Americans pulling into a Starbucks drive thru might think they are being served by a friendly staff member. But at some locations, the voice listening to the order is actually an AI robot. Behind the counter inside the store, baristas can lean on a virtual personal assistant to recall recipes or manage schedules. In the back of the shop, a scanning tool has taken on the painstaking process of counting the inventory, relieving staff of one of retail's most tedious chores, in a bid to fix the out-of-stock gaps that have frustrated the firm. The new technology is part of the hundreds of millions of dollars the 55-year-old coffee giant has been investing as it tries to win back customers after several years of struggling sales.

Large language models show promise as autonomous decision-making agents, yet their deployment in high-stakes domains remains fraught with risk. Without architectural safeguards, LLM agents exhibit catastrophic brittleness: identical capabilities produce wildly different outcomes depending solely on prompt framing. We present Chimera, a neuro-symbolic-causal architecture that integrates three complementary components - an LLM strategist, a formally verified symbolic constraint engine, and a causal inference module for counterfactual reasoning. We benchmark Chimera against baseline architectures (LLM-only, LLM with symbolic constraints) across 52-week simulations in a realistic e-commerce environment featuring price elasticity, trust dynamics, and seasonal demand. Under organizational biases toward either volume or margin optimization, LLM-only agents fail catastrophically (total loss of \$99K in volume scenarios) or destroy brand trust (-48.6% in margin scenarios). Adding symbolic constraints prevents disasters but achieves only 43-87% of Chimera's profit. Chimera consistently delivers the highest returns (\$1.52M and \$1.96M respectively, some cases +\$2.2M) while improving brand trust (+1.8% and +10.8%, some cases +20.86%), demonstrating prompt-agnostic robustness. Our TLA+ formal verification proves zero constraint violations across all scenarios. These results establish that architectural design not prompt engineering determines the reliability of autonomous agents in production environments. We provide open-source implementations and interactive demonstrations for reproducibility.

Xbox users in the US face price increases up to 50 percent on their monthly gaming subscription, making it a great time to check if you're on the right tier or if you even need to subscribe at all. Like it or loathe it, we live in a subscription economy. Music, movies, meal boxes, and more are no longer things you buy once. Gaming is no exception, and while every major player in the sector has some form of sub for players--from PlayStation Plus and Nintendo Switch Online for consoles to Apple Arcade on phones--none of them offered quite as much for a modest monthly fee as Xbox Game Pass. Depending on the subscription tier, the service gave players access to a significant library of titles and was available on Xbox consoles, PC, or via cloud gaming.

Order book imbalance (OBI) - buy orders minus sell orders near the best quote - measures supply-demand imbalance that can move prices. OBI is positively correlated with returns, and some investors try to use it to improve performance. Large orders placed at once can reveal intent, invite front-running, raise volatility, and cause losses. Execution algorithms therefore split parent orders into smaller lots to limit price distortion. In principle, using OBI inside such algorithms could improve execution, but prior evidence is scarce because isolating OBI's effect in real markets is nearly impossible amid many external factors. Multi-agent simulation offers a way to study this. In an artificial market, individual actors are agents whose rules and interactions form the model. This study builds an execution algorithm that accounts for OBI, tests it across several market patterns in artificial markets, and analyzes mechanisms, comparing it with a conventional (OBI-agnostic) algorithm. Results: (i) In stable markets, the OBI strategy's performance depends on the number of order slices; outcomes vary with how the parent order is partitioned. (ii) In markets with unstable prices, the OBI-based algorithm outperforms the conventional approach. (iii) Under spoofing manipulation, the OBI strategy is not significantly worse than the conventional algorithm, indicating limited vulnerability to spoofing. Overall, OBI provides a useful signal for execution. Incorporating OBI can add value - especially in volatile conditions - while remaining reasonably robust to spoofing; in calm markets, benefits are sensitive to slicing design.

