Search: “meta”

Hannah Murphy / Financial Times: Sources: Meta is building agentic tools, including an OpenClaw-like assistant powered by its new Muse Spark AI model to help users create AI bots — Social media platfo

My surprise here seems warranted, this paper was retracted (There are other peer-reviewed meta-analyses of the impact of AI on education finding positive effects, like: https://www.researchgate.net/pu

Bloomberg: Nearly 20 US state-run health insurance exchanges include ad trackers that send user data like race and citizenship info to companies like Meta, TikTok, Google — Nearly all of the 20 state-

Source via Meta: https://about.fb.com/news/2026/04/powering-ai-strengthening-the-grid-space-solar-energy-and-long-duration-storage/ I break down stories like this every day in my free newsletter. Keep

Bloomberg: A look at casino-style games, considered gambling in some US states, where player “whales” spend tens of thousands via IAP on Apple, Google, and Meta platforms — “please remove this game fr

arXiv:2602.11897v3 Announce Type: replace-cross Abstract: Cybersecurity decision-making increasingly occurs in environments characterized by uncertainty, partial observability, and adversarial manipul

Cool paper from Meta FAIR. It's on self-improving LLMs but on the pretraining side. (bookmark it) Most LLM safety, factuality, and reasoning fixes get bolted on at post-training. By then, the patterns

Annie Palmer / CNBC: Alphabet's stock climbed 10% on Thursday and 34% in April, its best month since 2004; Meta's stock plunged 8.5% on Thursday, its steepest drop since October — Alphabet's stock sur

Manus, an AI company Meta acquired for $2 billion last year is running ads promising quick, easy money with AI: Find local businesses without websites or with bad websites, have AI build them one, the

arXiv:2604.26390v1 Announce Type: cross Abstract: Balancing differential privacy (DP) with recommendation accuracy is a key challenge in privacy-preserving recommender systems, since DP-noise degrades

arXiv:2604.24912v1 Announce Type: cross Abstract: We introduce HAML (Hamiltonian Adaptation via Meta-Learning), a framework for fast online adaptation of effective Hamiltonian models of superconductin

Sheer insanity. Amazon, Google, Microsoft, and Meta collectively are spending more money than the Manhattan Project *every single month*. More than 12x the Manhattan Project every year. And what they

The Guardian: Australia unveils a draft News Bargaining Incentive scheme that imposes a 2.25% levy on local revenues for platforms; Meta says the government is “simply wrong” — Google also rejects nee

arXiv:2604.24178v1 Announce Type: cross Abstract: Multi-Objective Alignment aims to align Large Language Models (LLMs) with diverse and often conflicting human values by optimizing multiple objectives

arXiv:2604.24096v1 Announce Type: cross Abstract: Training reliable respiratory sound classification models remains challenging due to the limited size and subject diversity of datasets. Ensemble meth

China is blocking the Manus acquisition by Meta. A few things stand out: - China has historically been more opaque and less public in restricting foreign acquisitions of Chinese tech assets, while the

Clem Delangue posted about someone flying to work with the llama.cpp team on developing next-generation local AI capabilities, framed as meta commentary on headlines related to the endeavor. The post

arXiv:2604.19072v1 Announce Type: cross Abstract: Semi-supervised learning with manifold regularization is a classical framework for jointly learning from both labeled and unlabeled data, where the ke

Ashley Gold / Axios: Filing: Anthropic spent 1.6M and OpenAI 1M on lobbying in Q1, vs. 360K and 560K in Q1 2025, respectively; Meta topped Big Tech lobbying spending with 7.1M — Both Anthropic and Ope

arXiv:2604.12325v1 Announce Type: cross Abstract: We consider the problem of offline black-box optimization, where the goal is to discover optimal designs (e.g., molecules or materials) from past expe

arXiv:2604.12336v1 Announce Type: cross Abstract: Streaming Data-Driven Optimization (SDDO) problems arise in many applications where data arrive continuously and the optimization environment evolves

arXiv:2410.17976v2 Announce Type: replace-cross Abstract: metasnf is an R package that enables users to apply meta clustering, a method for efficiently searching a broad space of cluster solutions by

arXiv:2601.07208v2 Announce Type: replace-cross Abstract: Group-Relative Policy Optimization (GRPO) has emerged as an efficient paradigm for aligning Large Language Models (LLMs), yet its efficacy is

