HM Revenue and Customs has announced a 10-year, £175m deal with the British tech firm Quantexa to provide AI-powered technology to help improve its performance. Quantexa says its systems will combine data collected by HMRC with external sources to help the tax office identify incidents of fraud and fix unintentional errors more quickly. Its tasks will include helping HMRC to assist customer service staff, as well as to identify hidden networks of companies and individuals masking fraudulent activity. Public dissatisfaction with HMRC performance has crept up in recent years, according to government figures. A Freedom of Information request made by the campaigners at the Contentious Tax Group found there were more than 93,000 complaints made about the department in 2024-2025 .

TDK CEO Noboru Saito says the firm is prepared to add investments to ride the global boom in generative artificial intelligence. Electronics component linchpin TDK is prepared to add to what is already its biggest capital spending campaign ever in a push to ride the global boom in generative artificial intelligence. The company has added ¥100 billion ($640 million) to its multiyear investment plan each year since it rolled it out in 2024, and now CEO Noboru Saito says the effort may accelerate to match an expected surge in orders and demand. "Should promising prospects arise, our commitment is to make timely and opportunistic investments," Saito, 59, said in an interview. "If we don't sow the seeds for medium-to long-term growth now, we won't be able to reap the harvest later." In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Trump's hidden five-tap code in handshake with Xi... and the tell-tale'bullfrog smile' that betrayed the president How Kylie Kelce REALLY feels about Taylor Swift after her foul-mouthed wedding rant: Insiders reveal stark'differences' between the sisters-in-law... and truth about'feud' What REALLY goes on in some Equinox steam rooms: Gym insiders reveal eye-popping indecency... secret towel signals used by experimental married men... and clubs with most'aggressive' locker rooms Xi greets Trump with ominous warning about risk of war between US and China: 'Beware the Thucydides Trap' Inside Eric Swalwell's marriage implosion: Disgraced Democrat nowhere in sight at family home as his furious wife appears without her ring and delivers ultimate insult Home Depot and Lowe's use sneaky cameras in theft crackdown - but honest customers are the real victims Buster Murdaugh's explosive reaction as his father's murder conviction is overturned: Insiders reveal all about his secret new life... and jailhouse calls with Alex Inside Carrie Underwood's'grounded' and'traditional' home life on her 400-acre Tennessee farm Grotesque new Michael Jackson allegations raise questions about his accusers so taboo they're almost impossible to ask... but we must: MAUREEN CALLAHAN Kylie Jenner and Timothee Chalamet's mortifying relationship secrets exposed: Her'jealousy'... his pleas for'space'... and why he's now finally'on board' with a proposal Beautiful young mom appeared to have it all. Now her two toddlers are dead after falling into a pool while on COCAINE... and her own parents allegedly made very troubling comments about her Walmart axes 1,000 workers as white-collar jobs bloodbath reaches America's biggest private employer Grief author Kouri Richins gives 40-minute rant about love and calls husband's poisoning murder a'tragedy' as she learns fate in Moscow Mule slaying... and sends deranged message to her sons This miracle drug rapidly reversed my balding. It wrecked my sex life... but a microdosing hack gave me my libido and my hair back MORE: FBI files reveal reports of'four-foot tall' beings emerging from UFOs Scientists have announced a groundbreaking discovery lurking beyond our solar system that rewrites humanity's understanding of the cosmos. A new study led by Princeton University in New Jersey identified more than 10,000 new possible planets trillions of miles from Earth. That included at least 11 worlds described by scientists as'super-Earths.'

MUFG Bank, Mizuho Bank and Sumitomo Mitsui Banking are all likely to gain access to Anthropic's artificial intelligence model, Mythos. Japan's three megabanks are set to secure access to Anthropic's artificial intelligence model, Mythos, according to a person familiar with the matter, after its limited release last month sparked fears of a new age of cybersecurity risks. MUFG Bank, Sumitomo Mitsui Banking Corp. and Mizuho Bank are all likely to gain access to the artificial intelligence model developed by the U.S. firm, the person said, asking not to be identified because the information is private. The planned access was earlier reported by Nikkei. The move comes as financial institutions around the world grow alarmed about the risks created by Mythos, which has an unprecedented ability to detect software vulnerabilities. That has raised concerns that hackers could use Mythos to disrupt critical infrastructure, and access has so far been limited to a small number of U.S. companies and organizations.

Musk's xAI races to get Wall Street firms to use Grok chatbot A chat window for chatbot Grok. Musk's artificial intelligence venture, xAI, is moving with urgency to boost revenue by selling chatbot subscriptions and access to its computing resources before SpaceX's expected IPO next month. Billionaire Elon Musk's xAI has recruited multiple Wall Street firms with ties to his business empire to test its Grok chatbot, according to people familiar with the matter, part of a push to bolster revenue ahead of parent company SpaceX's initial public offering. Apollo Global Management and Morgan Stanley have begun using Grok internally alongside software from other AI model makers, said the people, who spoke on condition of anonymity as the information is not public. Valor Equity Partners is also using Grok, the people said. Despite some banks signing up for Grok, financiers are rarely using the chatbot for work, some of the people said.

