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New York Attorney General Letitia James sues Coinbase and Gemini, claiming their prediction markets violate state laws against illegal gambling (Jonathan Stempel/Reuters)

Jonathan Stempel / Reuters: New York Attorney General Letitia James sues Coinbase and Gemini, claiming their prediction markets violate state laws against illegal gambling — New York's attorney genera

model-releasestechmeme

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Research

NI Sampling: Accelerating Discrete Diffusion Sampling by Token Order Optimization

DGX agent

arXiv:2604.18471v1 Announce Type: new Abstract: Discrete diffusion language models (dLLMs) have recently emerged as a promising alternative to traditional autoregressive approaches, offering the flexi

researcharxiv-cs-lg

21 Apr 2026

NIM4-ASR: Towards Efficient, Robust, and Customizable Real-Time LLM-Based ASR

arXiv:2604.18105v1 Announce Type: cross Abstract: Integrating large language models (LLMs) into automatic speech recognition (ASR) has become a mainstream paradigm in recent years. Although existing L

NL2SQLBench: A Modular Benchmarking Framework for LLM-Enabled NL2SQL Solutions

arXiv:2604.16493v1 Announce Type: cross Abstract: Natural Language to SQL (NL2SQL) technology empowers non-expert users to query relational databases without requiring SQL expertise. While large langu

No One Fits All: From Fixed Prompting to Learned Routing in Multilingual LLMs

arXiv:2604.16937v1 Announce Type: new Abstract: Translation-based prompting is widely used in multilingual LLMs, yet its effectiveness varies across languages and tasks. We evaluate prompting strategi

researcharxiv-cs-cl

No-Worse Context-Aware Decoding: Preventing Neutral Regression in Context-Conditioned Generation

arXiv:2604.16686v1 Announce Type: new Abstract: Large language models (LLMs) can answer questions and summarize documents when conditioned on external contexts (e.g., retrieved evidence), yet context

researcharxiv-cs-cl

Local Ai

Noise-Adaptive Diffusion Sampling for Inverse Problems Without Task-Specific Tuning

arXiv:2604.16919v1 Announce Type: cross Abstract: Diffusion models (DMs) have recently shown remarkable performance on inverse problems (IPs). Optimization-based methods can fast solve IPs using DMs a

local-aiarxiv-cs-cv

Tutorials

Noise Injection: Improving Out-of-Distribution Generalization for Limited Size Datasets

arXiv:2511.03855v2 Announce Type: replace Abstract: Deep learned (DL) models for image recognition have been shown to fail to generalize to data from different devices, populations, etc. COVID-19 dete

tutorialsarxiv-cs-cv

Non-Stationarity in the Embedding Space of Time Series Foundation Models

arXiv:2604.16428v1 Announce Type: new Abstract: Time series foundation models (TSFMs) are widely used as generic feature extractors, yet the notion of non-stationarity in their embedding spaces remain

NOOUGAT: Towards Unified Online and Offline Multi-Object Tracking

arXiv:2509.02111v2 Announce Type: replace Abstract: The long-standing division between extit{online} and extit{offline} Multi-Object Tracking (MOT) has led to fragmented solutions that fail to address

applicationsarxiv-cs-cv

Agents

Nothing better than sprinting on a day-0 release alongside partners that run just as fast. Thank you @sarahmsachs and the @NotionHQ crew. Ex…

Nothing better than sprinting on a day-0 release alongside partners that run just as fast. Thank you @sarahmsachs and the @NotionHQ crew. Excited to see the day 1 reactions continue to roll in today.

agentsfireworks-ai--x

Novel Algorithms for Smoothly Differentiable and Efficiently Vectorizable Contact Manifold Construction

arXiv:2604.17538v1 Announce Type: new Abstract: Generating intelligent robot behavior in contact-rich settings is a research problem where zeroth-order methods currently prevail. Developing methods th

researcharxiv-cs-ro

NPCNet: Navigator-Driven Pseudo Text for Deep Clustering of Early Sepsis Phenotyping

arXiv:2602.03562v2 Announce Type: replace Abstract: Electronic Health Records (EHRs) provide high-dimensional temporal data essential for patient modeling; however, conventional algorithmic approaches

Local Ai

NPU Support?

