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  <title>Latent blog</title>
  <subtitle>Writing from the team building Latent: the research behind reading a model&#x27;s internal state, and what we measure.</subtitle>
  <link href="https://runlatent.ai/blog"/>
  <link rel="self" href="https://runlatent.ai/blog/feed.xml"/>
  <id>https://runlatent.ai/blog</id>
  <updated>2026-10-04T00:00:00Z</updated>
  <entry>
    <title>Latent is live</title>
    <link href="https://runlatent.ai/blog/latent-is-live"/>
    <id>https://runlatent.ai/blog/latent-is-live</id>
    <updated>2026-10-04T00:00:00Z</updated>
    <author><name>Vedant Gaur</name></author>
    <summary>Starting today, any team can sign up for Latent, add one line of Python and see which of its model&#x27;s answers are made up. Free to start.</summary>
  </entry>
  <entry>
    <title>Latent against an LLM judge</title>
    <link href="https://runlatent.ai/blog/latent-vs-llm-judge"/>
    <id>https://runlatent.ai/blog/latent-vs-llm-judge</id>
    <updated>2026-10-04T00:00:00Z</updated>
    <author><name>Vedant Gaur</name></author>
    <summary>Judging every answer with a frontier model takes seconds and costs about a cent per answer. Latent reads a finished answer in milliseconds, and inside your own model it adds no measurable time at all.</summary>
  </entry>
  <entry>
    <title>Why internal state</title>
    <link href="https://runlatent.ai/blog/why-internal-state"/>
    <id>https://runlatent.ai/blog/why-internal-state</id>
    <updated>2026-09-29T00:00:00Z</updated>
    <author><name>Vedant Gaur</name></author>
    <summary>A model&#x27;s internal state reveals failures its output doesn&#x27;t. Four published results on activations, chains of thought and training runs, and what they mean for teams serving their own models.</summary>
  </entry>
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