How to Build a Credit Scoring Grid From a Logistic Regression Model

AQSolotl and QuantrolOx Partner to Automate Quantum System Calibration

AQSolotl and QuantrolOx have announced a strategic partnership to integrate AQSolotl's Chronos-Q quantum control hardware with QuantrolOx's Quantum EDGE machine learning-based automation platform. The goal is to automate qubit calibration, reduce manual tuning cycles, and improve qubit stability as quantum systems scale toward commercial deployment. The collaboration will proceed in two phases: near-term technical integration and performance benchmarking, followed by deeper hardware-software co-design and joint commercial offerings for research and enterprise customers.

FirstQFM Reports Quantum Forecasting Results Using NVIDIA CUDA-Q FirstQFM Reports Quantum Forecasting Results Using NVIDIA CUDA-Q

FirstQFM announced at ISC 2026 that its Quantum Reservoir Computing (QRC) system achieved a 56.1% series-level win rate against leading classical foundation-model baselines in zero-shot financial time-series forecasting. Built on proprietary Quantum Foundation Models and powered by NVIDIA CUDA-Q, cuQuantum, and cuTensorNet, the system was scaled on the Leonardo Supercomputer. The company claims to outperform AI forecasting models from Google, Salesforce, and Amazon on NISQ-era hardware, with plans for both cloud and on-premises enterprise deployments using NVIDIA NVQLink.

Google Finance gets a dedicated app for Android

Google has launched a dedicated Android app for Google Finance, offering watchlists, real-time market data, live financial news, and an AI-powered 'Key Moments' feature that explains stock movements. An iOS version is planned for later. The move puts Google in direct competition with Yahoo Finance and Robinhood. Alongside the app, Google is rolling out portfolio tracking globally on the web, letting users view holdings in a single dashboard and interact with an AI research tool using natural-language queries. New AI task features allow users to set up automated briefings and performance summaries based on their watchlists or portfolios.

The Hot Path Belongs to GBDTs, Agents Own the Cold Path: A Payment-Fraud Benchmark

A reproducible benchmark comparing gradient-boosted decision trees (GBDTs) vs. LLM-based scoring for payment fraud detection across three dimensions: latency, cost, and determinism. On a single CPU core, GBDTs hit p99 latency of 0.15ms vs. ~1,200ms for LLMs — well outside the 100ms ISO 8583 authorization budget. Cost-wise, GBDTs run ~$54/hour at 50K TPS vs. $16,200–$351,000 for LLM tiers. Determinism is the most critical issue for regulated environments: GBDTs return identical scores on identical inputs while LLMs produce hundreds of distinct outputs even at temperature=0. The recommended architecture keeps deterministic tree ensembles on the synchronous hot path and deploys LLM agents on the asynchronous cold path for SAR drafting, evidence gathering, and agent-as-a-judge validation before human review. All benchmark code is open-source and reproducible on a laptop.

Taktile raises $110M to put AI in charge of bank decisions

Taktile, a Berlin-and-New-York startup, has raised $110M in a Series C led by Goldman Sachs Alternatives, bringing total funding to $184M. The company sells an Agentic Decision Platform that blends AI agents, hard rules, data, and human oversight to automate high-stakes financial decisions — including loan underwriting, insurance claims, customer onboarding, and anti-money-laundering checks. Clients include Mercury, Monzo, Faire, and Pleo. Taktile claims customers have achieved 95% automation in B2B underwriting and a 75% reduction in AML false positives. The company argues general-purpose AI is insufficient for regulated financial decisions where errors cost millions, positioning its platform as auditable and steerable by business owners, not just engineers. The funding will go toward expanding in the US, Europe, and Latin America, including a new São Paulo office.

Hexora v0.3: New features and improvements

Hexora v0.3 is a Python library for detecting malicious PyPI packages using static analysis. The new release adds a gradient boosting machine learning model that analyzes code structure, semantic features, and static analysis results to assess entire Python files. The ML model's primary role is filtering false positives — previously yielding 5-10 false positives per real finding. Running against newly published PyPI packages, it now detects 2-10 malicious packages daily. Remaining false positives mostly come from AI-related projects that use dynamic code execution, base64-inlined assets, or telemetry.

Cfet Ibm Plan Bets On Sequential Nanostack Power

Các nhà nghiên cứu tại Princeton đã phát triển phương pháp học tăng cường (reinforcement learning) kết hợp mô hình mạng nơ-ron tích chập (CNN) để tự động hóa thiết kế mạch tích hợp tần số vô tuyến (RFIC), rút ngắn thời gian từ vài tháng xuống còn vài phút. Hệ thống tạo ra các layout RFIC sẵn sàng sản xuất với hiệu suất băng thông kỷ lục cho bộ khuếch đại công suất 5G năm 2023, nhưng vẫn cần xác minh thủ công do hiện tượng "ảo giác AI" và thiếu dữ liệu mở.

Những tiến bộ trong AI tự động hóa thiết kế RFIC của IBM và Princeton sẽ giúp bạn tiết kiệm thời gian và năng lượng trong việc tối ưu hóa các giải pháp điện tử cao tần, đặc biệt khi công nghệ này đang thay đổi nhanh chóng và đòi hỏi sự linh hoạt trong thiết kế.