This limitation prevents HER from densifying the reward.

Jeff Bezos has a unique set of management practices he used and espoused during his time as CEO of Amazon. Amazon founder and former Chief Executive Officer Jeff Bezos honed his leadership philosophy running one of the world's largest companies. Project Prometheus, which Bezos co-founded with scientist Vik Bajaj, will use AI to accelerate engineering and manufacturing in fields like aerospace and automobiles, the New York Times reported. The startup has $6.2 billion in funding, sourced in part from Bezos himself, and employees counted in the dozens, some of whom were poached from leading AI labs like OpenAI and Google DeepMind. As co-CEO with Bajaj, Bezos is back in a formal executive post for the first time since stepping down from Amazon in 2021.

Saudi crown prince to visit U.S. with defense, AI and nuclear energy on agenda In his upcoming visit to the White House, the crown prince is seeking security guarantees and wants access to artificial intelligence technology and progress toward a deal on a civilian nuclear program. RIYADH/WASHINGTON - A visit by Saudi Arabia's de facto ruler to the White House for talks on Tuesday with U.S. President Donald Trump aims to deepen decades-old cooperation on oil and security while broadening ties in commerce, technology and potentially even nuclear energy. It will be the first trip by Crown Prince Mohammed bin Salman to the U.S. since the 2018 killing of Saudi critic Jamal Khashoggi by Saudi agents in Istanbul, which caused a global uproar. U.S. intelligence concluded that the crown prince approved the capture or killing of Khashoggi, a prominent critic. The crown prince, widely known by his initials MBS, denied ordering the operation but acknowledged responsibility as the kingdom's de facto ruler.

FOX News Media CEO Suzanne Scott participated in a fireside chat on Thursday with University of South Carolina students, discussing media operations and the business of journalism.

President Donald Trump has announced US embassies will give visa appointment priority to travellers with tickets to the 2026 World Cup. The Fifa Prioritised Appointment Scheduling System (Pass) will allow World Cup ticket-holders with long wait times to opt with Fifa for a prioritised interview, Trump said at the White House on Monday. Ticket-holders for the tournament - set for next June and July in the US, Canada and Mexico - will not be automatically granted a tourist visa, said Secretary of State Marco Rubio. But foreign nationals with tickets to World Cup football matches could get an interview at an embassy or consulate within six to eight weeks of applying, Rubio said. Your ticket is not a visa; it doesn't guarantee admission to the US, Rubio said, also at the White House on Monday.

This research aims to optimize intricate learning models by implementing quantization and bit-depth optimization techniques. The objective is to significantly cut time complexity while preserving model efficiency, thus addressing the challenge of extended execution times in intricate models. Two medical datasets were utilized as case studies to apply a Logistic Regression (LR) machine learning model. Using efficient quantization and bit depth optimization strategies the input data is downscaled from float64 to float32 and int32. The results demonstrated a significant reduction in time complexity, with only a minimal decrease in model accuracy post-optimization, showcasing the state-of-the-art optimization approach. This comprehensive study concludes that the impact of these optimization techniques varies depending on a set of parameters.

The Model Context Protocol (MCP) has been proposed as a unifying standard for connecting large language models (LLMs) with external tools and resources, promising the same role for AI integration that HTTP and USB played for the Web and peripherals. Yet, despite rapid adoption and hype, its trajectory remains uncertain. Are MCP marketplaces truly growing, or merely inflated by placeholders and abandoned prototypes? Are servers secure and privacy-preserving, or do they expose users to systemic risks? And do clients converge on standardized protocols, or remain fragmented across competing designs? In this paper, we present the first large-scale empirical study of the MCP ecosystem. We design and implement MCPCrawler, a systematic measurement framework that collects and normalizes data from six major markets. Over a 14-day campaign, MCPCrawler aggregated 17,630 raw entries, of which 8,401 valid projects (8,060 servers and 341 clients) were analyzed. Our results reveal that more than half of listed projects are invalid or low-value, that servers face structural risks including dependency monocultures and uneven maintenance, and that clients exhibit a transitional phase in protocol and connection patterns. Together, these findings provide the first evidence-based view of the MCP ecosystem, its risks, and its future trajectory.

The fundamental theorem of statistical learning states that binary PAC learning is governed by a single parameter -- the Vapnik-Chervonenkis (VC) dimension -- which determines both learnability and sample complexity. Extending this to multiclass classification has long been challenging, since Natarajan's work in the late 80s proposing the Natarajan dimension (Nat) as a natural analogue of VC. Daniely and Shalev-Shwartz (2014) introduced the DS dimension, later shown by Brukhim et al. (2022) to characterize multiclass learnability. Brukhim et al. also showed that Nat and DS can diverge arbitrarily, suggesting that multiclass learning is governed by DS rather than Nat. We show that agnostic multiclass PAC sample complexity is in fact governed by two distinct dimensions. Specifically, we prove nearly tight agnostic sample complexity bounds that, up to log factors, take the form $\frac{DS^{1.5}}ε + \frac{Nat}{ε^2}$ where $ε$ is the excess risk. This bound is tight up to a $\sqrt{DS}$ factor in the first term, nearly matching known $Nat/ε^2$ and $DS/ε$ lower bounds. The first term reflects the DS-controlled regime, while the second shows that the Natarajan dimension still dictates asymptotic behavior for small $ε$. Thus, unlike binary or online classification -- where a single dimension (VC or Littlestone) controls both phenomena -- multiclass learning inherently involves two structural parameters. Our technical approach departs from traditional agnostic learning methods based on uniform convergence or reductions to realizable cases. A key ingredient is a novel online procedure based on a self-adaptive multiplicative-weights algorithm performing a label-space reduction, which may be of independent interest.

Low latency event-selection (trigger) algorithms are essential components of Large Hadron Collider (LHC) operation. Modern machine learning (ML) models have shown great offline performance as classifiers and could improve trigger performance, thereby improving downstream physics analyses. However, inference on such large models does not satisfy the $40\text{MHz}$ online latency constraint at the LHC. In this work, we propose \texttt{PHAZE}, a novel framework built on cryptographic techniques like hashing and zero-knowledge machine learning (zkML) to achieve low latency inference, via a certifiable, early-exit mechanism from an arbitrarily large baseline model. We lay the foundations for such a framework to achieve nanosecond-order latency and discuss its inherent advantages, such as built-in anomaly detection, within the scope of LHC triggers, as well as its potential to enable a dynamic low-level trigger in the future.

High-dimensional data often contain low-dimensional signals obscured by structured background noise, which limits the effectiveness of standard PCA. Motivated by contrastive learning, we address the problem of recovering shared signal subspaces from positive pairs, paired observations sharing the same signal but differing in background. Our baseline, PCA+, uses alignment-only contrastive learning and succeeds when background variation is mild, but fails under strong noise or high-dimensional regimes. To address this, we introduce PCA++, a hard uniformity-constrained contrastive PCA that enforces identity covariance on projected features. PCA++ has a closed-form solution via a generalized eigenproblem, remains stable in high dimensions, and provably regularizes against background interference. We provide exact high-dimensional asymptotics in both fixed-aspect-ratio and growing-spike regimes, showing uniformity's role in robust signal recovery. Empirically, PCA++ outperforms standard PCA and alignment-only PCA+ on simulations, corrupted-MNIST, and single-cell transcriptomics, reliably recovering condition-invariant structure. More broadly, we clarify uniformity's role in contrastive learning, showing that explicit feature dispersion defends against structured noise and enhances robustness.