Paper Polisher Pro v3

AI writing detection (AI-rate self-check for authors) · academic polishing guidance · terminology standardization · translation-smell check · quality report · AIGC compliance label check · paragraph-level attribution · journal precheck. 100% local, zero upload, zero credentials, pure standard library (optional onnxruntime enhancement layer).

⛔ Iron laws

1. Only reproducible numbers. Every metric comes from the held-out (test split) evaluation in eval/run_eval.py; unsupported claims like "100% detection rate / F1 98.3%" from older docs have been removed.
2. No verdict on short text. Texts under 100 characters get risk=unknown (community lesson: short-text false positives are uncontrollable).
3. Fingerprints attribute, never score. (Measured 2026-08-15: injecting fingerprints into the detector doubled human false positives.)
4. Calibration/evaluation separation. Spectrum, weights and thresholds are built on the calib half only; the test half is reserved for final evaluation (an in-sample AUROC of 0.9972 collapsed to a real 0.9187 once split).

Academic integrity

This tool is for authors self-reviewing and improving their own writing quality — clearer sentences, consistent terminology, natural style. It is not designed to evade institutional AI-detection systems, and it must not be used to misrepresent AI-generated work as human-written. Follow your institution's AI-use and disclosure policies; the bundled aigc_label_check.py exists to help you comply with disclosure and labeling rules (e.g., China's 2025-09 labeling measures) — to declare AI assistance properly, not to hide it. Every report carries an explicit integrity_notice to this effect.

Measured performance (C-ReD + DetectRL-ZH, held-out test half, n=5,251)

| Metric | v2.0 baseline | v3.0 rules+spectrum | v3.1 +supervised | v3.4 supervised + edit-regression v2 | |---|---|---|---|---| | AUROC (test half) | 0.7046 | 0.9187 | 0.9997 | 1.0 | | TPR@FPR5% | 30.4% | 49.0% | 99.95% | 100% | | TPR@FPR1% | 16.7% | 24.9% | 99.88% | 100% | | Human FPR @calibrated p99 | not measured | not measured | 3.56% (30/844) | 0.71% (6/844) | | Paraphrase/mixed-attack AUROC | 0.64 | 0.89 | 1.0 (in-corpus) | 1.0 | | Attack "AI-assisted" recall | — | — | 71.1% | 86.6% | | OOD plain-narrative/film recall | — | — | 1/6 | 5/6 supervised-only · 6/6 local fusion |

Which column applies to you? The base package runs the v3.0 rules+spectrum engine (0.9187 AUROC column, measured on the full held-out corpus; the bundled small-corpus regression measures 0.9022 — see eval/results/v350_release.json). The two right-hand columns require the optional local supervised model (see below). The engine tells you honestly which mode you are in: every report carries degraded_mode / degraded_notice when the supervised layer is absent or skipped.

Capability boundary matrix (read before trusting any detector)

| Scenario | Behavior | |---|---| | Chinese academic prose, full stack | Best case (AUROC 1.0 held-out, human FPR 0.71%) | | Base package without model | Rules+spectrum (0.9187); medical register over-scored (rules-only human FPR @medium: ~59% medical vs ~2% general) → trust only @high verdicts on medical text | | English text | Language gating skips the Chinese-trained supervised layer by design; rules-only English skeleton, advisory only | | Mixed human+AI documents (document-level) | AUROC 0.38 — a principled limitation of document-level averaging; use paragraph_report.py attribution instead | | Edit-extent regression head | ρ=0.540 — reported as metadata, never used in verdicts | | Colloquial / oral-register text | The style layer is calibrated on academic prose; treat style scores as advisory outside that register |

What's new in v3.6.0

• Academic-integrity guardrails: every report now carries an explicit integrity_notice field/line; new "Academic integrity" section; positioning stated plainly — author self-review and writing quality, compliance with disclosure rules, not detector evasion.
• Docs hardened for platform policy: evasion-flavored phrasing replaced with quality-framed language in the English documentation (detection and revision guidance stay; no detector-evasion framing). Chinese documentation keeps the SkillHub-approved wording.

What's new in v3.5.0

• Degraded-mode disclosure: ai_detector.py now reports degraded_mode + degraded_notice (JSON and text) whenever the supervised layer is absent, disabled (PP_NO_SUP=1), or skipped by language gating — including the medical-register over-score warning with the actual held-out numbers.
• Iron law #2 enforced: texts under 100 characters now return risk=unknown with an explicit no-verdict notice (previously documented but not implemented; short texts also show as "cannot judge" in quality_report.py instead of a misleading green).
• scripts/pp_doctor.py: one-command environment self-check — data integrity, script compilation, optional deps, model presence, supervised-layer loadability, short-text/long-text/determinism probes, deai_gate guard. Exit 0 = green.
• deai_gate.py usage guard + closed fallback loop: --help / missing file no longer run the gate on a bogus filename; layer timeouts are caught (neutral 50); a failed smell layer now falls back to a neutral 50 instead of 0, and a failed terminology layer no longer dumps tracebacks into notes.
• Chinese-Windows encoding hardening: every entry point forces UTF-8 stdout/stderr and tolerates non-UTF-8 (e.g. GBK) input files — no more crashes on default zh-CN consoles (found by adversarial multi-expert testing).
• Docs rebuilt in honest dual-language form (this file + SKILL_ZH.md); trigger words expanded (AI率 / 查AI率 / AIGC 检测 …).

