fix: add contextual sense integrity and central ruling frames

Implemented HUDA-Net v38.0.6 contextual sense and central ruling architecture.

Changes:
- Added one-to-one contextual sense alignment for ambiguous surface terms.
- Prevented concepts distributed across unrelated fields from forming an exact match.
- Added domain-neutral OCR and text-integrity validation.
- Added evidence scope centrality to separate core rulings from side cases.
- Added central ruling frames for obligation, permission, validity, sufficiency, remedy, and exceptions.
- Added grounded multi-source ruling synthesis with professional formatting.
- Added contribution-based exact, related, and distant evidence tiers.
- Prevented broken or wrong-sense text from entering the final answer.
- Added lightweight startup regressions before the full runtime download.
- Preserved fully generic behavior with no topic-specific questions, answers, or entity dictionaries.

DEPLOY_HUDANET_V38_0_6.md

@@ -0,0 +1,21 @@
+# Deploy HUDA-Net v38.0.6
+
+1. Extract `hudanet_v38_0_6_contextual_sense_ruling_frame.zip`.
+2. Upload every extracted file and preserve the `hudanet_core/` directory structure.
+3. Replace the existing files in the Space root.
+4. Commit the changes.
+5. Run a **Factory reboot**.
+
+The lightweight preflight must print this before the large runtime download:
+
+```text
+✅ HUDA-Net v38.0.6 lightweight proposition, contextual-sense, text-integrity, central-ruling-frame, hierarchical-intent, relation-graph, issue-clustering, semantic-alignment, and compare-mode preflight passed before runtime download
+```
+
+After the datasets load, the generic self-test must also pass.
+
+Suggested commit title:
+
+```text
+fix: add contextual sense integrity and central ruling frames
+```

app.py

@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 """
-HUDA-Net Query-Conditioned Proposition Graph v38.0.5 Academic Integrated — Gradio Stable
+HUDA-Net Query-Conditioned Proposition Graph v38.0.6 Academic Integrated — Gradio Stable
 =============================
 Deploy this file as app.py in a Hugging Face Space and add HF_TOKEN as a
 read-only Space secret.
@@ -257,7 +257,7 @@ def _generic_proposition_preflight() -> None:
     )
     if not unrelated_rejected or not required_roles.issubset(roles):
         raise RuntimeError(
-            "HUDA-Net v38.0.5 preflight failed before runtime download: "
+            "HUDA-Net v38.0.6 preflight failed before runtime download: "
             + json.dumps(
                 {
                     "unrelated_rejected": unrelated_rejected,
@@ -310,7 +310,7 @@ def _generic_proposition_preflight() -> None:
     )
     if not relevant_passed or not distractor_rejected or not residue_pruned:
         raise RuntimeError(
-            "HUDA-Net v38.0.5 semantic-alignment preflight failed before runtime download: "
+            "HUDA-Net v38.0.6 semantic-alignment preflight failed before runtime download: "
             + json.dumps(
                 {
                     "relevant_passed": relevant_passed,
@@ -384,7 +384,7 @@ def _generic_proposition_preflight() -> None:
     )
     if not principle_ok:
         raise RuntimeError(
-            "HUDA-Net v38.0.5 hierarchical principle preflight failed before runtime download: "
+            "HUDA-Net v38.0.6 hierarchical principle preflight failed before runtime download: "
             + json.dumps(
                 {
                     "request_type": principle_result.query.primary_request_type,
@@ -397,6 +397,89 @@ def _generic_proposition_preflight() -> None:
             )
         )
 