I think Muse Spark came in far better than most were expecting as the first new model attempt from Meta, especially given the fact that it has been a year since Llama 4 with no models at all (and that

Routing every task to your largest model burns tokens, adds latency, and inflates costs. @AI21Labs' Maestro Orchestration Meta Model (OMM) is the layer above your stack that dynamically selects the ri

sam3.cpp - Meta's SAM 3 in pure C++ with @ggerganov's ggml - Supports SAM 3.1, 3, 2.1, 2 and EdgeTAM - FP16, 4-bit quant (EdgeTAM in 15 MB) - Apple Metal GPU, CUDA, CPU - Text-prompted: 'peach' → ever

arXiv:2610.19479v1 Announce Type: new Abstract: Sim-and-real co-training is an effective way to scale robot learning by augmenting limited real-world demonstrations with simulation data. However, exis

arXiv:2601.03822v2 Announce Type: replace Abstract: Large language models (LLMs) can achieve strong reasoning performance with sufficient computation, but they do not inherently know how much computat

arXiv:2610.18535v1 Announce Type: new Abstract: Video understanding benchmarks are the primary yardstick for measuring progress in video-language modeling, and a growing body of work relies on them to

arXiv:2610.11067v1 Announce Type: new Abstract: Prompt learning is a popular method for adapting foundation models, but learned prompts are typically task-specific and fail to generalize to new classe

arXiv:2610.11117v1 Announce Type: new Abstract: Dynamic convolution enhances convolutional neural networks (CNNs) by adapting kernels to input content, but it expresses the effective kernel as a linea

arXiv:2511.04000v2 Announce Type: replace-cross Abstract: Decision trees are widely used in high-stakes fields like finance and healthcare due to their interpretability. This work introduces an effici

arXiv:2610.10393v1 Announce Type: cross Abstract: Models can be retrained as new data arrive, but deploying every new version risks replacing a good policy with a worse one. We study how to plan polic

arXiv:2610.01949v1 Announce Type: cross Abstract: Ransomware has emerged as a major cybersecurity threat, with incidents increasing in frequency and impact across critical sectors. These attacks are t

arXiv:2609.38143v1 Announce Type: new Abstract: Agent performance depends on both reasoning ability and the environment in which it acts. We study test-time AI-for-AI, asking how a Builder can learn t

arXiv:2609.38147v1 Announce Type: new Abstract: As agents take on longer and more complex problems, controlling the execution becomes a task in its own right. Each step in the run brings new control c

arXiv:2609.21122v1 Announce Type: new Abstract: Manipulating previously unseen objects remains challenging, as their dynamics depend on latent physical properties, such as friction and mass distributi

arXiv:2609.19832v1 Announce Type: new Abstract: Relational table learning has gained increasing attention with the widespread use of relational databases. Existing methods typically rely on deep GNN o

arXiv:2609.18597v1 Announce Type: new Abstract: Propagation structures provide crucial evidence for fake news detection, yet existing approaches primarily rely on supervised GNN-based models, which re

arXiv:2602.00532v2 Announce Type: replace-cross Abstract: Constraint handling is central to constrained black-box optimization (BBO), where objective improvement and feasibility restoration often prov

arXiv:2609.11041v1 Announce Type: new Abstract: No Free Lunch theorem implies that any performance gains achieved by a classifier on a particular image distribution are necessarily offset by a loss of

arXiv:2609.09511v1 Announce Type: cross Abstract: Physical-layer authentication (PLA) in non-terrestrial networks (NTNs) is challenged by severe Doppler shifts, long delays, and fast channel variation

arXiv:2609.06396v1 Announce Type: new Abstract: Recursive self-improvement (RSI) lets a system improve the model-building machinery from its own failures, so every later model inherits the gain. Yet R

arXiv:2608.30678v1 Announce Type: new Abstract: Text-rich image understanding requires multimodal large language models (MLLMs) to organize OCR (Optical Character Recognition)-grounded evidence across

arXiv:2608.26638v1 Announce Type: new Abstract: Across various non-verifiable tasks, human evaluation is reliable but expensive, while automatic metrics are more scalable but often biased. Building on

arXiv:2608.20513v1 Announce Type: cross Abstract: Continuous deployment to large scale production systems creates a tension between release velocity and reliability. Every change is a potential reliab