tests/phase11/reports/phase11_summary.md

Phase 11 Summary — Observability & Architecture Audit

Section 1 — Pipeline Funnel

Grammar Model Calls            270
Grammar Raw Output Changed      98  (36.3% of inputs)
Grammar Diffs Extracted        134
  → Passed All Filters          44  (32.8% of diffs)
  → Rejected by Filters         82  (61.2% of diffs)
  → Unaccounted                   8  (6.0% — skipped by grammar pattern / directional)
Patches Applied                 44
PatchSet Conflicts               0
Final Corrections (all stages) 127

67.2% of grammar diffs are rejected by filters.

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Section 2 — Loss Analysis

Filter Rejection Breakdown

Filter	Rejections	% of 82	Effect
PunctuationGuard	32	39.0%	Blocks grammar stripping periods from correct text
TanweenGuard	30	36.6%	Blocks grammar stripping tanween (ً/ٌ/ٍ)
LatinGuard	9	11.0%	Blocks changes to Latin-containing text
DigitGuard	5	6.1%	Blocks changes to digit-containing text
IVtoOOV	3	3.7%	Blocks valid→non-word changes
Jaccard_03	2	2.4%	Blocks character-dissimilar changes
StageLocker	1	1.2%	Blocks overlap with spelling-corrected ranges

Filter Precision

Filter	Total	Correct	Incorrect	Precision
PunctuationGuard	32	32	0	100%
TanweenGuard	30	30	0	100%
LatinGuard	9	9	0	100%
DigitGuard	5	5	0	100%
IVtoOOV	3	3	0	100%
Jaccard_03	2	2	0	100%
StageLocker	1	1	0	100%
TOTAL	82	82	0	100%

All 82 rejections were CORRECT. No valid corrections were blocked by filters. The grammar model's FN problem is NOT caused by over-filtering.

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Section 3 — Evidence-Based Findings

Finding 1: Grammar FN are NOT filter-caused

Of 17 grammar FN:

Root Cause	Count	%
PATCH_FAILURE	13	76%
FILTER_FAILURE	2	12%
MODEL_FAILURE	2	12%

But the 13 PATCH_FAILURE cases are actually correct — the pipeline IS fixing them. The benchmark comparison was using substring matching (expected_correction in pipeline_output) which fails when expected contains only the corrected word (e.g., يذهبون) instead of the full sentence.

True grammar FN: 4 (not 17)

ID	Root Cause	Detail
G003	MODEL	حضرون instead of حضروا (wrong suffix)
G006	FILTER (IVtoOOV)	لعبوَ rejected — model adds fatha diacritical
G009	MODEL	Model returned unchanged
G022	MODEL	Model returned unchanged

G028 was also FILTER (IVtoOOV) — model outputs يفعلوَ instead of يفعلوا.

Finding 2: StageLocker is NOT the bottleneck

StageLocker caused only 1 rejection out of 82 total (1.2%). It is functioning correctly and not over-locking.

Finding 3: PatchSet has ZERO conflicts

127 patches generated across all 270 samples, with 0 cross-stage conflicts. PatchSet conflict resolution is not a correction loss point.

Finding 4: OffsetMapper has a known edge case

Delete-boundary positions map to the START of the deleted range (off-by-one). This affects:

• Tanween removal: end-position of جدا maps to position 3, not 4 (losing the tanween's original position)
• First char after any deletion

Impact on grammar FN: NONE. The 4 real grammar FN are caused by model quality and IVtoOOV filter, not OffsetMapper.

Finding 5: The grammar model adds diacriticals to jazm corrections

G006: لعب → لعبوَ (fatha on waw) G028: يفعلون → يفعلوَ (fatha on waw)

The model produces the correct ROOT form but adds a diacritical that makes it fail the IVtoOOV vocabulary check. The diacritical should be stripped before the vocab check.

Finding 7: Spelling ORTHO_PAIRS blocks genuine keyboard typos

Real-world case: بالرفم→بالرغم (ف→غ, "despite")

The spelling model correctly detects the fix. But _is_small_spelling_change() rejects it because:

• Path 1 (IV-IV guard): Both words are valid Arabic → only known orthographic fixes allowed → ف↔غ NOT in ORTHO_PAIRS → REJECT
• Path 2 (OOV path): Character check at line 966 → ف↔غ NOT in ORTHO_PAIRS → REJECT

ف and غ are adjacent on the Arabic keyboard — this is a classic keyboard typo pattern.

Affected letter pairs NOT in ORTHO_PAIRS:

• ف↔غ (fa/ghayn — adjacent)
• ق↔ف (qaf/fa — adjacent)
• ع↔غ (ain/ghayn — related)
• ص↔ض (sad/dad — related)
• ث↔ت (tha/ta — related)
• ذ↔د (dhal/dal — related)
• ز↔ر (zay/ra — adjacent)
• ط↔ظ (ta/dha — related)
• س↔ش (sin/shin — related)
• ح↔خ (ha/kha — related)

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Section 4 — Phase 12 Recommendations (Evidence-Backed)

Priority 1: Fix benchmark comparison logic (FREE wins)

13 grammar tests are actually PASSING but marked FN due to substring comparison. Fix the benchmark runner to compare full sentences, not just correction words. This alone raises grammar score from 60% → ~89%.

Priority 2: Strip diacriticals before IVtoOOV check

G006 and G028 are blocked because لعبوَ / يفعلوَ have fatha diacriticals that make them OOV. Strip diacriticals from grammar corrections before the vocab check. Cost: 2 lines of code. Fixes: 2 FN.

Priority 3: Do NOT weaken PunctuationGuard or TanweenGuard

Both filters have 100% precision. The grammar model consistently:

• Strips periods from correct text (32 cases)
• Strips tanween from correct text (30 cases)

These filters are preventing real damage.

Priority 4: Do NOT redesign StageLocker

StageLocker caused 1 rejection in 270 samples. It is functioning correctly.

Priority 5: Do NOT redesign PatchSet

Zero conflicts in 270 samples. No architectural change needed.

Priority 6: Add keyboard-adjacent pairs to spelling ORTHO_PAIRS

The spelling filter blocks genuine keyboard typos (بالرفم→بالرغم) because ف↔غ is not in ORTHO_PAIRS. Two approaches:

Option A: Add keyboard-adjacent Arabic letter pairs to ORTHO_PAIRS (simple, ~10 pairs)

• Risk: may allow some unwanted corrections between similar-sounding letters
• Benefit: fixes the most common real-world typo pattern

Option B: Create a separate KEYBOARD_TYPO_PAIRS set with weaker confidence (0.5)

• Risk: more complex logic
• Benefit: allows keyboard typos but with dampened confidence for user review

Not Recommended

• Adding NER — entity FP (3 cases) are minor compared to grammar issues
• Replacing grammar model — the model IS producing correct corrections for most patterns
• Redesigning OffsetMapper — edge case documented but not causing failures