+    # Validate contextual sense separation, OCR integrity, and central ruling
+    # frames with domain-neutral synthetic evidence before the runtime download.
+    sense_query = "ما هو حكم تنفيذ العامل للعملية؟"
+    sense_sources = [
+        {
+            "record_id":"preflight-sense-main", "book_id":"sense-main",
+            "book":"كتاب الحكم المركزي", "title":"حكم تنفيذ العامل للعملية",
+            "question":sense_query, "ruling":"لا يجب / يصح",
+            "answer":"لا يجب تنفيذ العامل للعملية، لكنه يصح منه إذا فعله.",
+            "source_kind":"clean certified source", "direct_probability":0.94,
+            "score":0.94, "dense_score":0.93, "cross_encoder_score":0.82,
+            "bm25_score":0.91, "retriever_agreement":5,
+        },
+        {
+            "record_id":"preflight-sense-supplement", "book_id":"sense-supplement",
+            "book":"كتاب الحكم المكمل", "title":"تنفيذ العامل والأهلية",
+            "question":"هل يجزئ تنفيذ العامل قبل اكتمال الأهلية؟",
+            "ruling":"صحيح غير مجزئ",
+            "answer":"يصح التنفيذ، لكنه لا يجزئ عن الالتزام الأصلي. فإذا اكتملت الأهلية وجب التنفيذ.",
+            "source_kind":"clean certified source", "direct_probability":0.90,
+            "score":0.90, "dense_score":0.92, "cross_encoder_score":0.80,
+            "bm25_score":0.88, "retriever_agreement":5,
+        },
+        {
+            "record_id":"preflight-sense-homograph", "book_id":"sense-homograph",
+            "book":"مصدر اللفظ المشترك", "title":"قبول العملية وآثارها",
+            "question":"ما علامة الانتفاع بالعملية بعد الرجوع؟", "ruling":"مقصد عام",
+            "answer":"أن يرجع العامل أصلح حالا وأكثر التزاما.",
+            "source_kind":"clean certified source", "direct_probability":0.97,
+            "score":0.97, "dense_score":0.95, "cross_encoder_score":0.79,
+            "bm25_score":0.76, "retriever_agreement":4,
+        },
+        {
+            "record_id":"preflight-sense-ocr", "book_id":"sense-ocr",
+            "book":"مصدر النص المشوه", "title":"حكم تنفيذ العامل للعملية",
+            "question":sense_query, "ruling":"تفصيل",
+            "answer":"خلاصة السجل: لاء إلا أن يفيق مساكل أفصئ دأؤود.",
+            "source_kind":"raw OCR source", "direct_probability":0.91,
+            "score":0.91, "dense_score":0.92, "cross_encoder_score":0.74,
+            "bm25_score":0.80, "retriever_agreement":4,
+        },
+    ]
+    sense_result = pipeline.resolve(sense_query, sense_sources, "ar")
+    sense_main_ok = any(
+        item.evidence.record_id == "preflight-sense-main" and item.accepted
+        for item in sense_result.ranked
+    )
+    homograph_rejected = any(
+        item.evidence.record_id == "preflight-sense-homograph"
+        and not item.accepted
+        and "contextual_sense_mismatch" in item.hard_rejections
+        for item in sense_result.ranked
+    )
+    ocr_rejected = any(
+        item.evidence.record_id == "preflight-sense-ocr"
+        and not item.accepted
+        and "evidence_text_integrity_failed" in item.hard_rejections
+        for item in sense_result.ranked
+    )
+    ruling_frame_ok = (
+        sense_result.query.primary_request_type == "ruling"
+        and "**الحكم المختصر:**" in sense_result.answer
+        and "لا يجب تنفيذ العامل للعملية" in sense_result.answer
+        and "لا يجزئ عن الالتزام الأصلي" in sense_result.answer
+        and "أصلح حالا" not in sense_result.answer
+        and "أفصئ دأؤود" not in sense_result.answer
+    )
+    if not sense_main_ok or not homograph_rejected or not ocr_rejected or not ruling_frame_ok:
+        raise RuntimeError(
+            "HUDA-Net v38.0.6 contextual-sense, integrity, and ruling-frame preflight failed before runtime download: "
+            + json.dumps(
+                {
+                    "sense_main_ok": sense_main_ok,
+                    "homograph_rejected": homograph_rejected,
+                    "ocr_rejected": ocr_rejected,
+                    "ruling_frame_ok": ruling_frame_ok,
+                    "answer": sense_result.answer,
+                    "details": sense_result.details,
+                },
+                ensure_ascii=False,
+            )
+        )
+
     # Validate the compare-sources contract with coherent generic evidence before
     # downloading the multi-gigabyte runtime. This catches audit-fixture regressions
     # immediately and verifies real synthesis rather than bypassing the gate.
@@ -431,7 +514,7 @@ def _generic_proposition_preflight() -> None:
     primary_ok = ("Book 1" in primary_only.answer) ^ ("Book 2" in primary_only.answer)
     if not compare_ok or not primary_ok or compared.answer == primary_only.answer:
         raise RuntimeError(
-            "HUDA-Net v38.0.5 compare-mode preflight failed before runtime download: "
+            "HUDA-Net v38.0.6 compare-mode preflight failed before runtime download: "
             + json.dumps(
                 {
                     "compare_ok": compare_ok,
@@ -443,7 +526,7 @@ def _generic_proposition_preflight() -> None:
             )
         )
     print(
-        "✅ HUDA-Net v38.0.5 lightweight proposition, hierarchical-intent, relation-graph, issue-clustering, semantic-alignment, and compare-mode preflight "
+        "✅ HUDA-Net v38.0.6 lightweight proposition, contextual-sense, text-integrity, central-ruling-frame, hierarchical-intent, relation-graph, issue-clustering, semantic-alignment, and compare-mode preflight "
         "passed before runtime download"
     )
 
@@ -460,7 +543,7 @@ def _zerogpu_registration_only():
 
 _download_hudanet_private_datasets()
 
-VERSION = "38.0.5"
+VERSION = "38.0.6"
 CONFIG = {
     "INPUT_ROOT": "/kaggle/input",
     "WORK_ROOT": "/tmp/hudanet_v27",
@@ -3250,7 +3333,7 @@ import pandas as pd
 from scipy import sparse
 import joblib
 
-UI_VERSION = "38.0.5"
+UI_VERSION = "38.0.6"
 UI_CONFIG = {
     "INPUT_ROOT": "/kaggle/input",
     "RUNTIME_DATASET_SLUG": "hudanet-bilingual-certified-runtime",
@@ -5756,10 +5839,25 @@ def validate_answer_quality_v36_4() -> dict:
     add("principle_and_consequence_rendered","**القاعدة:**" in principle.answer and "**وعند مخالفة القاعدة:**" in principle.answer,principle.answer)
     add("answerability_used_sources",len(principle.details.get("used_record_ids",[]))>=2,principle.details)
 
