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Overview
Distributional builds testing software that measures whether an artificial intelligence application behaves consistently over time. Founded in 2023 in San Francisco, it treats non-determinism as a statistical property to be characterised rather than eliminated. Enterprises use it to gain confidence before deploying generative systems.
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Distributional's next chapter: Talaria Scientific
Distributional is now Talaria Scientific . Same company, same investors, hard pivot, new mission. This post is the honest version of how we got here and where we're going. What we set out to do We started Distributional in 2023 to solve AI testing for the
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Auditing the Risk Claims of Distributional Reinforcement Learning
Abstract page for arXiv paper 2607.11607: Auditing the Risk Claims of Distributional Reinforcement Learning
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UnpredictaBench: A Benchmark for Evaluating Distributional Randomness in LLMs
Abstract page for arXiv paper 2606.06622: UnpredictaBench: A Benchmark for Evaluating Distributional Randomness in LLMs
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Beyond averages: how DBNL's distribution comparison reveals what summary metrics hide
Archive note: From the Distributional archive: this post is preserved with its original byline and date, and the product it describes has been sunset. Distributional is now Talaria Scientific. Read the pivot post first, then the Talaria manifesto . Your P
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Examples of issues that break agents in production
Archive note: From the Distributional archive: this post is preserved with its original byline and date, and the product it describes has been sunset. Distributional is now Talaria Scientific. Read the pivot post first, then the Talaria manifesto . As we
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