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        <identifier>oai:lifeofresearch.com:33</identifier>
        <datestamp>2026-09-22T17:34:54Z</datestamp>
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        <dc:title>Out-of-Distribution With Respect to What? Four Referents Behind One Predicate, and Why OOD Detection Results Do Not Transfer to Foundation-Model Reasoning</dc:title>
        <dc:creator>Pranay M. Mahendrakar</dc:creator>
        <dc:subject>out-of-distribution detection</dc:subject>
        <dc:subject>distribution shift</dc:subject>
        <dc:subject>foundation models</dc:subject>
        <dc:subject>membership inference</dc:subject>
        <dc:subject>construct validity</dc:subject>
        <dc:subject>chain-of-thought reasoning</dc:subject>
        <dc:subject>uncertainty estimation</dc:subject>
        <dc:subject>evaluation</dc:subject>
        <dc:description>(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0.
Out-of-distribution detection was formalised for a setting in which the training distribution is known, the label space is closed, and a designated test set stands in for everything outside it. Foundation models satisfy none of those conditions, and yet a growing body of work reports out-of-distribution detection for large language models using perplexity, hidden-state distance or learned probes, and a parallel body of work reports that chain-of-thought reasoning degrades sharply under distribution shift. This paper argues that &quot;out of distribution&quot; carries at least four distinct referents in that literature - membership in the pretraining corpus, novelty of the semantic class, distance from a designated reference dataset, and impending model error - and that results are routinely obtained against one referent and read as though they held for another. The separation is not a matter of taste. For the membership referent, the published record contains a direct negative result: membership inference and contamination detection on pretraining data perform at or near chance, and the evaluations that appeared to show otherwise were measuring distribution shift between the member and non-member samples rather than membership. That makes the referent most often invoked in informal discussion the one with no working operational test, which in turn means the claim that a reasoning failure occurred &quot;outside the training distribution&quot; is currently untestable in that sense for any model whose corpus is undisclosed. Nine studies that would settle the open parts are named, four of them runnable today on open-corpus model suites. No experiments are reported here, and the case against this paper&apos;s own position is stated in full.
The literature search, drafting and citation verification for this paper were carried out with AI assistance under the author&apos;s direction. Every citation was machine-verified against the arXiv API and Crossref before inclusion, and every quantitative claim was read back against the cited source&apos;s own text. The author is responsible for the final text and for all claims made in it.</dc:description>
        <dc:publisher>Life of Research</dc:publisher>
        <dc:date>2026-08-27</dc:date>
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        <dc:identifier>https://lifeofresearch.com/articles/33</dc:identifier>
        <dc:identifier>https://doi.org/10.5281/zenodo.22123547</dc:identifier>
        <dc:language>eng</dc:language>
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