+    sense_query="ما هو حكم تنفيذ العامل للعملية؟"
+    sense_sources=[
+        {"record_id":"sense-main","book_id":"sm","book":"كتاب الحكم المركزي","title":"حكم تنفيذ العامل للعملية","question":sense_query,"ruling":"لا يجب / يصح","answer":"لا يجب تنفيذ العامل للعملية، لكنه يصح منه إذا فعله.","source_kind":"clean certified source","direct_probability":0.94,"score":0.94,"dense_score":0.93,"cross_encoder_score":0.82,"bm25_score":0.91,"retriever_agreement":5},
+        {"record_id":"sense-supplement","book_id":"ss","book":"كتاب الحكم المكمل","title":"تنفيذ العامل والأهلية","question":"هل يجزئ تنفيذ العامل قبل اكتمال الأهلية؟","ruling":"صحيح غير مجزئ","answer":"يصح التنفيذ، لكنه لا يجزئ عن الالتزام الأصلي. فإذا اكتملت الأهلية وجب التنفيذ.","source_kind":"clean certified source","direct_probability":0.90,"score":0.90,"dense_score":0.92,"cross_encoder_score":0.80,"bm25_score":0.88,"retriever_agreement":5},
+        {"record_id":"sense-homograph","book_id":"sh","book":"مصدر اللفظ المشترك","title":"قبول العملية وآثارها","question":"ما علامة الانتفاع بالعملية بعد الرجوع؟","ruling":"مقصد عام","answer":"أن يرجع العامل أصلح حالا وأكثر التزاما.","source_kind":"clean certified source","direct_probability":0.97,"score":0.97,"dense_score":0.95,"cross_encoder_score":0.79,"bm25_score":0.76,"retriever_agreement":4},
+        {"record_id":"sense-ocr","book_id":"so","book":"مصدر النص المشوه","title":"حكم تنفيذ العامل للعملية","question":sense_query,"ruling":"تفصيل","answer":"خلاصة السجل: لاء إلا أن يفيق مساكل أفصئ دأؤود.","source_kind":"raw OCR source","direct_probability":0.91,"score":0.91,"dense_score":0.92,"cross_encoder_score":0.74,"bm25_score":0.80,"retriever_agreement":4},
+    ]
+    sense=pipeline.resolve(sense_query,sense_sources,"ar")
+    add("specific_ruling_intent",sense.query.primary_request_type=="ruling",sense.details)
+    add("contextual_homograph_rejected",any(x.evidence.record_id=="sense-homograph" and not x.accepted and "contextual_sense_mismatch" in x.hard_rejections for x in sense.ranked),sense.details)
+    add("ocr_integrity_rejected",any(x.evidence.record_id=="sense-ocr" and not x.accepted and "evidence_text_integrity_failed" in x.hard_rejections for x in sense.ranked),sense.details)
+    add("central_ruling_frame_rendered","**الحكم المختصر:**" in sense.answer and "لا يجب تنفيذ العامل للعملية" in sense.answer,sense.answer)
+    add("complementary_ruling_facet_retained","لا يجزئ عن الالتزام الأصلي" in sense.answer,sense.answer)
+    add("broken_or_wrong_sense_text_not_rendered","أصلح حالا" not in sense.answer and "أفصئ دأؤود" not in sense.answer,sense.answer)
+
     failed=[item for item in checks if not item["passed"]]
     if failed:
-        raise RuntimeError("HUDA-Net v38.0.5 generic proposition self-test failed: "+json.dumps(failed,ensure_ascii=False))
-    print(f"✅ HUDA-Net v38.0.5 generic proposition self-test passed: {len(checks)} checks")
+        raise RuntimeError("HUDA-Net v38.0.6 generic proposition self-test failed: "+json.dumps(failed,ensure_ascii=False))
+    print(f"✅ HUDA-Net v38.0.6 generic proposition self-test passed: {len(checks)} checks")
     return {"passed":True,"tested":len(checks),"checks":checks}
 
 def validate_specificity_guard_v33(engine: ProfessionalEvidenceEngine) -> dict:

hudanet_core/README.md

@@ -1,45 +1,27 @@
-# HUDA-Net Hierarchical Intent and Relation-Aware Issue Graph v38.0.5
+# HUDA-Net Generic Evidence Core v38.0.6
 
-This library builds answers from the current user question and grounded source records. It contains no topic-specific entity dictionary, named question scenario, or stored fiqh answer.
+The core is domain-neutral. It does not contain stored jurisprudential questions, answers, entity aliases, or topic dictionaries.
 
-## Pipeline
+## Resolution pipeline
 
-1. `query.py` creates an initial multi-label interpretation of the current question.
-2. `relation_graph.py` extracts directed semantic relations such as acting on behalf of another party, receiving something from another party, sequence, responsibility, and prerequisite order.
-3. `evidence.py` converts each retrieved record into a normalized evidence frame, including answer type, outcome, relation graph, directness, and source quality.
-4. `compatibility.py` performs first-pass logical scoring using request type, semantic-mass coverage, relation direction, polarity, source quality, answerability, and retrieval support.
-5. `intent_repair.py` lets strong, mutually consistent evidence repair a weak initial request-type decision without using named scenarios.
-6. The evidence is re-ranked against the repaired query interpretation.
-7. `issue_clustering.py` separates nearby sub-issues by their directed relations, residual concepts, outcomes, and answer structure.
-8. `answerability.py` checks whether each candidate can contribute a grounded proposition to the requested answer.
-9. `consensus.py` selects the dominant issue cluster, combines compatible dimensions, and separates genuine conflicts.
-10. `propositions.py` extracts atomic propositions, preserves source order, validates grounding, and clusters paraphrases.
-11. `grounding.py` verifies that every displayed statement is traceable to accepted source fields.
-12. `synthesis.py` plans the final answer from the selected issue cluster and its supported propositions.
-13. `rendering.py` formats definitions, principles, conditions, rulings, consequences, comparisons, and conflicts professionally with source attribution.
-14. `pipeline.py` assigns final exact, related, and distant tiers only after answer contribution and answerability are known.
+1. Current-turn query normalization and multi-label request interpretation.
+2. Evidence-conditioned query focus induction.
+3. Directed relation-graph extraction.
+4. Contextual-sense alignment using one-to-one local concept co-occurrence.
+5. Text-integrity scoring for OCR and fragmented source text.
+6. Logic-first compatibility gates and multi-signal semantic rescue.
+7. Evidence-conditioned intent repair.
+8. Relation-aware sub-issue clustering.
+9. Atomic proposition extraction with grounding verification.
+10. Central ruling-frame planning across obligation, permission, validity, sufficiency, remedy, and recommendation dimensions.
+11. Consensus/conflict analysis and professional source attribution.
+12. Contribution-based exact, related, and distant evidence tiers.
 