Ollama does not officially support TPUs or NPUs currently , though community discussions and projects are exploring integration possibilities. The Reddit post likely addresses user questions about whe

local-air-ollama

NTIRE 2026 Rip Current Detection and Segmentation (RipDetSeg) Challenge Report

model-releasesarxiv-cs-cv

arXiv:2604.17070v1 Announce Type: new Abstract: This report presents the NTIRE 2026 Rip Current Detection and Segmentation (RipDetSeg) Challenge, which targets automatic rip current understanding in i

NullFace: Training-Free Localized Face Anonymization

arXiv:2503.08478v2 Announce Type: replace Abstract: Privacy concerns around ever increasing number of cameras are increasing in today's digital age. Although existing anonymization methods are able to

applicationsarxiv-cs-cv

Local Ai

NVGS: Neural Visibility for Occlusion Culling in 3D Gaussian Splatting

arXiv:2511.19202v2 Announce Type: replace Abstract: 3D Gaussian Splatting can exploit frustum culling and level-of-detail strategies to accelerate rendering of scenes containing a large number of prim

local-aiarxiv-cs-cv

OASIS: On-Demand Hierarchical Event Memory for Streaming Video Reasoning

arXiv:2604.17052v1 Announce Type: new Abstract: Streaming video reasoning requires models to operate in a setting where history grows without bound while meaningful evidence remains scarce. In such a

Agents

Oblivion: Self-Adaptive Agentic Memory Control through Decay-Driven Activation

arXiv:2604.00131v2 Announce Type: replace Abstract: Human memory adapts through selective forgetting: experiences become less accessible over time but can be reactivated by reinforcement or contextual

agentsarxiv-cs-cl

Tutorials

OC-Distill: Ontology-aware Contrastive Learning with Cross-Modal Distillation for ICU Risk Prediction

arXiv:2604.16878v1 Announce Type: new Abstract: Early prediction of severe clinical deterioration and remaining length of stay can enable timely intervention and better resource allocation in high-acu

tutorialsarxiv-cs-lg

OD3: Optimization-free Dataset Distillation for Object Detection

arXiv:2506.01942v2 Announce Type: replace Abstract: Training large neural networks on large-scale datasets requires substantial computational resources, particularly for dense prediction tasks such as

OFlow: Injecting Object-Aware Temporal Flow Matching for Robust Robotic Manipulation

arXiv:2604.17876v1 Announce Type: new Abstract: Robust robotic manipulation requires not only predicting how the scene evolves over time, but also recognizing task-relevant objects in complex scenes.

applicationsarxiv-cs-ro

Tools

OK, here's a resolution - I managed to get it to think using these settings: 'thinking': { 'type': 'adaptive', 'display': 'summarized' }, 'o…

OK, here's a resolution - I managed to get it to think using these settings: 'thinking': { 'type': 'adaptive', 'display': 'summarized' }, 'output_config': { 'effort': 'max' } Without 'display': 'summa

toolssimon-willison--x

Omni-Embed-Audio: Leveraging Multimodal LLMs for Robust Audio-Text Retrieval

arXiv:2604.18360v1 Announce Type: cross Abstract: Audio-text retrieval systems based on Contrastive Language-Audio Pretraining (CLAP) achieve strong performance on traditional benchmarks; however, the

applicationsarxiv-cs-cl

OmniHuman: A Large-scale Dataset and Benchmark for Human-Centric Video Generation

model-releasesarxiv-cs-cv

arXiv:2604.18326v1 Announce Type: new Abstract: Recent advancements in audio-video joint generation models have demonstrated impressive capabilities in content creation. However, generating high-fidel

OmniVLA-RL: A Vision-Language-Action Model with Spatial Understanding and Online RL

arXiv:2604.17706v1 Announce Type: new Abstract: Visual-Language-Action (VLA) models represent a paradigm shift in embodied AI, yet existing frameworks often struggle with imprecise spatial perception,

safetyarxiv-cs-ro

OmniZip: Audio-Guided Dynamic Token Compression for Fast Omnimodal Large Language Models

arXiv:2511.14582v2 Announce Type: replace Abstract: Omnimodal large language models (OmniLLMs) have attracted increasing research attention of late towards unified audio-video understanding. However,

On Different Notions of Redundancy in Conditional-Independence-Based Discovery of Graphical Models

arXiv:2502.08531v3 Announce Type: replace Abstract: Conditional-independence-based discovery uses statistical tests to identify a graphical model that represents the independence structure of variable

On Inverse Problems, Parameter Estimation, and Domain Generalization

model-releasesarxiv-cs-lg

arXiv:2506.06024v2 Announce Type: replace-cross Abstract: Signal restoration and inverse problems are key elements in most real-world data science applications. In the past decades, with the emergence

