This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG . FBI director defends Georgia election probe, touts'historic' crime drop Why Trump's lawsuit against the IRS is'something you don't see every day' Inside the FBI's investigation into paid protest groups Tech expert warns social media execs sound like'drug lords' as addiction trial begins FBI Director Kash Patel calls capture of two top fugitives a'historic moment' Christian gaming company says its mission invites children'into a world of hope and God's truth' Fox News Flash top headlines are here.

Amazon agrees to pay $2.5 billion to settle FTC allegations over Prime enrollment practices and difficult cancellation processes, with $1.5 billion for consumer refunds.

Everlane, Revolve and Under Armour pilot Happy Returns' AI system to combat return fraud as retailers prepare for $850 billion in returned goods in 2025.

From dead crabs to shredded bed sheets, fraudsters are using fake photos and videos to get their money back from ecommerce sites. I don't want to admit it, but I did spend a lot of money online this holiday shopping season. And unsurprisingly, some of those purchases didn't meet my expectations. A photobook I bought was damaged in transit, so I snapped a few pictures, emailed them to the merchant, and got a refund. Online shopping platforms have long depended on photos submitted by customers to confirm that refund requests are legitimate.

Data brokers sell personal shopping information to scammers who craft convincing fake refund messages during peak holiday purchasing periods.

How to make sure you're getting a good deal on Black Friday Whether you're excited for the seasonal sales or avoiding the shops altogether, it's hard to escape the countless emails and social media adverts on Black Friday deals. The US holiday - which falls this Friday - has been firmly adopted by UK retailers, and what was once a single day of sales now spans the weeks before and after. However eight in 10 deals promoted during this buying bonanza were cheaper or the same price outside of the four-week Black Friday period, according to research from consumer group Which? This suggests shoppers could get the same or a better deal at other times of the year. But if you're planning to buy now, here's how to make sure you bag a bargain.

AI agents increasingly rely on external tools to solve complex, long-horizon tasks. Advancing such agents requires reproducible evaluation and large-scale training in controllable, diverse, and realistic tool-use environments. However, real-world APIs are limited in availability, domain coverage, and stability, often requiring access keys and imposing rate limits, which render them impractical for stable evaluation or scalable training. To address these challenges, we introduce SynthTools, a flexible and scalable framework for generating synthetic tool ecosystems. Our framework consists of three core components: Tool Generation for automatic and scalable creation of diverse tools, Tool Simulation to emulate realistic tool behaviors, and Tool Audit to ensure correctness and consistency of tool simulation. To illustrate its scalability, we show that SynthTools can readily produce toolsets that span twice as many domains and twice as many tools per domain as prior work. Furthermore, the tool simulation and tool audit components demonstrate strong reliability, achieving $94\%$ and $99\%$ accuracy respectively. Finally, we construct downstream tasks from the generated tools that even state-of-the-art models struggle to complete. By enabling scalable, diverse, and reliable tool ecosystems, SynthTools provides a practical path toward large-scale training and stable evaluation of tool-use agents. Our code is available at https://github.com/namkoong-lab/SynthTools.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Millions of Californians are getting a refund on their electric bill. Credits for Californians on October electric bills are set to go up in the coming years, according to the governor's office. This is read by an automated voice. Please report any issues or inconsistencies here .

In this paper, we propose Selection and Pooling with Large Language Models (SPILL), an intuitive and domain-adaptive method for intent clustering without fine-tuning. Existing embeddings-based clustering methods rely on a few labeled examples or unsupervised fine-tuning to optimize results for each new dataset, which makes them less generalizable to multiple datasets. Our goal is to make these existing embedders more generalizable to new domain datasets without further fine-tuning. Inspired by our theoretical derivation and simulation results on the effectiveness of sampling and pooling techniques, we view the clustering task as a small-scale selection problem. A good solution to this problem is associated with better clustering performance. Accordingly, we propose a two-stage approach: First, for each utterance (referred to as the seed), we derive its embedding using an existing embedder. Then, we apply a distance metric to select a pool of candidates close to the seed. Because the embedder is not optimized for new datasets, in the second stage, we use an LLM to further select utterances from these candidates that share the same intent as the seed. Finally, we pool these selected candidates with the seed to derive a refined embedding for the seed. We found that our method generally outperforms directly using an embedder, and it achieves comparable results to other state-of-the-art studies, even those that use much larger models and require fine-tuning, showing its strength and efficiency. Our results indicate that our method enables existing embedders to be further improved without additional fine-tuning, making them more adaptable to new domain datasets. Additionally, viewing the clustering task as a small-scale selection problem gives the potential of using LLMs to customize clustering tasks according to the user's goals.

The Consumer Financial Protection Bureau proposed a new measure on Friday that could protect your Robux from scammers and hackers. The proposed rule would interpret terms in the Electronic Fund Transfer Act, or EFTA, which has traditionally been used to protect consumers from unauthorized debit transactions, to include some virtual currencies supplied by gaming and cryptocurrency companies. "Gamers--or in some cases their parents and guardians--have reported issues such as trouble when converting dollars to in-game currency, unauthorized transactions, account hacks and takeovers, theft, scams, and loss of assets," reads the CFPB's post announcing the proposal. "They have also described receiving limited to no help from gaming companies and the banks or digital wallets involved. Refunds are often denied, people are finding their gaming accounts suspended by the video game company after a player tries to get a refund from their financial institution, or people are left caught in doom loops with AI-powered customer service representatives while they're just trying to get straight answers."