-## Design guarantees
+## Safety properties
 
-- Conversation history is display-only and never enters retrieval.
-- No domain-specific topic aliases or question-specific answers are stored in the core library.
-- Neural and lexical retrievers nominate candidates; logical and relational compatibility decides whether they may build the answer.
-- Directed relations distinguish, for example, acting for another party from receiving funding from another party even when surface words overlap.
-- Weak initial intent classifications can be repaired by coherent evidence, but only through generic multi-signal voting.
-- The final answer is built from one dominant issue cluster rather than mixing every nearby sub-issue.
-- A record cannot appear in the exact tier unless it is answerable and contributed to the grounded answer.
-- A high confidence value cannot accompany an abstention or an ungrounded answer.
-- Every displayed proposition records its source, page, and contributing record IDs.
-- If clean compatible evidence is insufficient, the system abstains instead of dumping fragments.
-
-## v38.0.5
-
-- Added hierarchical multi-label request-type interpretation.
-- Added evidence-conditioned intent repair.
-- Added directed semantic relation graphs.
-- Added relation-aware compatibility and hard rejection for reversed or unrelated roles.
-- Added sub-issue clustering and dominant-cluster selection.
-- Added principle/rule answer planning with rule, consequence, support, and sources.
-- Added answerability-aware exact/related/distant tier assignment.
-- Added confidence consistency checks for abstentions.
-- Added regressions for generic principle questions, side-issue separation, and relation-direction errors.
+- A single token cannot satisfy two different query concepts in the contextual-sense gate.
+- Concepts dispersed across unrelated source fields cannot create an exact match.
+- Broken OCR text cannot enter the final answer merely because its source ranked highly.
+- Exact evidence must contribute a grounded proposition to the rendered answer.
+- Conversation history is display-only and is excluded from retrieval.
+- Dense similarity alone cannot override type, relation, sense, integrity, or grounding gates.

hudanet_core/centrality.py

@@ -0,0 +1,60 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Mapping
+
+from .contextual_sense import SenseAlignment
+from .text import TextProcessor
+from .types import EvidenceFrame, QueryFrame
+
+
+@dataclass(frozen=True)
+class CentralityResult:
+    score: float
+    question_alignment: float
+    title_alignment: float
+    extra_scope_penalty: float
+
+
+class ScopeCentralityEvaluator:
+    """Estimate whether evidence answers the central question or a side case."""
+
+    def __init__(self, text: TextProcessor):
+        self.text = text
+
+    def evaluate(
+        self,
+        query: QueryFrame,
+        evidence: EvidenceFrame,
+        source: Mapping[str, object],
+        sense: SenseAlignment,
+        *,
+        type_match: float,
+        directness: float,
+    ) -> CentralityResult:
+        linked_question = str(source.get("question", "") or "")
+        title = str(source.get("title", "") or "")
+        question_alignment = self.text.sentence_similarity(query.raw, linked_question, query.language) if linked_question else 0.0
+        title_alignment = self.text.sentence_similarity(query.raw, title, query.language) if title else 0.0
+
+        scope_terms = self.text.content_terms(f"{title} {linked_question}", query.language)
+        query_terms = set(query.subject_terms) | set(query.operator_terms)
+        residual = [term for term in scope_terms if term not in query_terms]
+        residual_density = len(set(residual)) / max(1, len(set(scope_terms)))
+        relation_narrowing = min(1.0, len(evidence.relation_edges) / 4.0)
+        extra_scope_penalty = min(1.0, 0.72 * residual_density + 0.28 * relation_narrowing)
+
+        score = (
+            0.28 * max(question_alignment, title_alignment)
+            + 0.22 * sense.score
+            + 0.18 * sense.joint_scope_coverage
+            + 0.17 * type_match
+            + 0.15 * directness
+            - 0.18 * extra_scope_penalty
+        )
+        return CentralityResult(
+            score=max(0.0, min(1.0, score)),
+            question_alignment=max(0.0, min(1.0, question_alignment)),
+            title_alignment=max(0.0, min(1.0, title_alignment)),
+            extra_scope_penalty=max(0.0, min(1.0, extra_scope_penalty)),
+        )

hudanet_core/compatibility.py

@@ -3,6 +3,8 @@ from __future__ import annotations
 from typing import Dict, List, Mapping
 
 from .relation_graph import RelationGraphAnalyzer
+from .contextual_sense import ContextualSenseAligner
+from .centrality import ScopeCentralityEvaluator
 from .text import TextProcessor
 from .types import EvidenceFrame, QueryFrame, ScoredEvidence
 
@@ -18,6 +20,8 @@ class CompatibilityScorer:
         self.weights = {key: float(value) for key, value in ranking.get("weights", {}).items()}
         self.thresholds = {key: float(value) for key, value in ranking.get("thresholds", {}).items()}
         self.compatibility = ranking.get("type_compatibility", {})
+        self.sense = ContextualSenseAligner(text)
+        self.centrality = ScopeCentralityEvaluator(text)
 
     @staticmethod
     def _bounded_score(value: object) -> float:
@@ -150,6 +154,10 @@ class CompatibilityScorer:
         evidence_relation_edges = tuple(edge for edge in evidence.relation_edges if edge[1] in strict_relations)
         relation_alignment = self.relations.alignment(query_relation_edges, evidence_relation_edges) if query_relation_edges else 0.75
         principle_strength = evidence.principle_strength if query.primary_request_type == "principle" else 0.65
+        sense = self.sense.evaluate(query, evidence, source)
+        centrality = self.centrality.evaluate(
+            query, evidence, source, sense, type_match=type_match, directness=directness
+        )
 
         source_quality = 0.68
         source_kind = str(source.get("source_kind", "")).casefold()
@@ -159,12 +167,15 @@ class CompatibilityScorer:
             source_quality = min(source_quality, 0.58)
 
