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Harald Steck

Machine-learning researcher, first author (with Chaitanya Ekanadham and Nathan Kallus) of "Is Cosine-Similarity of Embeddings Really About Similarity?" (WWW 2024 Companion, arXiv:2403.05440), which shows analytically that the cosine similarity between two learned embeddings from a regularized linear (matrix-factorization) model depends on free parameters of the training objective — "cosine-similarity can yield arbitrary and therefore meaningless 'similarities'" — rather than tracking a stable property of the underlying data.

Matters to this vault as the single foundational citation behind its entire "embedding false friend" diagnostic genre: claim-cosine-similarity-of-embeddings-can-be-arbitrary grounds the mechanism directly, and at least four observation notes diagnosing the vault's own retrieval-index false positives cite it either directly or by wikilink — observation-hawks-jeffress-cosine-pairing-is-embedding-false-friend, observation-gates-jevons-cosine-pairing-is-embedding-false-friend, observation-falcon-helmholtz-inference-embedding-false-friend, observation-ml-ad-en-gedi-cosine-pairing-is-embedding-false-friend — plus two more this promotion (observation-ml-ad-en-gedi-and-gates-jevons-false-friend-notes-are-a-confirmed-bridge, observation-gates-jevons-is-fourth-confirmed-embedding-false-friend-instance).

This hub is built belatedly (2026-08-16) despite the vault having leaned on Steck's result since 2026-07-12 — exactly the "recurring, uncaught foundational figure" shape the entity-candidates blind-spot warning describes; his name had never appeared in a capture's ## Entity candidates section before the 2026-08-16 capture that finally flagged it. Per sources.md's single-source-concentration cap, three claim-notes already rest directly on this paper as an unrefereed primary — see question-corroborate-steck-2024-cosine-arbitrariness-single-source-cap for the open question that gates further direct citation.

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