※この投稿は 2026 年 5 月 11 日に The MATLAB Blog へ 投稿されたものの抄訳です。 --

今日のゲストブロガーは Rikin Mehta です。Rikin は MathWorks の Database Toolbox チームのソフトウェア エンジニアです。この投稿では、エージェントがどこで苦労しているかを発見することから、実際に問題を解決するものを作成することまで、MATLAB Agentic Toolkit の ORM スキルを開発した背景にあるストーリーを共有します。

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※この投稿は 2026 年 5 月 11 日に The MATLAB Blog へ 投稿されたものの抄訳です。 --

今日のゲストブロガーは Rikin Mehta です。Rikin は MathWorks の Database Toolbox チームのソフトウェア エンジニアです。この投稿では、エージェントがどこで苦労しているかを発見することから、実際に問題を解決するものを作成することまで、MATLAB Agentic Toolkit の ORM スキルを開発した背景にあるストーリーを共有します。

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Verification, standards, and the role of executable models in digital engineering Trust Is Proven, Not Assumed In the industries our customers operate in, including aerospace and defense, automotive, industrial automation and machinery, medical devices, and rail, trust is never assumed. It is proven. These industries are built on an uncomfortable truth: humans make mistakes. Systems behave in unexpected ways. Interactions compound risk. That is why certification frameworks and standards such as DO‑178C for aerospace and defense, ISO 26262 for automotive functional safety, and IEC 61508 for industrial and railway systems exist. They are not bureaucratic overhead. They are how organizations enforce discipline, reduce risk, and deliver systems they can stand behind. For background on these standards and how they shape engineering practice, see MathWorks resources for: Aerospace and...

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Verification, standards, and the role of executable models in digital engineering Trust Is Proven, Not Assumed In the industries our customers operate in, including aerospace and defense, automotive, industrial automation and machinery, medical devices, and rail, trust is never assumed. It is proven. These industries are built on an uncomfortable truth: humans make mistakes. Systems behave in unexpected ways. Interactions compound risk. That is why certification frameworks and standards such as DO‑178C for aerospace and defense, ISO 26262 for automotive functional safety, and IEC 61508 for industrial and railway systems exist. They are not bureaucratic overhead. They are how organizations enforce discipline, reduce risk, and deliver systems they can stand behind. For background on these standards and how they shape engineering practice, see MathWorks resources for: Aerospace and...

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A practical MATLAB walkthrough comparing tracking error and exact exposure approaches.

When you build a factor-based portfolio, the central design choice is how strictly to enforce your factor views. Do you treat target exposures as soft goals or do you impose them as hard constraints that the portfolio must satisfy exactly? That decision can materially change the diversification, concentration, and feasibility of the resulting portfolio.

In this blog post, we use data from the Fama-French Data Library, a widely used academic source, to make the comparison practical in a realistic setting. While the example uses the Fama-French three-factor structure—market (Mkt), size (SMB), and value (HML)—the techniques apply to any multi-factor framework.

We compare two practical optimization approaches using MATLAB®:

Tracking Error Minimization—Targets specific factor exposures...

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A practical MATLAB walkthrough comparing tracking error and exact exposure approaches.

When you build a factor-based portfolio, the central design choice is how strictly to enforce your factor views. Do you treat target exposures as soft goals or do you impose them as hard constraints that the portfolio must satisfy exactly? That decision can materially change the diversification, concentration, and feasibility of the resulting portfolio.

In this blog post, we use data from the Fama-French Data Library, a widely used academic source, to make the comparison practical in a realistic setting. While the example uses the Fama-French three-factor structure—market (Mkt), size (SMB), and value (HML)—the techniques apply to any multi-factor framework.

We compare two practical optimization approaches using MATLAB®:

Tracking Error Minimization—Targets specific factor exposures...

read more >>

]]> Thu, 14 May 2026 00:00:00 +0000

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In modern financial markets, pricing, hedging, and risk decisions all hinge on volatility modelling. Yet volatility is not directly observable. It has to be inferred from the prices of listed vanilla options and represented as a surface that encodes forward-looking market expectations across strikes and maturities. This is where conventional calibration approaches start to fail. The Fragile Art of Modeling Volatility The drift term, which captures an asset’s long-term direction, is rigidly set by no arbitrage conditions. Volatility is not. “Volatility is far more challenging,” explains Dr. Benjamin Joseph, Quantitative Researcher and Lead Core App Developer at genOTC. “Current models rely on simplifying parametric assumptions you wouldn’t necessarily want to impose; they are fragile and unstable. When these assumptions break, your model will struggle to replicate what the market is telling you.” Most institutions rely on parametric families such as SVI or SABR that impose...

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Today's guest blogger is Rikin Mehta. Rikin is a Software Engineer on the Database Toolbox team at MathWorks. In this post, he shares the story behind engineering the ORM Skill for the MATLAB Agentic Toolkit, from discovering where agents struggle to building something that actually fixes the problem.

If you read Mike Croucher's

...

