The Announcement Carries the Cue: Markup, Boundaries, and the Notation of Pre-Training Corpora
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Computer Science > Computation and Language
arXiv:2608.09093 (cs)
[Submitted on 10 Aug 2026]
Title:The Announcement Carries the Cue: Markup, Boundaries, and the Notation of Pre-Training Corpora
Authors:E. M. Freeburg
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Abstract:How a document's arrangement is written down, its notation, is a training variable that no dataset card records. The field has established that text-extraction choices change model behaviour, and has never once measured the notation of what those choices put into the corpus. We define clean-window survival, a deterministic count of how much of a stream still demands the boundary inference, and measure notation on three fronts. What corpora carry: a census of thirteen public corpora, where survival falls to 0.153 in a vision-converted PDF slice against 0.889 in C4; the scarce resource is not unmarked text but long unmarked text; a pre-registered supply test finds what remains institutional, not consumer. Our own pre-registered prediction failed: converters do not fabricate structure on prose, and that null forced the reliability mechanism that survives it. What readers use: across five base models spanning 0.6B to 8.2B and two pipelines, deleting a structural announcement makes the following prose measurably harder to predict, while swapping its notation moves nothing. That zero does not make notation unimportant; it relocates the variable: the operative cue is the announcement, not the sigil. What writers impose: a bounded null. Base models do not impose the marked register above the authored baseline, and handed prose with every announcement deleted they do not put one back, at a rate indistinguishable from zero against an authored reference of zero. We ship the format those measurements imply: the pure frame, paragraphs in authored order, every announcement deleted into a reversible sidecar, mixed against the marked copy over announcement presence rather than notation. Choose format operators by the capability they train, not by the fidelity they preserve, and record extractor identity and survival on data cards.
| Comments: | 44 pages, 10 tables, 7 figures. Pre-registered protocols and their amendment history ship with the repository. Code, data, and instruments: this https URL |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.09093 [cs.CL] |
| (or arXiv:2608.09093v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.09093
arXiv-issued DOI via DataCite (pending registration)
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