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  <id>urn:uuid:2b50d339-88da-562d-80f3-4afc8c18b78f</id>
  <title>SocioLens — RO-LD01-05: Does the EU&apos;s training-compute criterion track how general a model is?</title>
  <subtitle>New versions of this research opportunity and changes to the claims it rests on.</subtitle>
  <link rel="self" type="application/atom+xml" href="https://sociolens-web.pages.dev/opportunities/ro-ld01-05/feed.xml"/>
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  <updated>2026-10-16T00:00:00Z</updated>
  <author><name>SocioLens</name></author>
  <entry>
    <id>urn:uuid:5c6eae96-5f73-55f7-ac70-b4e74422f255</id>
    <title type="text">New research opportunity: RO-LD01-05 — Does the EU&apos;s training-compute criterion track how general a model is?</title>
    <link rel="alternate" type="text/html" href="https://sociolens-web.pages.dev/opportunities/ro-ld01-05/v1/"/>
    <updated>2026-10-16T00:00:00Z</updated>
    <summary type="text">Across open-weight and documented models released from 2020 to 2026, how closely does training compute predict measured generality (the range of distinct tasks performed competently), and how many models fall on the wrong side of the 10²³ FLOP indicative criterion that the European Commission uses for a general-purpose AI model?</summary>
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