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Today's guest blogger is Rikin Mehta. Rikin is a Software Engineer on the Database Toolbox team at MathWorks. In this post, he shares the story behind engineering the ORM Skill for the MATLAB Agentic Toolkit, from discovering where agents struggle to building something that actually fixes the problem.

If you read Mike Croucher's

...

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Forecasting workflows often begin with exploration:

The Time Series Modeler app in MATLAB is designed for this early phase. It lets you build,

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Guest Writer: Jessa Karlberg Jessa is a software engineer on the Graphics Interactions and Accessibility team at MathWorks. After years of creating her own charts and data visualizations, she now develops tools to help others do the same, with an eye to interactive design and user experience. When she’s not coding, she enjoys drinking way too much tea and knitting copious quantities of hats.

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Model-Based Systems Engineering (MBSE) promises better decisions, stronger traceability, and clearer alignment across teams. But in many organizations, the day-to-day reality still feels fragmented. Requirements may live in one tool. Architecture lives in another. Analysis happens somewhere else. Verification evidence is scattered across reports, scripts, and specialized tools. Teams may have models, but they still struggle to keep engineering data connected, current, and authoritative. The gap matters because digital engineering does not succeed just because a team creates more digital artifacts. It succeeds when engineering information stays connected in ways that help people make decisions, understand change, and maintain confidence in what is true. The hard part is rarely just creating models. The hard part is making them work together across disciplines and across tools. The Evolution of Systems Engineering Systems engineering has already evolved well beyond a...

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TL:DR: The latest update of the MATLAB Agentic Toolkit includes the first of a set of advanced toolbox-specific skills that allow AI agents to perform various tasks more quickly, cheaply and robustly. This first round focuses on Database Toolbox with other toolbox-related skills in the pipeline.

Skills: Strengthening an AI's MATLAB-Fu

TL:DR: The latest update of the MATLAB Agentic Toolkit includes the first of a set of advanced toolbox-specific skills that allow AI agents to perform various tasks more quickly, cheaply and robustly. This first round focuses on Database Toolbox with other toolbox-related skills in the pipeline.

Skills: Strengthening an AI's MATLAB-Fu

Modern devices are extraordinarily capable. Yet most still perceive the world the same way a human eye does, seeing color and shape but missing the deeper information the light spectrum contains. Far more exists in that spectrum than conventional sensors can capture, and for decades, it has remained out of reach. Agate Sensors is changing that with the world's first fully software-defined hyperspectral chip that brings lab-grade spectral intelligence to an ultra-compact, mass-manufacturable form factor. By shifting intelligence from rigid, costly hardware to software, the team is making what was once inaccessible far more accessible. "By bringing lab-grade hyperspectral capabilities to an ultra-compact, fully software-controlled chip, we are redefining spectral measurement and defining a new category of capabilities that simply did not exist before," says Mikael Westerlund, Chief Business Officer and Co‑founder of Agate...

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Modern devices are extraordinarily capable. Yet most still perceive the world the same way a human eye does, seeing color and shape but missing the deeper information the light spectrum contains. Far more exists in that spectrum than conventional sensors can capture, and for decades, it has remained out of reach. Agate Sensors is changing that with the world's first fully software-defined hyperspectral chip that brings lab-grade spectral intelligence to an ultra-compact, mass-manufacturable form factor. By shifting intelligence from rigid, costly hardware to software, the team is making what was once inaccessible far more accessible. "By bringing lab-grade hyperspectral capabilities to an ultra-compact, fully software-controlled chip, we are redefining spectral measurement and defining a new category of capabilities that simply did not exist before," says Mikael Westerlund, Chief Business Officer and Co‑founder of Agate...

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※この投稿は 2026 年 4 月 26 日に Guy on Simulink へ 投稿されたものの抄訳です。

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]]> Mon, 27 Apr 2026 00:00:00 +0000

The R2026a release is now available, and I’ve selected a few highlights to keep you up to date on the most important graphics and app building features. This release introduces a new way to publish interactive graphics to the web and collaborate on apps using MATLAB Drive, along with a new chart for exploring data distributions, updates to the axes toolbar, and extensions to existing features that may not already be on your radar.  R2026a also improves graphics stability and memory usage, enabling more visualizations from large data sets than in previous releases.

See the release notes for a complete list.

Share Interactive Graphics on the Web with Web Canvas     Have you ever wanted to zoom in, pan, or rotate an exported MATLAB plot displayed on a webpage? Now you can! R2026a introduces web canvases that preserve the...

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The R2026a release is now available, and I’ve selected a few highlights to keep you up to date on the most important graphics and app building features. This release introduces a new way to publish interactive graphics to the web and collaborate on apps using MATLAB Drive, along with a new chart for exploring data distributions, updates to the axes toolbar, and extensions to existing features that may not already be on your radar.  R2026a also improves graphics stability and memory usage, enabling more visualizations from large data sets than in previous releases.

See the release notes for a complete list.

Share Interactive Graphics on the Web with Web Canvas     Have you ever wanted to zoom in, pan, or rotate an exported MATLAB plot displayed on a webpage? Now you can! R2026a introduces web canvases that preserve the...

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