## 🔬 Review status
This working paper has **not yet gone through the multi-referee review** of the UQO working-paper series (blinded referee reports, editorial decision, re-execution, full revision, independent verification). Its results should be read as preliminary. Only reviewed papers of the series are presented on [wp.spboucher.ai](https://wp.spboucher.ai) and [www.spboucher.ai](https://www.spboucher.ai/research).
---
# WP-AI-0002 — Human Text Reconstruction Transformer (HTR)
**Question.** Can a Transformer reconstruct characteristics of pre-2018 human-authored scientific prose from
LLM-rewritten versions of the same passages?
**Deliverables.** The project, part of UQO Research (series AI, id AI-0002), has two deliverables:
- a peer-review-ready paper, *Human Text Reconstruction: Learning the Inverse of LLM-Induced Transformations in
Scientific Writing*;
- a released model family (HTR-Tiny/Small/Base, plus Large if feasible) that can actually be used.
**Prior work.** Paraphrase inversion already exists (Rivera Soto et al., 2024, 2026). This project studies
something different:
- **Reconstruction fidelity** against identity and paraphrase baselines.
- **Rights-verified, pre-2018 scientific originals** with controlled Claude rewrite variants.
- **Factual integrity.**
- **Non-identifiability.**
- **Memorisation.**
See `docs/RESEARCH_GAP.md`.
## Status (2026-11-09)
- **Gate A: GO.** 100-paper PMC smoke test with 2 013 eligible passages; licence governance in place
(`docs/gates/GATE_A.md`).
- **Pilot corpus.** 1 180 PMC documents with 23 658 eligible passages (`data/parsed/pilot_pmc`). Book trials:
11 Gutenberg books and 17 NCBI Bookshelf chapters. arXiv and Crossref economics/finance adapters are in
progress.
- **Vertical slice.** Runs end to end with mock rewrites at 0 USD spend. M1-nano passes the one-batch overfit
test.
- **Not started:** real Claude rewrites (Gate B, ceiling 30 USD), trained models at release scale, and the paper
draft.
- **Full status:** `docs/IMPLEMENTATION_CHECKLIST.md`, `docs/architecture/RESEARCH_PLAN.md`.
## Quick start
```bash
python3 -m venv .venv && .venv/bin/pip install -e ".[api,hf,dev]"
.venv/bin/python -m pytest tests -q -m "not network" # unit tests, no network
scripts/vertical_slice.sh # corpus → mock rewrites → pairs → M1-nano → metrics (0 USD)
.venv/bin/python scripts/discover_pmc.py --candidates 400 --max-docs 100 --seed 1 --out data/raw/pmc_smoke
```
Using a trained checkpoint:
```python
from htr.inference.pipeline import HTRReconstructor
r = HTRReconstructor.from_pretrained("artifacts/runs//best").reconstruct(text, {"discipline": "economics"})
print(r.reconstruction, r.warnings)
```
**Spending money.** Real API calls require `--live` and respect the budget ceilings
(`docs/reproducibility/COMPUTE_BUDGET.md`). All commands are listed in
`docs/reproducibility/REPRODUCIBILITY.md`.
## Layout
```
configs/ rewriting (pilot.json, pricing.json); data/training/evaluation/release (to fill)
src/htr/ ingest · licenses · parse · segment · dedup · data · rewrite · validate · models · training ·
inference · evaluation · release · utils
scripts/ discover_{pmc,arxiv,books} · build_corpus · make_splits · rewrite · validate_pairs ·
train_tokenizer · train · predict · evaluate · vertical_slice.sh
tests/ unit · integration (network-marked) · regression
data/ manifests (committed) · raw · parsed · pairs · splits (not committed)
artifacts/ runs/checkpoints (not committed) · metrics · figures · tables · reports
docs/ MANDATE · RESEARCH_GAP · PREREGISTRATION · DECISION_LOG · RISK_REGISTER · IMPLEMENTATION_CHECKLIST ·
architecture/ · data_governance/ · experiments/ · reproducibility/ · gates/ · literature/
paper/ model_cards/ demo/
```
## Honest-claims notice
HTR outputs are **synthetic reconstructions**. They are a model's estimate of pre-2018-style scientific prose
given an LLM rewrite. They are not the original text, they do not restore authorship, and they are not evidence
that a text was written by a human. HTR is not an AI-text detector. Every result is reported against identity
and paraphrase baselines with confidence intervals, and null or negative results are reported.
## Licence
- **Code:** Apache-2.0 (`pyproject.toml`).
- **Data and derived text:** per-document licences. Only CC0, public-domain and CC BY items are redistributable
(`docs/data_governance/LICENSE_MATRIX.md`).
- **Model weights:** licence to be decided at release. It depends on the training licence set and on
Anthropic's terms for model outputs (open question, `LICENSE_MATRIX.md` §4).
- **Attribution:** NLM/PMC and all source authors are acknowledged.
## Citation
```bibtex
@techreport{boucher2026htr,
author = {Boucher, Simon-Pierre},
title = {Human Text Reconstruction: Learning the Inverse of {LLM}-Induced Transformations in Scientific Writing},
institution = {Universit{\'e} du Qu{\'e}bec en Outaouais},
type = {Working Paper},
number = {AI-0002},
year = {2026},
note = {In preparation}
}
```