         answerability = min(1.0,
-            0.20 * type_match
-            + 0.23 * topic_metrics["semantic_mass_coverage"]
-            + 0.18 * directness
-            + 0.15 * evidence.answer_quality
-            + 0.14 * relation_alignment
-            + 0.10 * principle_strength
+            0.15 * type_match
+            + 0.17 * topic_metrics["semantic_mass_coverage"]
+            + 0.14 * directness
+            + 0.13 * evidence.answer_quality
+            + 0.10 * relation_alignment
+            + 0.07 * principle_strength
+            + 0.10 * sense.score
+            + 0.07 * sense.joint_scope_coverage
+            + 0.07 * evidence.integrity_score
         )
         metrics = {
             "request_type_match": type_match,
@@ -179,6 +190,17 @@ class CompatibilityScorer:
             "relation_alignment": relation_alignment,
             "principle_strength": principle_strength,
             "answerability": answerability,
+            "sense_alignment": sense.score,
+            "joint_scope_coverage": sense.joint_scope_coverage,
+            "metadata_joint_coverage": sense.metadata_joint_coverage,
+            "answer_joint_coverage": sense.answer_joint_coverage,
+            "sense_dispersion_risk": sense.dispersion_risk,
+            "local_context_alignment": sense.local_context_alignment,
+            "evidence_integrity": evidence.integrity_score,
+            "scope_centrality": centrality.score,
+            "linked_question_alignment": centrality.question_alignment,
+            "title_alignment": centrality.title_alignment,
+            "extra_scope_penalty": centrality.extra_scope_penalty,
         }
         score = sum(self.weights.get(name, 0.0) * value for name, value in metrics.items())
 
@@ -199,6 +221,9 @@ class CompatibilityScorer:
             and matched_ratio >= self.thresholds.get("rescue_matched_concept_ratio", 0.60)
             and directness >= self.thresholds.get("rescue_directness", 0.58)
             and evidence.answer_quality >= self.thresholds.get("minimum_answer_quality", 0.48)
+            and evidence.integrity_score >= self.thresholds.get("rescue_evidence_integrity", 0.54)
+            and sense.score >= self.thresholds.get("rescue_sense_alignment", 0.60)
+            and sense.joint_scope_coverage >= self.thresholds.get("rescue_joint_scope_coverage", 0.62)
             and (
                 neural >= self.thresholds.get("rescue_neural_prior", 0.72)
                 or agreement >= self.thresholds.get("rescue_retriever_agreement", 0.40)
@@ -259,6 +284,21 @@ class CompatibilityScorer:
             and not semantic_rescue
         ):
             hard_rejections.append("directed_relation_mismatch")
+        if (
+            len(query.subject_terms) > 1
+            and sense.score < self.thresholds.get("minimum_sense_alignment", 0.52)
+            and sense.joint_scope_coverage < self.thresholds.get("minimum_joint_scope_coverage", 0.62)
+            and not semantic_rescue
+        ):
+            hard_rejections.append("contextual_sense_mismatch")
+        if (
+            sense.dispersion_risk > self.thresholds.get("maximum_sense_dispersion_risk", 0.46)
+            and sense.joint_scope_coverage < self.thresholds.get("minimum_joint_scope_coverage", 0.62)
+            and not semantic_rescue
+        ):
+            hard_rejections.append("query_concepts_only_distributed_across_unrelated_fields")
+        if evidence.integrity_score < self.thresholds.get("minimum_evidence_integrity", 0.44):
+            hard_rejections.append("evidence_text_integrity_failed")
         if answerability < self.thresholds.get("minimum_answerability", 0.48) and not semantic_rescue:
             hard_rejections.append("insufficient_answerability")
         if not evidence.answer_text.strip():
@@ -278,6 +318,12 @@ class CompatibilityScorer:
             reasons.append("قيود الواقعة مغطاة بوضوح" if query.language == "ar" else "Case constraints are clearly covered")
         if relation_alignment >= 0.70 and query_relation_edges:
             reasons.append("اتجاه العلاقة في السؤال مغطى داخل الشاهد" if query.language == "ar" else "The directed relation in the question is covered")
+        if sense.score >= 0.72 and sense.joint_scope_coverage >= 0.72:
+            reasons.append("مفاهيم السؤال تجتمع في سياق واحد بالمعنى نفسه" if query.language == "ar" else "Query concepts co-occur in one coherent sense context")
+        if evidence.integrity_score >= 0.70:
+            reasons.append("سلامة النص تسمح ببناء جواب موثق" if query.language == "ar" else "Text integrity supports grounded synthesis")
+        if centrality.score >= 0.68:
+            reasons.append("الشاهد يجيب عن مركز السؤال لا عن مسألة جانبية" if query.language == "ar" else "The evidence addresses the central question rather than a side case")
         if directness >= 0.72:
             reasons.append("النتيجة مباشرة وقابلة للبناء عليها" if query.language == "ar" else "The outcome is direct and usable")
         if evidence.answer_quality >= 0.75:

