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Kazuki Nakayashiki

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#natural language process... Preprint Oct 2026

More Reader Data or a Better Teacher? Updating a Model of Crowd Highlights in Late 2026

A model trained on readers'highlights can show a newcomer where earlier readers converged on a page, even on pages with few readers. A year of stronger language models suggests two upgrades: use them directly, or use them as teachers. We compare both with a third, more reader data, on one social highlighting platform,...

Kazuki Nakayashiki, Keisuke Watanabe · 0 citations
#artificial intelligence Preprint Sep 2026

Plan Pointers and Record-Directive Form in Budgeted Verification of Inherited Agent Memory

A model that inherits one-line memories may pull one archived source record before acting; a directive in the store can steer that pull: a pointer, a criterion or both. Across sixteen registered studies (179,352 attempts) we measured where the request goes under each form; every result is descriptive, with registered i...

Kazuki Nakayashiki · 0 citations
Jul 2026

Language Models Agree With Each Other, Not With Readers

Convergence is measured against a human reference nobody built for the purpose -- 2,523 reader mark sets across 120 web documents, produced by people highlighting for their own reasons on a platform where the overlay of others'marks is off by default.

Kazuki Nakayashiki, Keisuke Watanabe · 1 citation

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