to see your power bills
Senators Josh Hawley and Elizabeth Warren want the Energy Information Administration to gather more details about how data centers use power — and how that affects the grid.
ByteDance’s new AI video generation model, Dreamina Seedance 2.0, comes to CapCut
The new model in CapCut will have built-in protections for making video from real faces or unauthorized intellectual property.
Conntour raises $7M from General Catalyst, YC to build an AI search engine for security video systems
Conntour uses AI models to let security teams query camera feeds using natural language to find any object, person, or situation.
Cohere launches an open source voice model specifically for transcription
Relatively light at just 2 billion parameters, the model is meant for use with consumer-grade GPUs for those who want to self-host it. It currently supports 14 languages.
A ‘pound of flesh’ from data centers: one senator’s answer to AI job losses
Fears of AI-driven job loss are growing fast, and they’re fueling backlash against data centers. Sen. Mark Warner suggests taxing them to help workers survive the transition.
Mistral releases a new open source model for speech generation
The model, which lets enterprises build voice agents for sales and customer engagement, puts Mistral in direct competition with the likes of ElevenLabs, Deepgram, and OpenAI.
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Visuospatial Perspective Taking in Multimodal Language Models
arXiv:2603.23510v1 Announce Type: new Abstract: As multimodal language models (MLMs) are increasingly used in social and collaborative settings, it is crucial to evaluate their perspective-taking abilities. Existing benchmarks largely rely on text-based vignettes or static scene understanding, leaving visuospatial perspective-taking...
Berta: an open-source, modular tool for AI-enabled clinical documentation
arXiv:2603.23513v1 Announce Type: new Abstract: Commercial AI scribes cost \$99-600 per physician per month, operate as opaque systems, and do not return data to institutional infrastructure, limiting organizational control over data governance, quality improvement, and clinical workflows. We developed Berta,...
DISCO: Document Intelligence Suite for COmparative Evaluation
arXiv:2603.23511v1 Announce Type: new Abstract: Document intelligence requires accurate text extraction and reliable reasoning over document content. We introduce \textbf{DISCO}, a \emph{Document Intelligence Suite for COmparative Evaluation}, that evaluates optical character recognition (OCR) pipelines and vision-language models (VLMs) separately on...
Training a Large Language Model for Medical Coding Using Privacy-Preserving Synthetic Clinical Data
arXiv:2603.23515v1 Announce Type: new Abstract: Improving the accuracy and reliability of medical coding reduces clinician burnout and supports revenue cycle processes, freeing providers to focus more on patient care. However, automating the assignment of ICD-10-CM and CPT codes from clinical...
Plato's Cave: A Human-Centered Research Verification System
arXiv:2603.23526v1 Announce Type: new Abstract: The growing publication rate of research papers has created an urgent need for better ways to fact-check information, assess writing quality, and identify unverifiable claims. We present Plato's Cave as an open-source, human-centered research verification...
Prompt Compression in Production Task Orchestration: A Pre-Registered Randomized Trial
arXiv:2603.23525v1 Announce Type: new Abstract: The economics of prompt compression depend not only on reducing input tokens but on how compression changes output length, which is typically priced several times higher. We evaluate this in a pre-registered six-arm randomized controlled...
Chitrakshara: A Large Multilingual Multimodal Dataset for Indian languages
arXiv:2603.23521v1 Announce Type: new Abstract: Multimodal research has predominantly focused on single-image reasoning, with limited exploration of multi-image scenarios. Recent models have sought to enhance multi-image understanding through large-scale pretraining on interleaved image-text datasets. However, most Vision-Language Models (VLMs) are...
Not All Pretraining are Created Equal: Threshold Tuning and Class Weighting for Imbalanced Polarization Tasks in Low-Resource Settings
arXiv:2603.23534v1 Announce Type: new Abstract: This paper describes my submission to the Polarization Shared Task at SemEval-2025, which addresses polarization detection and classification in social media text. I develop Transformer-based systems for English and Swahili across three subtasks: binary polarization...
Revisiting Real-Time Digging-In Effects: No Evidence from NP/Z Garden-Paths
arXiv:2603.23624v1 Announce Type: new Abstract: Digging-in effects, where disambiguation difficulty increases with longer ambiguous regions, have been cited as evidence for self-organized sentence processing, in which structural commitments strengthen over time. In contrast, surprisal theory predicts no such effect unless...
Perturbation: A simple and efficient adversarial tracer for representation learning in language models
arXiv:2603.23821v1 Announce Type: new Abstract: Linguistic representation learning in deep neural language models (LMs) has been studied for decades, for both practical and theoretical reasons. However, finding representations in LMs remains an unsolved problem, in part due to a dilemma...
OmniACBench: A Benchmark for Evaluating Context-Grounded Acoustic Control in Omni-Modal Models
arXiv:2603.23938v1 Announce Type: new Abstract: Most testbeds for omni-modal models assess multimodal understanding via textual outputs, leaving it unclear whether these models can properly speak their answers. To study this, we introduce OmniACBench, a benchmark for evaluating context-grounded acoustic control...
Argument Mining as a Text-to-Text Generation Task
arXiv:2603.23949v1 Announce Type: new Abstract: Argument Mining(AM) aims to uncover the argumentative structures within a text. Previous methods require several subtasks, such as span identification, component classification, and relation classification. Consequently, these methods need rule-based postprocessing to derive argumentative structures...
The Price Reversal Phenomenon: When Cheaper Reasoning Models End Up Costing More
arXiv:2603.23971v1 Announce Type: new Abstract: Developers and consumers increasingly choose reasoning language models (RLMs) based on their listed API prices. However, how accurately do these prices reflect actual inference costs? We conduct the first systematic study of this question, evaluating...
Sparse Growing Transformer: Training-Time Sparse Depth Allocation via Progressive Attention Looping
arXiv:2603.23998v1 Announce Type: new Abstract: Existing approaches to increasing the effective depth of Transformers predominantly rely on parameter reuse, extending computation through recursive execution. Under this paradigm, the network structure remains static along the training timeline, and additional computational depth...
Synthetic Mixed Training: Scaling Parametric Knowledge Acquisition Beyond RAG
arXiv:2603.23562v1 Announce Type: new Abstract: Synthetic data augmentation helps language models learn new knowledge in data-constrained domains. However, naively scaling existing synthetic data methods by training on more synthetic tokens or using stronger generators yields diminishing returns below the performance...