hudanet_core/contextual_sense.py

@@ -0,0 +1,152 @@
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Mapping, Sequence, Tuple
+
+from .text import TextProcessor
+from .types import EvidenceFrame, QueryFrame
+
+
+@dataclass(frozen=True)
+class SenseAlignment:
+    score: float
+    joint_scope_coverage: float
+    metadata_joint_coverage: float
+    answer_joint_coverage: float
+    dispersion_risk: float
+    local_context_alignment: float
+
+
+class ContextualSenseAligner:
+    """Disambiguate shared surface terms through local co-occurrence structure.
+
+    A term may occur in two records with different meanings. The aligner therefore
+    asks whether the query concepts occur together inside one coherent local unit,
+    rather than accepting a record where each concept appears in an unrelated field.
+    No entity dictionary or domain-specific sense inventory is used.
+    """
+
+    def __init__(self, text: TextProcessor):
+        self.text = text
+
+    def _units(self, values: Sequence[object], lang: str) -> Tuple[Tuple[str, ...], ...]:
+        units = []
+        for value in values:
+            raw = str(value or "").strip()
+            if not raw:
+                continue
+            clauses = self.text.clauses(raw, lang) or (raw,)
+            for clause in clauses:
+                terms = self.text.content_terms(clause, lang)
+                if terms:
+                    units.append(tuple(terms))
+        return tuple(units)
+
+    def _strict_unit_coverage(self, query: QueryFrame, unit: Sequence[str]) -> float:
+        """One-to-one concept coverage inside a local unit.
+
+        A single surface token may not satisfy two distinct query concepts. This
+        prevents near-homographs or a shared stem from faking local co-occurrence.
+        """
+        if not query.subject_terms or not unit:
+            return 0.0
+        candidates = []
+        for q_index, query_term in enumerate(query.subject_terms):
+            for u_index, unit_term in enumerate(unit):
+                similarity = self.text.term_similarity(query_term, unit_term)
+                if similarity >= 0.90:
+                    candidates.append((similarity, q_index, u_index))
+        candidates.sort(reverse=True)
+        used_query = set()
+        used_unit = set()
+        matched_weight = 0.0
+        total_weight = sum(float(query.term_weights.get(term, 1.0)) for term in query.subject_terms)
+        for similarity, q_index, u_index in candidates:
+            if q_index in used_query or u_index in used_unit:
+                continue
+            used_query.add(q_index)
+            used_unit.add(u_index)
+            term = query.subject_terms[q_index]
+            matched_weight += float(query.term_weights.get(term, 1.0)) * similarity
+        return min(1.0, matched_weight / max(1e-9, total_weight))
+
+    def _max_coverage(self, query: QueryFrame, units: Sequence[Sequence[str]]) -> float:
+        if not query.subject_terms:
+            return 0.0
+        return max((self._strict_unit_coverage(query, unit) for unit in units), default=0.0)
+
+    def evaluate(self, query: QueryFrame, evidence: EvidenceFrame, source: Mapping[str, object]) -> SenseAlignment:
+        if len(query.subject_terms) <= 1:
+            return SenseAlignment(0.82, 1.0, 1.0, 1.0, 0.0, 0.82)
+
+        metadata_values = [
+            source.get("title", ""), source.get("question", ""), source.get("chapter", ""),
+            source.get("category", ""), source.get("ruling", ""),
+        ]
+        answer_values = list(evidence.answer_segments) or [evidence.answer_text]
+        metadata_units = self._units(metadata_values, query.language)
+        answer_units = self._units(answer_values, query.language)
+        all_units = (*metadata_units, *answer_units)
+
+        metadata_joint = self._max_coverage(query, metadata_units)
+        answer_joint = self._max_coverage(query, answer_units)
+        joint = max(metadata_joint, answer_joint)
+
+        global_terms = tuple(dict.fromkeys((*evidence.metadata_terms, *evidence.answer_terms)))
+        global_coverage = self.text.weighted_fuzzy_overlap(
+            query.subject_terms, global_terms, query.term_weights
+        )
+        dispersion_risk = max(0.0, global_coverage - joint)
+
+        # The query itself supplies the local context for each concept: the other
+        # supported query concepts. Evidence receives credit when they co-occur in
+        # one field or clause. This distinguishes homographs without a lexicon.
+        local_context = 0.0
+        if all_units:
+            matched_pairs = 0.0
+            total_pairs = 0
+            for index, term in enumerate(query.subject_terms):
+                companions = [value for pos, value in enumerate(query.subject_terms) if pos != index]
+                if not companions:
+                    continue
+                total_pairs += 1
+                best = 0.0
+                for unit in all_units:
+                    term_matches = sorted(
+                        ((self.text.term_similarity(term, value), index) for index, value in enumerate(unit)),
+                        reverse=True,
+                    )
+                    companion_matches = sorted(
+                        (
+                            (self.text.term_similarity(companion, value), index)
+                            for companion in companions for index, value in enumerate(unit)
+                        ),
+                        reverse=True,
+                    )
+                    for term_hit, term_index in term_matches:
+                        if term_hit < 0.90:
+                            break
+                        for companion_hit, companion_index in companion_matches:
+                            if companion_hit < 0.90:
+                                break
+                            if companion_index != term_index:
+                                best = max(best, min(term_hit, companion_hit))
+                                break
+                matched_pairs += best
+            local_context = matched_pairs / max(1, total_pairs)
+
+        score = (
+            0.48 * joint
+            + 0.22 * metadata_joint
+            + 0.12 * answer_joint
+            + 0.18 * local_context
+            - 0.28 * dispersion_risk
+        )
+        return SenseAlignment(
+            score=max(0.0, min(1.0, score)),
+            joint_scope_coverage=max(0.0, min(1.0, joint)),
+            metadata_joint_coverage=max(0.0, min(1.0, metadata_joint)),
+            answer_joint_coverage=max(0.0, min(1.0, answer_joint)),
+            dispersion_risk=max(0.0, min(1.0, dispersion_risk)),
+            local_context_alignment=max(0.0, min(1.0, local_context)),
+        )

hudanet_core/evidence.py

@@ -5,6 +5,7 @@ from typing import Any, Dict, List, Mapping, Tuple
 
 from .relation_graph import RelationGraphAnalyzer
 from .text import TextProcessor
+from .text_integrity import TextIntegrityEvaluator
 from .types import EvidenceFrame
 
 
@@ -14,6 +15,7 @@ class EvidenceAnalyzer:
         self.semantic = semantic
         self.schema = schema
         self.relations = relations
+        self.integrity = TextIntegrityEvaluator(text, semantic)
 
     @staticmethod
     def _clean(value: Any) -> str:
@@ -153,9 +155,23 @@ class EvidenceAnalyzer:
         if "disputed" in outcomes and len(outcomes) == 1:
             decisive = min(decisive, 0.38)
 
