--Decentralized trading of real-world alternative assets (e.g., gold) requires bridging physical asset custody with blockchain systems while meeting strict requirements for compliance, liquidity, and risk management. We present a research-oriented architecture, GoldMine OS, that employs multiple specialized AI agents to automate and secure the tokenization and exchange of physical gold into a blockchain-based stablecoin ("OZ"). We detail the design of four cooperative agents (for Compliance, T oken Issuance, Market-Making, and Risk Control) and a coordinating core, and we evaluate the system through both simulation and a controlled pilot deployment. In experiments, the prototype achieves on-demand token issuance in under 1.2 s, a speed-up of over 100 compared to traditional manual workflows. The integrated Market-Making agent provides tight liquidity (spreads often <0.5%) even under volatile market conditions. Through fault injection tests, we demonstrate the system's resilience: an oracle price spoofing attack is detected and mitigated within 10 s, and a simulated vault mis-reporting triggers an immediate halt of issuances with minimal impact on users. Our results indicate that an AI-agent-based decentralized exchange for alternative assets can meet rigorous performance and safety requirements. We discuss the broader implications for democratizing access to traditionally illiquid assets and outline how our governance model (multi-signature agent updates and on-chain community voting on risk parameters) ensures ongoing transparency, adaptability, and formal assurance of system integrity. Tokenizing real-world assets (RW As) like precious metals on blockchains promises to democratize access to alternative investments, but it raises significant challenges in trust, compliance, and market stability [1] [2]. For instance, gold-backed cryptocurrencies such as P AX Gold (P AXG) and Tether Gold (XAUT) peg digital tokens to physical gold reserves, yet they rely heavily on centralized processes for custody and compliance [2]. Achieving a truly decentralized yet regulatorily compliant trading platform for assets like gold remains an open problem. Key hurdles include ensuring that on-chain token supply always mirrors off-chain reserves (requiring robust proof-of-reserve mechanisms), automating complex compliance checks (KYC/AML) in a user-friendly manner, providing continuous liquidity in thinly-traded assets, and guarding against failures of external data sources (the well-known oracle problem [3]). In this paper, we address these challenges by designing and evaluating GoldMine OS, an AI-driven multi-agent architecture for decentralized trading of gold-backed tokens.

WeTrade Group a global diversified "software as a service" ("SaaS") technology service provider committed to providing technical support and digital transformation tools for enterprises across multiple industries, announced that it has entered into those certain securities purchase agreements with certain accredited investors for the sale and issuance of a new series of senior secured convertible notes in the original principal amount of $18,333,333.33 The net proceeds, after original issue discount will total $16.5 million. The transaction has not been closed as of the date hereof. Provided no Event of Default (as defined in the Notes) has occurred, the Notes will accrue interest at an interest rate of 5% per annum, and the Company will be required to pay installment amounts, or at its option redeem such amounts under the Notes each month commencing on the last trading day of the calendar month in which the control account trigger date occurs, and thereafter, the last trading day of each calendar month until the maturity date of eighteen months from issuance, at which time all outstanding amounts remaining, if any, will be due and payable in full. At the Company's option, each redemption payment can be made in cash or stock at a discount to the then-current closing bid price at the time of the redemption.

There is considerable evidence that machine learning algorithms have better predictive abilities than humans in various financial settings. But, the literature has not tested whether these algorithmic predictions are more rational than human predictions. We study the predictions of corporate earnings from several algorithms, notably linear regressions and a popular algorithm called Gradient Boosted Regression Trees (GBRT). On average, GBRT outperformed both linear regressions and human stock analysts, but it still overreacted to news and did not satisfy rational expectation as normally defined. By reducing the learning rate, the magnitude of overreaction can be minimized, but it comes with the cost of poorer out-of-sample prediction accuracy. Human stock analysts who have been trained in machine learning methods overreact less than traditionally trained analysts. Additionally, stock analyst predictions reflect information not otherwise available to machine algorithms.

The objective of this paper is to explore how financial big data and machine learning methods can be applied to model and understand financial products. We focus on residential mortgage backed securities, resMBS, which were at the heart of the 2008 US financial crisis. These securities are contained within a prospectus and have a complex waterfall payoff structure. Multiple financial institutions form a supply chain to create prospectuses. To model this supply chain, we use unsupervised probabilistic methods, particularly dynamic topics models (DTM), to extract a set of features (topics) reflecting community formation and temporal evolution along the chain. We then provide insight into the performance of the resMBS securities and the impact of the supply chain through a series of increasingly comprehensive models. First, models at the security level directly identify salient features of resMBS securities that impact their performance. We then extend the model to include prospectus level features and demonstrate that the composition of the prospectus is significant. Our model also shows that communities along the supply chain that are associated with the generation of the prospectuses and securities have an impact on performance. We are the first to show that toxic communities that are closely linked to financial institutions that played a key role in the subprime crisis can increase the risk of failure of resMBS securities.

The average time it took from filing to issuance was 850 days. The patent that took the longest time to issue was U.S. Patent No. 10410308, System, method, and device for personal medical care, intelligent analysis, and diagnosis.

We introduce a random forest approach to enable spreads' prediction in the primary catastrophe bond market. We investigate whether all information provided to investors in the offering circular prior to a new issuance is equally important in predicting its spread. The whole population of non-life catastrophe bonds issued from December 2009 to May 2018 is used. The random forest shows an impressive predictive power on unseen primary catastrophe bond data explaining 93% of the total variability. For comparison, linear regression, our benchmark model, has inferior predictive performance explaining only 47% of the total variability. All details provided in the offering circular are predictive of spread but in a varying degree. The stability of the results is studied. The usage of random forest can speed up investment decisions in the catastrophe bond industry.

Ask tech analysts for a digital transformation definition and many will have a quick one at the ready. In the trenches of businesses struggling to automate processes with AI, analytics and rules-based engines, there's less spoken of digital transformation and more about bottom-line results -- one project at a time. Without passing judgment on the accuracy of IDC's, Forrester Research's and Gartner's digital transformation definitions (see "Leading analyst firms describe digital transformation"), it's easy to agree they're all convincingly vague. In the content management sphere, the meaning of the term digital transformation can lean toward making content from all corners of the enterprise accessible to all users, subdivided to just the snippet of text and graphics that an employee or customer needs at the time -- and nothing more -- dynamically rendered to the device in use. But whatever the true meaning of digital transformation, customers and vendors at the PegaWorld 2018 user conference in Las Vegas had difficultly agreeing on, or even expressing, how to define this shopworn tech expression in the real world, where actual CFOs approve real budget spending on technology that affects everyday workflow.

WIRE)--CORNAMI, a high-performance computing company in artificial intelligence (AI), machine learning, and big data, today announced that key monolithic patents around its next generation, highly-efficient, advanced, multi-core architecture technology have been issued, thereby greatly enhancing the CORNAMI IP asset value portfolio. The CORNAMI patent portfolio now has over 60 patents with more than another dozen pending in US and International PTO (Patent and Trademark offices). CORNAMI has developed a non-Von Neumann parallel architecture with independent decision making capabilities at each processing core, interspersed with high-speed memory, all interconnected by a biologically inspired network to produce a scalable "sea of cores". This unique architecture delivers tremendous advancements in efficient multi-core parallel processing that dramatically changes the output-to-power performance at the petabyte data-set scale. There is built-in demand for real-time and actionable data analytics for applications in the hyper-growth big data, machine learning and AI markets.