Look Up Say More Versus Creator Hub Switch Off Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Trending Now Safety Net In My Bag VidCon with Mashable Back to School Furtastic All Series Save on laptops, generators, and more. Tabitha Britt is an award-winning freelance journalist, editor, and SEO/AEO strategist. Aside from reviewing dating apps and sex toys for Mashable, Tabitha is also the founding editor-in-chief of DO YOU ENDO -- a digital magazine by individuals with endometriosis, for individuals with endometriosis. She has a Master's degree in Creative Publishing and Critical Journalism from The New School for Social Research and is a grad of Sextech School. You can find more of her work in various online publications, including,, and .

Stone tablets recount a corruption case so widespread that it became a capital offense in 480 CE. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Archival photo of a copy of a similar inscription found elsewhere in the empire. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

Why $370bn tech group Palantir pays 1.4 percent tax rate: Report Palantir Technologies, the United States data analytics and artificial intelligence company which has contracts with the country's military and intelligence apparatus, has "engineered its corporate structure" to pay no US federal corporate income tax, according to a new report. The study by the Centre for International Corporate Tax Accountability and Research (CICTAR) comes as Palantir reports soaring revenues, driven partly by government contracts, while it faces continued criticism for providing technology to the Israeli military amid the genocide in Gaza. What does the report show? Earlier this week, Palantir reported second-quarter revenue of $1.94bn, up 93 percent from a year earlier. But despite its rapid growth, CICTAR said its global effective tax rate was just 1.4 percent in 2025.

Things to Do in L.A. Tap to enable a layout that focuses on the article. This is read by an automated voice. Please report any issues or inconsistencies here . See more from the L.A. Times in Google Search. More than three dozen California legislators are calling for Gov. Gavin Newsom to exempt the state's film and TV production incentive program from a recently approved cap on corporate tax credits, warning that without action it will be "significantly kneecapped."

We derive the first closed-form condition under which artificial intelligence (AI) capital profits could sustainably finance a universal basic income (UBI) without relying on new taxation or the creation of new jobs. In a Solow-Zeira task-automation economy with a CES aggregator $σ< 1$, we introduce an AI capability parameter that scales the productivity of automatable tasks and obtain a tractable expression for the AI capability threshold -- the minimum productivity of AI relative to pre-AI automation required for a balanced transfer. Using current U.S. economic parameters, we find that even in the conservative scenario where no new tasks or jobs emerge, AI systems would only need to reach only 5-7 times today's automation productivity to fund an 11%-of-GDP UBI. Our analysis also reveals some specific policy levers: raising public revenue share (e.g. profit taxation) of AI capital from the current 15% to about 33% halves the required AI capability threshold to attain UBI to 3 times existing automation productivity, but gains diminish beyond 50% public revenue share, especially if regulatory costs increase. Market structure also strongly affects outcomes: monopolistic or concentrated oligopolistic markets reduce the threshold by increasing economic rents, whereas heightened competition significantly raises it. These results therefore offer a rigorous benchmark for assessing when advancing AI capabilities might sustainably finance social transfers in an increasingly automated economy.

Legal reasoning requires both precise interpretation of statutory language and consistent application of complex rules, presenting significant challenges for AI systems. This paper introduces a modular multi-agent framework that decomposes legal reasoning into distinct knowledge acquisition and application stages. In the first stage, specialized agents extract legal concepts and formalize rules to create verifiable intermediate representations of statutes. The second stage applies this knowledge to specific cases through three steps: analyzing queries to map case facts onto the ontology schema, performing symbolic inference to derive logically entailed conclusions, and generating final answers using a programmatic implementation that operationalizes the ontological knowledge. This bridging of natural language understanding with symbolic reasoning provides explicit and verifiable inspection points, significantly enhancing transparency compared to end-to-end approaches. Evaluation on statutory tax calculation tasks demonstrates substantial improvements, with foundational models achieving 76.4\% accuracy compared to 18.8\% baseline performance, effectively narrowing the performance gap between reasoning and foundational models. These findings suggest that modular architectures with formalized knowledge representations can make sophisticated legal reasoning more accessible through computationally efficient models while enhancing consistency and explainability in AI legal reasoning, establishing a foundation for future research into more transparent, trustworthy, and effective AI systems for legal domain.

Category V ariable Definition Tax Avoidance CETR Cash Effective T ax Rate = Cash Taxes Paid / Pre - tax Income GETR GAAP Effective Tax Rate = T otal Tax Expense / Pre - tax Income CETR3 Three - year average CETR GETR3 Three - year average GETR CETR5 Five - year average CETR GETR5 Five - year average GETR A_CETR Adjusted Cash Effective Tax Rate A_GETR Adjusted GAAP Effective T ax Rate A_CETR3 Adjusted three - year average CETR A_GETR3 Adjusted three - year average GETR A_CETR5 Adjusted five - year average CETR A_GETR5 Adjusted five - year average GETR TSTA Total Book - T ax Difference (accrual - based measure) TSDA Discretionary Book - Tax Difference (discretionary accrual - based measure) Profitability ROA Return on Assets = Net Income / Lagged T otal Assets ROE Return on Equity = Net Income / Lagged Equity CFO Operating Cash Flow scaled by total assets LOSS Loss dummy (1 if prior - year net income < 0) Stability LEV Leverage = T otal Liabilities / Total Assets CUR Current Ratio = Current Assets / Current Liabilities SIZE Natural logarithm of total assets PPE Ratio of Property, Plant, and Equipment to total assets AGE Natural logarithm of firm age (based on year of establishment) INVREC Ratio of inventories and receivables to total assets Growth GRW Sales growth rate MB Market - to - Book Ratio = Market Capitalization / Book Equity TQ Tobin's Q = (Market Capitalization + Total Liabilities) / T otal Assets Market Valuation & Governance KOSPI KOSPI listing status dummy BIG4 Big4 audit dummy FORN Foreign ownership share (%) OWN Largest shareholder ownership share (%) Stability Measures Stability measures reflect a firm's financial soundness and its ability to meet obligations. Leverage (LEV) is defined as total liabilities divided by total assets, indicating the firm's degree of financial leverage. The current ratio (CUR), calculated as current assets divided by current liabilities, captures short - term liquidity and payment capacity. Firm size (SIZE) is measured as the natural logarithm of total assets, providing a quantitative indicator of scale. The proportion of property, plant, and eq uipment (PPE), defined as tangible fixed assets divided by total assets, is used to assess the structural stability of the asset base.

Designing adaptive mechanisms to align individual and collective interests remains a central challenge in artificial social intelligence. Existing methods often struggle with modeling heterogeneous agents possessing persistent latent traits (e.g., skills, preferences) and dealing with complex multi-agent system dynamics. These challenges are compounded by the critical need for high sample efficiency due to costly real-world interactions. World Models, by learning to predict environmental dynamics, offer a promising pathway to enhance mechanism design in heterogeneous and complex systems. In this paper, we introduce a novel method named SWM-AP (Social World Model-Augmented Mechanism Design Policy Learning), which learns a social world model hierarchically modeling agents' behavior to enhance mechanism design. Specifically, the social world model infers agents' traits from their interaction trajectories and learns a trait-based model to predict agents' responses to the deployed mechanisms. The mechanism design policy collects extensive training trajectories by interacting with the social world model, while concurrently inferring agents' traits online during real-world interactions to further boost policy learning efficiency. Experiments in diverse settings (tax policy design, team coordination, and facility location) demonstrate that SWM-AP outperforms established model-based and model-free RL baselines in cumulative rewards and sample efficiency.

America needs tax breaks for tradesmen to solve skilled worker shortages threatening national security and infrastructure.