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Browse filesIntroduce MatrAIx, describe its persona-driven agent evaluation work, and link its public datasets and website.
README.md
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title: MatrAIx
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# MatrAIx
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**Persona-driven simulation for evaluating AI agents in the real world.**
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MatrAIx builds diverse, high-dimensional persona populations that interact with AI systems across surveys, chat, websites, and operating-system applications. Our goal is to help teams evaluate how agents behave for different people, uncover long-tail failures before deployment, and produce reproducible evidence for product improvement and model training.
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## What we build
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- **Persona data:** Structured human, extracted, and synthetic persona attributes spanning demographic, behavioral, professional, cognitive, and contextual dimensions.
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- **Agent evaluation:** Repeatable simulations across survey, conversational, web, and native-app environments.
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- **Evaluation artifacts:** Scores, interaction traces, behavioral evidence, and training-ready data for SFT and RL workflows.
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## Public resources
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| Resource | Description |
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| [MatrAIx Persona 1M Public Release](https://huggingface.co/datasets/MatrAIx2026/MatrAIx_Persona_1M_Public_Release) | Public persona records organized by real-human, extracted, and synthetic sources. |
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| [Demo Application Data](https://huggingface.co/datasets/MatrAIx2026/Demo_Application_Data) | Persona profiles for evaluation tasks across surveys, chat, web, and OS applications. |
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| [Persona8B](https://huggingface.co/datasets/MatrAIx2026/Persona8B) | Large-scale persona data from the broader MatrAIx persona space. |
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| [Existing Data](https://huggingface.co/datasets/MatrAIx2026/Existing_Data) | Supporting public data used in MatrAIx research and development. |
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## Explore MatrAIx
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- Website: [matraix.ai](https://matraix.ai)
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- Interactive persona survey: [matraix.ai/play.html](https://matraix.ai/play.html)
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- Demo application catalog: [Demo Application Data](https://huggingface.co/datasets/MatrAIx2026/Demo_Application_Data)
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- Contact: [info@matraix.ai](mailto:info@matraix.ai)
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We are building evaluation infrastructure for AI agents that must work for the full diversity of real users—not only the average case.
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