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...
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...
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.
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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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...
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...
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...
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...
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...
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...
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...
Safe Reinforcement Learning with Preference-based Constraint Inference
arXiv:2603.23565v1 Announce Type: new Abstract: Safe reinforcement learning (RL) is a standard paradigm for safety-critical decision making. However, real-world safety constraints can be complex, subjective, and even hard to explicitly specify. Existing works on constraint inference rely on restrictive assumptions...
CDMT-EHR: A Continuous-Time Diffusion Framework for Generating Mixed-Type Time-Series Electronic Health Records
arXiv:2603.23719v1 Announce Type: new Abstract: Electronic health records (EHRs) are invaluable for clinical research, yet privacy concerns severely restrict data sharing. Synthetic data generation offers a promising solution, but EHRs present unique challenges: they contain both numerical and categorical features...
BXRL: Behavior-Explainable Reinforcement Learning
arXiv:2603.23738v1 Announce Type: new Abstract: A major challenge of Reinforcement Learning is that agents often learn undesired behaviors that seem to defy the reward structure they were given. Explainable Reinforcement Learning (XRL) methods can answer queries such as "explain this...
Self Paced Gaussian Contextual Reinforcement Learning
arXiv:2603.23755v1 Announce Type: new Abstract: Curriculum learning improves reinforcement learning (RL) efficiency by sequencing tasks from simple to complex. However, many self-paced curriculum methods rely on computationally expensive inner-loop optimizations, limiting their scalability in high-dimensional context spaces. In this paper,...
Manifold Generalization Provably Proceeds Memorization in Diffusion Models
arXiv:2603.23792v1 Announce Type: new Abstract: Diffusion models often generate novel samples even when the learned score is only \emph{coarse} -- a phenomenon not accounted for by the standard view of diffusion training as density estimation. In this paper, we show...
HDPO: Hybrid Distillation Policy Optimization via Privileged Self-Distillation
arXiv:2603.23871v1 Announce Type: new Abstract: Large language models trained with reinforcement learning (RL) for mathematical reasoning face a fundamental challenge: on problems the model cannot solve at all - "cliff" prompts - the RL gradient vanishes entirely, preventing any learning...
GRMLR: Knowledge-Enhanced Small-Data Learning for Deep-Sea Cold Seep Stage Inference
arXiv:2603.23961v1 Announce Type: new Abstract: Deep-sea cold seep stage assessment has traditionally relied on costly, high-risk manned submersible operations and visual surveys of macrofauna. Although microbial communities provide a promising and more cost-effective alternative, reliable inference remains challenging because the...
Wireless communication empowers online scheduling of partially-observable transportation multi-robot systems in a smart factory
arXiv:2603.23967v1 Announce Type: new Abstract: Achieving agile and reconfigurable production flows in smart factories depends on online multi-robot task assignment (MRTA), which requires online collision-free and congestion-free route scheduling of transportation multi-robot systems (T-MRS), e.g., collaborative automatic guided vehicles (AGVs)....
Lagrangian Relaxation Score-based Generation for Mixed Integer linear Programming
arXiv:2603.24033v1 Announce Type: new Abstract: Predict-and-search (PaS) methods have shown promise for accelerating mixed-integer linear programming (MILP) solving. However, existing approaches typically assume variable independence and rely on deterministic single-point predictions, which limits solution diversityand often necessitates extensive downstream search...
The least surprising chapter of the Manus story is what’s happening right now
Did anyone think there would not be a reckoning over this tie-up?
Mercor competitor Deccan AI raises $25M, sources experts from India
Deccan AI concentrates its workforce in India to manage quality in a fast-growing but fragmented AI training market.