Tech founder Jesse Proudman is leaving Washington as the state’s new 9.9% millionaire tax takes hold, warning of a looming "tax flight" to states like Texas.

We establish the first population risk bounds for Kolmogorov-Arnold Networks (KANs) trained by mini-batch SGD with gradient clipping, covering non-private SGD as well as differentially private SGD (DP-SGD) with Gaussian perturbations that interpolate between independent and temporally correlated noise. This setting is substantially closer to practice than prior KAN theory along two axes: training is by mini-batch SGD, the standard recipe for modern networks, rather than full-batch gradient descent (GD); and correlated-noise mechanisms have empirically shown a more favorable privacy-utility tradeoff than independent-noise mechanisms. Our results cover the corresponding full-batch GD and independent-noise DP-GD results for KANs by Wang et al. (2026), while yielding sharper fixed-second-layer specializations. The technical core is a new analysis route for correlated-noise DP training in the non-convex regime. Temporal dependence breaks the conditional-centering structure underlying standard one-step SGD arguments, and the projection step obstructs the exact cancellation structure of correlated perturbations. We address these difficulties through an auxiliary unprojected dynamics, a shifted iterate that absorbs the current noise perturbation, and a high-probability bootstrap certifying projection inactivity. Combining this optimization analysis with a stability-based generalization argument yields the stated population risk bounds. To the best of our knowledge, this is the first optimization and population risk analysis of a correlated-noise mechanism for DP training beyond convex learning, in particular for neural networks.

Reinforcement learning agents for portfolio management are typically trained and deployed as static policies, with no mechanism for using price forecasts at inference time. We propose $\text{FPILOT}$ (**Fin**ancial **P**lugin **I**nference-time **L**earning for **O**ptimal **T**rading), a plugin inference-time optimization framework inspired by Model Predictive Control (MPC). Our key structural insight is that future prices mostly do not depend on one agent's portfolio allocation, so a suitable predictive model can produce a multi-step price trajectory without iterative action-conditioned rollouts as in typical reinforcement learning. At each decision step, we use the forecaster's predicted price trajectory to construct an allocation-based imagined return objective, and optimize the policy at inference-time before executing one step of the trade. Our framework is compatible with any pre-trained agent and adapts the policy to the forecaster's predictions without any retraining. Evaluated across five policy learning algorithms on the TradeMaster DJ30 benchmark, $\text{FPILOT}$ produces consistent improvements in total return and return-based risk-adjusted metrics (Sharpe, Sortino, Calmar), with stochastic policies benefiting more than deterministic ones. Further, using synthetic forecasts at calibrated quality levels, we show that gains consistently improve with forecaster quality, suggesting that our performance will improve based on advances in financial forecasting.

Conformal prediction provides a principled framework for uncertainty quantification with finite-sample coverage guarantees. While recent work has extended conformal prediction to online and sequential settings, existing methods typically focus on a single coverage level and do not ensure consistency across multiple confidence levels. In many real-world applications, such as weather forecasting, macroeconomic prediction, and risk management, different users operate under heterogeneous risk tolerances and require calibrated uncertainty estimates across a range of coverage levels. In such settings, it is desirable to produce prediction sets corresponding to different coverage levels that are nested and valid simultaneously. In this paper, we propose two novel online conformal prediction methods that output \emph{nested prediction sets} across a range of coverage levels, enabling simultaneous uncertainty quantification across the entire risk spectrum. Beyond interpretability, jointly estimating multiple coverage levels is known to improve statistical efficiency in classical quantile regression by enforcing non-crossing constraints and sharing information across quantiles. Our approaches leverage an online optimization perspective with small regret that translates to quantile estimation error control while enforcing nestedness of prediction sets. Empirical results on synthetic and real-world datasets, including applications in forecasting tasks with heterogeneous risk requirements, demonstrate that our method achieves stable coverage across all levels, strictly nested prediction sets, and improved efficiency compared to existing online conformal baselines.

It has been recently shown that e-processes are sufficient for sequential testing in the following sense: every level-$α$ sequential test can be obtained by thresholding an e-process at $1/α$. However, in the above result, neither does the test have to be asymptotically optimal (in terms of stopping times) nor does the e-process have to be asymptotically log-optimal. It has separately been shown that asymptotically log-optimal e-processes yield asymptotically optimal sequential tests. In this paper, we prove the converse, arguably completing the story: it is possible to aggregate asymptotically optimal sequential tests into asymptotically log-optimal e-processes. This is accomplished by using a new class of WAIT e-processes: those that are Weighted Aggregates of Indicators of stopping Times that begin at zero, are nondecreasing and increase to infinity under the alternative at the optimal rate. Importantly, the paper discusses several nuances in the varied definitions of asymptotic (log-)optimality.