When you purchase through links in our articles, we may earn a small commission. Expect price increases on DRAM memory and NAND storage sometime before the end of the year. If you aren't already bracing for price increases across the tech industry, you might want to start because it'll soon be more expensive to buy storage and memory from one of the world's premier brands. South Korean website New Daily reports that tech giant Samsung has announced price increases for DRAM memory and NAND drives. Starting in Q4 of 2025, Samsung's DRAM memory will jump up in price between 15 and 30 percent while their NAND drives will be around 5 to 10 percent more expensive.

Are you tired of paying more and more for video games? Game prices have been creeping up from major publishers for the last few years, with plenty of gamers spotting a recent jump in Nintendo's first-party prices. Microsoft's Xbox brand is following suit, and it's not even waiting for a new console launch to do it. As of today, US prices of every current Xbox game console have shot up. IGN reports that the base model Xbox Series S with 512GB of storage is now 380 (up from 300), and the Series X gets a 100 bump across the board, with the top-end 2TB model going from 600 to 730.

The big tech giants are all-in on AI. Instead of charging 20 per user for Gemini in Workspace, Google is just including it from now on. "We believe that AI is not just another tool; it's a foundational shift in how work gets done," says Google's President of Cloud Applications in a blog post. What a shock, the guy selling melons thinks everyone should be buying melons. "…By removing the need to pay for an add-on to access our latest generative AI capabilities, we're simplifying our plans and pricing to bring the added value of Google AI to all Workspace customers."

Prior to our efforts, there has already been work striving towards this goal. For example, Self-ICL (Chen et al., 2023) begins by prompting the LLM to generate few-shot new, diverse, and creative proxy queries tailored to the target task, and then solves each of that independently using the ZS-CoT manner, which in turn yields proxy exemplars for prompting LLMs to engage in reasoning. Auto-ICL (Yang et al., 2023) operates similarly to Self-ICL, but it differs in that Auto-ICL instructs the LLM to produce proxy queries that have the same structure as the given query. Analogical Prompting (Yasunaga et al., 2023) draws on the cognitive process of solving new problems from relevant past experiences, i.e., inspired by analogical reasoning, which prompts the language model to self-generate relevant examples in context before embarking on the solution of a given query. Notably, the one-pass generation mode employed in Analogical Prompting necessitates that the LLM possesses robust capabilities for both following instructions and generating responses. We revisit the aforementioned approaches and discern that their efficacy hinges on guiding the LLM to recall experiences relevant to the given query. However, solely considering such experiences may lead to the generation of proxy queries that are as challenging as the given query, along with corresponding erroneous proxy solutions, potentially misleading the solution of the original given query.

This study delves into the relationship between emotional trends from X platform data and the market dynamics of well-known cryptocurrencies Cardano, Binance, Fantom, Matic, and Ripple over the period from October 2022 to March 2023. Leveraging SenticNet, we identified emotions like Fear and Anxiety, Rage and Anger, Grief and Sadness, Delight and Pleasantness, Enthusiasm and Eagerness, and Delight and Joy. Following data extraction, we segmented each month into bi-weekly intervals, replicating this process for price data obtained from Finance-Yahoo. Consequently, a comparative analysis was conducted, establishing connections between emotional trends observed across bi-weekly intervals and cryptocurrency prices, uncovering significant correlations between emotional sentiments and coin valuations.

The price of AI Floki (AIFLOKI) is $0.000000038085 today with a 24-hour trading volume of $21,126.88. This represents a 24.50% price increase in the last 24 hours and a price increase in the past 7 days. With a circulating supply of 0 AIFLOKI, AI Floki is valued at a market cap of -. AIFLOKI tokens can be traded on decentralized exchanges and centralized crypto exchanges. The most popular exchange to buy and trade AI Floki is PancakeSwap (v2), where the most active trading pair AIFLOKI/WBNB has a trading volume of $13,519.96 in the last 24 hours.