On-Orbit Space AI: Federated, Multi-Agent, and Collaborative Algorithms for Satellite Constellations

arXiv:2604.16518v1 Announce Type: new Abstract: Satellite constellations are transforming space systems from isolated spacecraft into networked, software-defined platforms capable of on-orbit percepti

safetyarxiv-cs-ro

On Safety Risks in Experience-Driven Self-Evolving Agents

arXiv:2604.16968v1 Announce Type: new Abstract: Experience-driven self-evolution has emerged as a promising paradigm for improving the autonomy of large language model agents, yet its reliance on self

safetyarxiv-cs-cl

On the Convergence and Size Transferability of Continuous-depth Graph Neural Networks

arXiv:2510.03923v2 Announce Type: replace Abstract: Continuous-depth graph neural networks, also known as Graph Neural Differential Equations (GNDEs), combine the structural inductive bias of Graph Ne

safetyarxiv-cs-lg

On the Emergence of Syntax by Means of Local Interaction

arXiv:2604.17857v1 Announce Type: new Abstract: Can syntactic processing emerge spontaneously from purely local interaction? We present a concrete instance on a minimal system: an 18,658-parameter two

On the Generalization Bounds of Symbolic Regression with Genetic Programming

model-releasesarxiv-cs-lg

arXiv:2604.17402v1 Announce Type: new Abstract: Symbolic regression (SR) with genetic programming (GP) aims to discover interpretable mathematical expressions directly from data. Despite its strong em

On the Importance and Evaluation of Narrativity in Natural Language AI Explanations

arXiv:2604.18311v1 Announce Type: new Abstract: Explainable AI (XAI) aims to make the behaviour of machine learning models interpretable, yet many explanation methods remain difficult to understand. T

On the Importance of Tactile Sensing for Imitation Learning: A Case Study on Robotic Match Lighting

arXiv:2504.13618v4 Announce Type: replace Abstract: The field of robotic manipulation has advanced significantly in recent years. At the sensing level, several novel tactile sensors have been develope

safetyarxiv-cs-ro

On the Interpolation Effect of Score Smoothing in Diffusion Models

arXiv:2502.19499v3 Announce Type: replace Abstract: Diffusion models have achieved remarkable progress in various domains with an intriguing ability to produce new data that do not exist in the traini

On The Mathematics of the Natural Physics of Optimization

arXiv:2604.17645v1 Announce Type: cross Abstract: A number of optimization algorithms have been inspired by the physics of Newtonian motion. Here, we ask the question: do algorithms themselves obey so

On the Predictive Power of Representation Dispersion in Language Models

arXiv:2506.24106v2 Announce Type: replace Abstract: We show that a language model's ability to predict text is tightly linked to the breadth of its embedding space: models that spread their contextual

On the Robustness of LLM-Based Dense Retrievers: A Systematic Analysis of Generalizability and Stability

arXiv:2604.16576v1 Announce Type: cross Abstract: Decoder-only large language models (LLMs) are increasingly replacing BERT-style architectures as the backbone for dense retrieval, achieving substanti

researcharxiv-cs-cl

On the Sample Complexity of Learning for Blind Inverse Problems

arXiv:2512.23405v4 Announce Type: replace Abstract: Blind inverse problems arise in many experimental settings where both the signal of interest and the forward operator are (partially) unknown. In th

On the Shelf Life of Fine-Tuned LLM-Judges: Future-Proofing, Backward-Compatibility, and Question Generalization

arXiv:2509.23542v2 Announce Type: replace Abstract: The LLM-as-a-judge paradigm is widely used in both evaluating free-text model responses and reward modeling for model alignment and fine-tuning. Rec

safetyarxiv-cs-cl

On the Theory of Continual Learning with Gradient Descent for Neural Networks

arXiv:2510.05573v2 Announce Type: replace-cross Abstract: Continual learning, the ability of a model to adapt to an ongoing sequence of tasks without forgetting earlier ones, is a central goal of arti

One Adapts to Any: Meta Reward Modeling for Personalized LLM Alignment

arXiv:2601.18731v2 Announce Type: replace Abstract: Alignment of Large Language Models (LLMs) aims to align outputs with human preferences, and personalized alignment further adapts models to individu

safetyarxiv-cs-cl

one of my favorite quality of life features

one of my favorite quality of life features Claude Code in the terminal will now show recaps when you switch focus away from the session and then come back. This should help you stay more in flow whil

model-releasesthariq--x

One of the most jarring things about current AI is its lack of introspection ability and metacognition. It doesn't know what it doesn't know…

researchfrancois-chollet--x

One of the most jarring things about current AI is its lack of introspection ability and metacognition. It doesn't know what it doesn't know, how it knows, or how it could find out. It's a one-way sys

One-Step Diffusion with Inverse Residual Fields for Unsupervised Industrial Anomaly Detection

arXiv:2604.18393v1 Announce Type: new Abstract: Diffusion models have achieved outstanding performance in unsupervised industrial anomaly detection (uIAD) by learning a manifold of normal data under t

Agents

OneDrive: Unified Multi-Paradigm Driving with Vision-Language-Action Models

arXiv:2604.17915v1 Announce Type: new Abstract: Vision-Language Models(VLMs) excel at autoregressive text generation, yet end-to-end autonomous driving requires multi-task learning with structured out

agentsarxiv-cs-cv