-        qualities = [self._segment_quality(value, lang) for value in answer_segments]
-        answer_quality = max(qualities or [0.0])
-        noise_ratio = 1.0 - (sum(qualities) / max(1, len(qualities)))
+        legacy_qualities = [self._segment_quality(value, lang) for value in answer_segments]
+        integrity_results = [
+            self.integrity.assess(value, lang, metadata_text) for value in answer_segments
+        ]
+        integrity_scores = [result.score for result in integrity_results]
+        combined_qualities = [
+            0.38 * legacy + 0.62 * integrity
+            for legacy, integrity in zip(legacy_qualities, integrity_scores)
+        ]
+        answer_quality = max(combined_qualities or [0.0])
+        integrity_score = max(integrity_scores or [0.0])
+        noise_ratio = 1.0 - (sum(combined_qualities) / max(1, len(combined_qualities)))
+        integrity_details = {
+            "best_integrity": integrity_score,
+            "mean_integrity": sum(integrity_scores) / max(1, len(integrity_scores)),
+            "clean_segment_ratio": sum(1 for value in integrity_scores if value >= 0.58) / max(1, len(integrity_scores)),
+        }
         completeness = min(
             1.0,
             0.15
@@ -201,4 +217,6 @@ class EvidenceAnalyzer:
             raw=source,
             relation_edges=tuple(relation_edges),
             principle_strength=float(principle_strength),
+            integrity_score=float(integrity_score),
+            integrity_details={key: float(value) for key, value in integrity_details.items()},
         )

hudanet_core/pipeline.py

@@ -182,6 +182,10 @@ class GenericEvidencePipeline:
                 and float(item.metrics.get("answer_directness", 0.0)) >= 0.58
                 and float(item.metrics.get("answerability", 0.0)) >= float(self.resources.ranking.get("thresholds", {}).get("exact_answerability", 0.64))
                 and float(item.metrics.get("relation_alignment", 0.75)) >= float(self.resources.ranking.get("thresholds", {}).get("exact_relation_alignment", 0.58))
+                and float(item.metrics.get("sense_alignment", 0.0)) >= float(self.resources.ranking.get("thresholds", {}).get("exact_sense_alignment", 0.70))
+                and float(item.metrics.get("joint_scope_coverage", 0.0)) >= float(self.resources.ranking.get("thresholds", {}).get("exact_joint_scope_coverage", 0.72))
+                and float(item.metrics.get("evidence_integrity", 0.0)) >= float(self.resources.ranking.get("thresholds", {}).get("exact_evidence_integrity", 0.58))
+                and float(item.metrics.get("scope_centrality", 0.0)) >= float(self.resources.ranking.get("thresholds", {}).get("exact_scope_centrality", 0.58))
             )
             if is_exact:
                 source["tier"] = "exact"

hudanet_core/propositions.py

@@ -6,6 +6,7 @@ from typing import List, Sequence, Set, Tuple
 from .grounding import GroundingVerifier
 from .roles import PropositionRoleClassifier
 from .text import TextProcessor
+from .text_integrity import TextIntegrityEvaluator
 from .types import Proposition, PropositionCluster, QueryFrame, ScoredEvidence
 
 
@@ -24,6 +25,7 @@ class PropositionExtractor:
         self.ranking = ranking
         self.grounding = GroundingVerifier(text, templates.get("preferred_answer_fields", []))
         self.roles = PropositionRoleClassifier(text, semantic)
+        self.integrity = TextIntegrityEvaluator(text, semantic)
 
     @staticmethod
     def _clean(value: str) -> str:
@@ -192,6 +194,7 @@ class PropositionExtractor:
         min_quality = float(thresholds.get("minimum_proposition_quality", 0.54))
         min_score = float(thresholds.get("minimum_proposition_score", 0.52))
         min_focus = float(thresholds.get("minimum_proposition_focus", 0.44))
+        min_clause_integrity = float(thresholds.get("minimum_clause_integrity", 0.48))
 
         for item in selected:
             source_terms = tuple(dict.fromkeys((*item.evidence.metadata_terms, *item.evidence.answer_terms)))
@@ -216,8 +219,10 @@ class PropositionExtractor:
                     continue
                 if explicit_item and query.language == "ar":
                     candidate = re.sub(r"^و(?=[\u0600-\u06FF])", "", candidate).strip()
-                quality = self._quality(candidate, query.language, explicit_item=explicit_item)
-                if quality < min_quality:
+                legacy_quality = self._quality(candidate, query.language, explicit_item=explicit_item)
+                integrity = self.integrity.assess(candidate, query.language, item.evidence.metadata_text)
+                quality = 0.38 * legacy_quality + 0.62 * integrity.score
+                if quality < min_quality or integrity.score < min_clause_integrity:
                     rejected.append(candidate)
                     continue
 
@@ -225,6 +230,12 @@ class PropositionExtractor:
                 candidate_focus = self.text.fuzzy_term_overlap(query.qualifier_terms, terms)
                 candidate_anchor = self.text.fuzzy_term_overlap(query.anchor_terms, terms)
                 relation = self._relation_score(candidate, query, item, explicit_item=explicit_item)
+                local_outcomes = self._local_outcomes(candidate, query.language)
+                structured_clause = (
+                    preliminary_role != "statement"
+                    or any(value != "unspecified" for value in local_outcomes)
+                    or explicit_item
+                )
                 policy = self.ranking.get("proposition_policy", {}) or {}
                 contextual_roles = set(
                     (policy.get("contextual_roles_by_request", {}) or {}).get(
@@ -242,7 +253,7 @@ class PropositionExtractor:
                 # query inherits that source scope. This handles pronouns and omitted
                 # subjects without any entity-specific dictionary.
                 inheritance = 0.0
-                if source_type_match or explicit_item or relation >= 0.65:
+                if structured_clause and (source_type_match or explicit_item or relation >= 0.65):
                     inheritance = 0.88 * source_focus
 
