# language: en license: apache-2.0 library_name: htr tags: [text2text-generation, scientific-writing, style-reconstruction, research] pipeline_tag: text2text-generation

# {name} (v{version})

Human Text Reconstruction Transformer — a research model that maps an LLM-rewritten scientific passage back towards the distribution of the pre-2018 human-authored original. Outputs are synthetic reconstructions: they are not the original text, and they are not evidence of human authorship or of AI use. Part of UQO working paper WP-AI-0002.

Field	Value
Architecture	`{arch}` (encoder–decoder Transformer; m3 = dual-latent content/style with copy gate)
Parameters	{n_params_m} M
d_model / layers (enc+dec)	{d_model} / {layers}
Vocabulary (SentencePiece unigram)	{vocab}
Max sequence length	{max_len} tokens
Style classes (m3)	{style_classes}
Training run	`{run}`
Exported	{exported_at}

# Intended use

Research on how LLM rewriting transforms scientific prose; stylometric analysis; benchmarking inverse-paraphrase models; a tool for authors who want a draft re-aligned with historical scientific register as a suggestion that must be checked. Not intended for AI-text detection, authorship attribution, academic-integrity decisions, or production of text presented as human-written.

# How to use

python

from htr.inference.pipeline import HTRReconstructor
rec = HTRReconstructor.from_pretrained("path/to/{name}")
r = rec.reconstruct("LLM-rewritten paragraph …", metadata={"discipline": "economics", "pub_year": 2012, "section_role": "results"})
print(r.reconstruction); print(r.warnings)

CLI: htr reconstruct --model path/to/{name} --text "…" --discipline economics --year 2012.

# Training data

{training_data}

# Evaluation (document-safe test split; identity baseline reported alongside)

json

{metrics_json}

Metrics: chrF++, normalised edit similarity, ROUGE-L, exact match, number recall, placeholder (citation/math/URL) preservation, stylometric Wasserstein distance to originals. Confidence intervals are document-clustered bootstrap.

# Limitations and risks

Reconstruction from a paraphrase is non-identifiable: many originals map to the same rewrite; the model returns one plausible estimate. Lexical overlap with the true original is bounded.
Factual integrity is enforced heuristically (protected spans, number checks); residual errors are flagged in warnings, not guaranteed absent. Always verify numbers, citations and negations.
Trained on Claude rewrites of CC-licensed biomedical, economics/finance, arXiv and public-domain book passages; behaviour on other rewriters, languages or genres is reported in the paper's generalisation experiments only.
Memorisation: training originals were deduplicated and the model is evaluated for nearest-neighbour overlap; see the paper. Do not use to recover specific copyrighted texts.

# Licence

Code and weights: Apache-2.0. Data licences: see LICENSE-MODEL.txt and the data card. Attribution to NLM/PMC, arXiv, publishers and Project Gutenberg sources is in the data card.

# Citation

Boucher, S.-P. (2026). Human Text Reconstruction: Learning the Inverse of LLM-Induced Transformations in Scientific Writing. UQO Working Paper AI-0002.

spb wpai0002_uqo