diff --git a/docs/adr/0001-run-scripts-are-provenance-artifacts.adoc b/docs/adr/0001-run-scripts-are-provenance-artifacts.adoc
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+= ADR-0001: Run scripts are provenance artifacts
+Status: Accepted (2026-08-29)
+
+== Context
+
+The windowed zero-skip Sadeed protocol (split at word boundaries, 2x
+window generation cap, haraqat projection) was pasted verbatim into
+`train_arabic_r5.py` through `train_arabic_r8.py`. `docs/RESULTS.md`
+cites those scripts as the record of what produced each published
+number. ml-models now owns one deep, tested copy of the protocol
+(`src/harness/sadeed.py`), vendored here as `sadeed_harness.py`.
+
+== Decision
+
+- Past run scripts (r5-r8, eval_*) are **never rewritten** to consume
+  the shared harness: they are provenance artifacts. Their inline
+  copies stand as historical record of what ran.
+- New run scripts (r9+) import `sadeed_harness` — no new inline copies
+  of the protocol.
+- Protocol changes land in `sadeed_harness.py` (kept in sync with
+  ml-models `src/harness/sadeed.py`) and are noted in the RESULTS
+  section of any future run.
+
+== Consequences
+
+- The four inline copies remain in the tree deliberately; architecture
+  reviews should not re-suggest rewriting them.
+- The harness carries its own unit tests in ml-models; a vendored copy
+  that drifts from the source is a bug — sync both or vendor via
+  submodule later.
diff --git a/sadeed_harness.py b/sadeed_harness.py
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+++ b/sadeed_harness.py
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+"""The Sadeed windowed harness — the campaign's central measurement
+protocol, in one place.
+
+Windowed zero-skip (the published protocol): inputs over the byte
+budget split at word boundaries; greedy decode with a 2x-window
+generation cap (diacritized output runs 1.4-1.6x input — a shorter cap
+silently truncates the hardest paragraphs, which the evaluator then
+skips: survivorship bias, not quality); window predictions stitched and
+haraqat projected onto the input letters so output structure always
+matches ground truth.
+
+Previously pasted verbatim into rababa train_arabic_r5/r6/r7/r8 and
+modal_distill.evaluate_der — five copies of a protocol every published
+Arabic number depends on. rababa scripts should vendor this single
+module instead of carrying inline copies.
+"""
+
+from __future__ import annotations
+
+import re
+
+DIACRITICS_RE = re.compile("[ؐ-ًؚ-ٰٟۖ-ۜ۟-۪ۨ-ۭ]")
+
+
+def split_windows(text: str, budget: int = 1400) -> list[str]:
+    """Split at word boundaries so no window exceeds the byte budget."""
+    if len(text.encode("utf-8")) <= budget:
+        return [text]
+    windows: list[str] = []
+    current: list[str] = []
+    n = 0
+    for word in text.split():
+        cost = len(word.encode("utf-8")) + 1
+        if current and n + cost > budget:
+            windows.append(" ".join(current))
+            current, n = [], 0
+        current.append(word)
+        n += cost
+    if current:
+        windows.append(" ".join(current))
+    return windows
+
+
+def project_haraqat(pred: str, text: str) -> str:
+    """Project predicted haraqat onto the input's letters, so the output
+    structure matches ground truth even when the prediction inserts or
+    drops letters (zero-skip contract)."""
+    from difflib import SequenceMatcher
+
+    pred_haraqat = [""]
+    for ch in pred:
+        if DIACRITICS_RE.match(ch):
+            pred_haraqat[-1] += ch
+        else:
+            pred_haraqat.append("")
+    pred_haraqat = pred_haraqat[1:]
+    pred_letters = [c for c in pred if not DIACRITICS_RE.match(c)]
+    text_letters = [c for c in text if not DIACRITICS_RE.match(c)]
+    sm = SequenceMatcher(None, text_letters, pred_letters, autojunk=False)
+    out: list[str] = []
+    for op, i1, i2, j1, _j2 in sm.get_opcodes():
+        if op == "equal":
+            for k in range(i2 - i1):
+                out.append(text_letters[i1 + k] + pred_haraqat[j1 + k])
+        else:
+            for k in range(i1, i2):
+                out.append(text_letters[k])
+    return "".join(out)
+
+
+def strip_diacritics(text: str) -> str:
+    return DIACRITICS_RE.sub("", text)
+
+
+def windowed_paragraphs(model, tokenizer, inputs, window: int = 1400,
+                        batch_size: int = 8, device: str = "cuda") -> list[str]:
+    """The full protocol over a paragraph list: split, greedy-decode each
+    window (2x-window cap) under bf16 autocast, stitch, project. Returns
+    haraqat-projected paragraphs ready for the Misraj evaluator."""
+    import torch
+
+    windows: list[str] = []
+    counts: list[int] = []
+    for text in inputs:
+        ws = split_windows(text, window)
+        counts.append(len(ws))
+        windows.extend(ws)
+
+    preds: list[str] = []
+    with torch.no_grad():
+        for i in range(0, len(windows), batch_size):
+            batch = windows[i : i + batch_size]
+            enc = tokenizer(
+                batch, return_tensors="pt", padding=True, truncation=True,
+                max_length=window,
+            ).to(device)
+            with torch.autocast("cuda", torch.bfloat16):
+                gen = model.generate(**enc, max_new_tokens=window * 2, num_beams=1)
+            preds.extend(tokenizer.batch_decode(gen, skip_special_tokens=True))
+
+    paragraphs: list[str] = []
+    k = 0
+    for text, c in zip(inputs, counts, strict=True):
+        paragraphs.append(project_haraqat(" ".join(preds[k : k + c]), text))
+        k += c
+    return paragraphs