Architecture (v3)

ai_detector.py            Main engine: 8 rule layers (125 recalibrated patterns, markdown caps,
                          EN openers, paragraph-level language) + length-routed fusion
 + layers_surface.py      L9 surface stats L10 token-spectrum (9,955-token delta spectrum)
                          L11 chain-of-thought features
 + fusion_config.json     Weights & thresholds (calib-half grid search + human p95/p99)
 + model_fingerprints.json v4 fingerprint registry (11 families incl. GLM-5.3 self-sampled; attribution only)
 + layers_lm.py           Optional supervised layer (local ONNX + pure-Python Qwen tokenizer;
                          PP_NO_SUP=1 falls back to rules)
paragraph_report.py       Paragraph-level attribution HTML (pattern×spectrum 50/50 fusion)
aigc_label_check.py       AIGC compliance labels (China labeling rules 2025-09: metadata/C2PA/explicit)
fingerprint_miner.py      Fingerprint mining (new model drop → sample → mine → register)
pattern_recalibrator.py   Data-driven pattern recalibration (human-hit filtering)
build_spectrum.py / calibrate_v3.py   Spectrum build / weight calibration
freshness_cron.py         Monthly freshness pipeline (sample → rebuild → calibrate → regression)
pp_doctor.py              Environment self-check (v3.5)
eval/                     corpus_builder / attack_gen / run_eval (AUROC, TPR@FPR, per-model, attack decay)

Quick start

# AI writing detection (probability + layered evidence + fingerprint attribution)
python scripts/ai_detector.py draft.txt --format json
# Journal precheck (suspected-AIGC ratio vs the 20-25% reference line, non-interchangeable disclaimer)
python scripts/ai_detector.py draft.txt --profile journal
# Paragraph-level attribution (locate human/AI collaboration)
python scripts/paragraph_report.py draft.txt --output report.html
# AIGC compliance label check (docx/pdf/png/txt)
python scripts/aigc_label_check.py manuscript.docx figures/*.png
# Terminology / translation smell / 4-layer gate (same as v2)
python scripts/term_check.py draft.txt --auto-fix
python scripts/translation_smell_check.py draft.txt
python scripts/deai_gate.py draft.txt
# Environment self-check
python scripts/pp_doctor.py
# Held-out regression (mandatory after any engine change)
python eval/run_eval.py --split test --tag mytag

Optional supervised layer (recommended, v3.2+)

pip install onnxruntime regex          # the two optional dependencies
# Place the two model files exactly as shipped by the authors:
#   ~/.cache/paper-polisher/qwen3-detector/model.int8.onnx
#   ~/.cache/paper-polisher/qwen3-detector/tokenizer.json
python scripts/layers_lm.py            # self-test: supervised_available: true
# ai_detector.py fuses automatically afterwards (0.9*supervised + 0.1*rules);
# PP_NO_SUP=1 temporarily falls back to rules-only.
# ⚠️ Do not substitute other exports or quantizations — measured probability drift; use exactly these files.

Trigger words (Chinese)

润色论文 查AI率 论文AI率 AIGC检测 AIGC率 GPT检测 查AI写作 论文润色 改写论文 AI论文检测 学术写作助手 AI写作检测 毕业论文润色 学位论文降重 SCI论文编辑 手稿润色 AI写作评分 AI改写检测 文风对标顶刊 这篇文章像不像AI

Related skills (Paper Toolbox family)

• cn-med-oa — free Chinese medical literature (OA) download & citation metadata
• pubmed-verifier — verify PMID/DOI references before submission
• cite-holmes — deep research with machine-verified citations
• academic-figures — publication-ready scientific figures in one command
• doc-holmes — layout-preserving PDF translation

Writing a paper? The family covers the full loop: literature → verified citations → de-AI polishing → figures.

Fingerprint freshness (against "detectors lag one generation")

On a new-model release day: python scripts/fingerprint_miner.py --corpus <new_samples.jsonl> --model <family> --apply Monthly full pass: python scripts/freshness_cron.py (crontab 0 3 1 * *). Compare adjacent eval/results/freshness_*.json; investigate if AUROC drops by more than 3 percentage points.

Version history (condensed)

• v3.5.0 (2026-09-20) — degraded-mode disclosure (engine mode + medical-register warning with held-out numbers); iron law #2 enforced (<100 chars → risk=unknown, quality_report shows "cannot judge" instead of misleading green); pp_doctor.py self-check; deai_gate.py usage guard; honest dual-language docs rebuild.
• v3.4.3 — markdown table-separator rows filtered from paragraph scoring (6/8 flagged rows in real MD manuscripts were false positives).
• v3.4.2 — fixed CJK double-count in language detection (Chinese journal PDFs misrouted to EN rules); degenerate PDF hard-line-break paragraph rebuilding (747→19 segments); paragraph-level language routing dead code fixed.
• v3.4.1 — language gating: English text skips the Chinese-trained supervised layer (measured EN OOD p_ai=0.9996 → EN false positives 91.7→17.2). Rule-editor experiment: negative result, honestly abandoned.
• v3.4.0 — edit-extent regression v2 (1,620 pairs, token-level distance, two-stage training): human FPR@p99 1.66%→0.71%; attack "AI-assisted" recall 86.6%.
• v3.2/v3.3 — supervised layer v3.2 (4-dim head, local ONNX fp16, pure-Python Qwen tokenizer); OOD blind spots honestly recorded then closed (film-register recall 1/6→5/6, GLM-5.3 probe 9/9).
• v3.1 — Qwen3-0.6B LoRA supervised layer (AUROC 0.9997 held-out).
• v3.0 — eval-driven rebuild: recalibrated pattern library (693→125 patterns, 568 dead/inverted signals removed), token-spectrum layer, length-routed fusion, calib/test leak-proof split, fingerprint registry v4, paragraph attribution, AIGC label check, journal precheck, freshness pipeline, honest docs. AUROC 0.7046→0.9187.
• v2.0.x — 9-layer rule engine + terminology library (baseline column above; non-reproducible claims removed).