Eric Freeburg — Independent Research

E. M. Freeburg

Independent research by E. M. Freeburg. Research, Essays, and thought on questions that recur across culture, character, and public life. Free to read in full.

Episodes

  1. 2d ago

    The Missing Format

    Every corpus decision the field records is about which text to train on: domain, language, quality, recency, deduplication. None is about how the text's arrangement is notated. A Markdown heading is a two-token cue present at every discourse boundary and absent everywhere else; it never lies, and conversion pipelines now attach it to essentially everything long enough to have structure. Where the cue is present, a boundary does not have to be inferred. It can be read. The situation is measured three ways. A census of ten public pre-training corpora shows that the supply of long, coherent, low-boundary-density text has collapsed to a rounding error, and that the newest corpora are the most heavily marked. A converter study shows the markup is faithful — which makes the cue more trustworthy, not less, and closes the obvious escape route: after serialization no document has zero markup, so there is no null level left to reweight toward. And a paired-serialization probe on a small base model puts the framing under direct reading-side test. Swapping a boundary's notation while keeping its typographic form changes the model's use of long-range context by a measured zero — the operative cue is the announcement, not the sigil. Deleting the announcement itself makes the following prose measurably harder to predict, and the model recovers none of the loss from the long context that contains the evidence. The marker is informative, and the inference that should substitute for it is absent. What follows is deliberately conservative: format augmentation rather than format replacement — present the same structure in multiple notations so the sigil stops being a shortcut — and long-context slot substitution as the first serious experiment, because the long-context stage is the one place where the training sequence is the document, and it is currently fed the most heavily marked corpus anyone has built. 00:18 — Summary03:00 — Nothing left to infer06:53 — Nobody decided this10:05 — What the corpora contain13:49 — The cue that cannot be reweighted19:26 — The one stage where the sequence is the document24:30 — A first measurement32:07 — Format augmentation37:07 — The program42:31 — Coda: record it, then build it

    The Missing Format
  2. 5d ago

    Glaring Fireball

    A novel enters a modern training corpus through a scan, an OCR pass, and a linearizer. It comes out with headings the novelist never wrote, breaks where the thought did not break, and a chapter that ran forty pages cut into eleven sections. Every word survives. The domain label survives. What does not survive is the arrangement — and the arrangement is what the model trains on. The substitution happens upstream of every control the field exercises over training data. Mixture weights choose how often to sample the converted document; filters choose whether to keep it. Both take the corpus as given, and the corpus was given by an extractor. No reported quantity would change if a different extraction library had been chosen. This proposal sets out three studies to establish whether that matters, ordered so the cheapest can end the inquiry. The first needs no models and no training: run current converters over two document classes and count the structural elements in the output with no counterpart in the source. If converters turn out to be conservative on continuous prose, the concern dissolves. The second reads public checkpoints for a predicted divergence between benchmark performance and long-form likelihood. The third — the only expensive one — is a three-arm matched pre-training run separating faithful conversion from imposed hierarchy, which no existing experiment does. 00:21 — Summary03:40 — The gap05:42 — The hypothesis08:29 — Why the gap has persisted10:40 — What has to be distinguished12:37 — The program17:51 — What would follow, and what would not19:32 — Why now rather than later23:08 — Relation to existing work25:01 — What is already known about the model side26:53 — What this would produce

    Glaring Fireball
  3. Mar 27

    The Last Fingerprint: How Markdown Training Shapes LLM Prose

    The em dash is the most discussed tell of machine-written text, and no mechanistic account of it existed. Separately, everyone had noticed that language models default to markdown. Nobody had connected the two. The argument here is that they are one phenomenon: the em dash is markdown leaking into prose — the smallest surviving unit of a structural orientation acquired from markdown-saturated training data, and amplified afterwards by fine-tuning. The paper lays out that genealogy in five steps, from corpus composition through the dash's dual status as both punctuation and structural joint. Then it tests it. Twelve models from five providers were instructed to suppress markdown. Headers, bullets, and bold vanish; em dashes persist — except in Meta's Llama models, which produce none at all. Rates run from 0.0 per thousand words to 9.1 under active suppression. A three-condition gradient shows that even explicit prohibition of the dash fails to eliminate it in some models, and a base-versus-instruct comparison finds the tendency already present before RLHF. The conclusion reframes em dash frequency as a diagnostic of a particular fine-tuning procedure rather than a stylistic defect. 00:18 — Abstract02:27 — 1. Introduction07:22 — 2. Background and Related Work13:58 — 3. The Em Dash Genealogy24:30 — 4. The Two Discourses27:41 — 5. Empirical Evidence42:04 — 6. Discussion50:58 — 7. Conclusion

    The Last Fingerprint: How Markdown Training Shapes LLM Prose

About

Independent research by E. M. Freeburg. Research, Essays, and thought on questions that recur across culture, character, and public life. Free to read in full.