                 # Evidence-conditioned proposition scope inheritance.
@@ -269,6 +280,8 @@ class PropositionExtractor:
                         and type_alignment >= float(inheritance_cfg.get("minimum_request_type_match", 0.62))
                         and candidate_anchor >= float(inheritance_cfg.get("minimum_candidate_anchor", 0.42))
                         and relation >= float(inheritance_cfg.get("minimum_candidate_relation", 0.66))
+                        and integrity.score >= float(inheritance_cfg.get("minimum_clause_integrity", 0.55))
+                        and (structured_clause or candidate_focus >= 0.24)
                     ):
                         scope_trust = (
                             0.30 * semantic_mass
@@ -295,7 +308,6 @@ class PropositionExtractor:
                     rejected.append(candidate)
                     continue
 
-                local_outcomes = self._local_outcomes(candidate, query.language)
                 if (
                     query.primary_request_type == "principle"
                     and field == "ruling"

hudanet_core/query.py

@@ -123,14 +123,21 @@ class QueryAnalyzer:
         matched_intent_tokens = set()
         for type_id, rule in self.semantic.get("request_types", {}).items():
             patterns = (rule.get("patterns", {}) or {}).get(lang, []) or []
+            keywords = (rule.get("keywords", {}) or {}).get(lang, []) or []
             hits = sum(1 for pattern in patterns if self.text.phrase_hit(normalized, pattern))
-            if hits:
+            keyword_hits = sum(1 for keyword in keywords if self.text.phrase_hit(normalized, keyword))
+            if hits or keyword_hits:
                 priority = float(rule.get("priority", 1.0) or 1.0)
-                score = min(1.0, min(1.0, 0.58 + 0.18 * hits) * priority)
+                # Generic shells such as "what is" / "ما هو" must not outrank a
+                # more specific request word that appears in the same question.
+                lexical_specificity = min(0.30, 0.12 * keyword_hits)
+                score = min(1.0, (0.42 + 0.18 * hits + lexical_specificity) * priority)
+                if hits and not keyword_hits and type_id == "description":
+                    score *= 0.68
                 request_scores.append((type_id, score))
                 request_type_scores[type_id] = max(request_type_scores.get(type_id, 0.0), score)
                 matched_intent_tokens.update(
-                    self.text.content_terms(" ".join(rule.get("keywords", {}).get(lang, [])), lang)
+                    self.text.content_terms(" ".join(keywords), lang)
                 )
         request_scores.sort(key=lambda item: item[1], reverse=True)
         request_types = tuple(item[0] for item in request_scores)

hudanet_core/resources/answer_templates.json

@@ -1,5 +1,5 @@
 {
-  "version": "2.1.0",
+  "version": "1.4.0",
   "answer_prefix": {
     "ar": "**الإجابة:** ",
     "en": "**Answer:** "
@@ -136,5 +136,13 @@
   "support_heading": {
     "ar": "التفصيل أو الدليل",
     "en": "Supporting detail or evidence"
+  },
+  "ruling_summary_heading": {
+    "ar": "الحكم المختصر",
+    "en": "Ruling summary"
+  },
+  "ruling_detail_heading": {
+    "ar": "التفصيل والاستثناءات",
+    "en": "Details and exceptions"
   }
 }

hudanet_core/resources/ranking_config.json

@@ -1,22 +1,26 @@
 {
-  "version": "2.3.0",
-  "description": "Domain-neutral hierarchical intent repair, directed relation alignment, issue clustering, answerability-aware tiers, proposition ranking, and grounded synthesis.",
+  "version": "2.4.0",
+  "description": "Domain-neutral contextual-sense alignment, text-integrity gating, central ruling frames, hierarchical intent repair, directed relations, issue clustering, answerability-aware tiers, and grounded synthesis.",
   "weights": {
-    "request_type_match": 0.112,
-    "topic_match": 0.064,
-    "anchor_coverage": 0.072,
-    "query_focus_coverage": 0.096,
-    "semantic_mass_coverage": 0.16,
-    "critical_constraint_coverage": 0.08,
-    "polarity_alignment": 0.064,
-    "answer_directness": 0.064,
-    "evidence_completeness": 0.024,
-    "answer_quality": 0.032,
-    "source_quality": 0.016,
-    "neural_prior": 0.016,
-    "relation_alignment": 0.1,
-    "principle_strength": 0.04,
-    "answerability": 0.06
+    "request_type_match": 0.085,
+    "topic_match": 0.04,
+    "anchor_coverage": 0.05,
+    "query_focus_coverage": 0.06,
+    "semantic_mass_coverage": 0.11,
+    "critical_constraint_coverage": 0.055,
+    "polarity_alignment": 0.045,
+    "answer_directness": 0.05,
+    "evidence_completeness": 0.015,
+    "answer_quality": 0.03,
+    "source_quality": 0.015,
+    "neural_prior": 0.015,
+    "relation_alignment": 0.07,
+    "principle_strength": 0.02,
+    "answerability": 0.05,
+    "sense_alignment": 0.085,
+    "joint_scope_coverage": 0.05,
+    "evidence_integrity": 0.055,
+    "scope_centrality": 0.1
   },
   "thresholds": {
     "accept": 0.53,
@@ -46,7 +50,19 @@
     "hard_relation_mismatch": 0.23,
     "minimum_answerability": 0.48,
     "exact_answerability": 0.64,
-    "exact_relation_alignment": 0.58
+    "exact_relation_alignment": 0.58,
+    "minimum_sense_alignment": 0.52,
+    "minimum_joint_scope_coverage": 0.62,
+    "maximum_sense_dispersion_risk": 0.46,
+    "minimum_evidence_integrity": 0.44,
+    "rescue_sense_alignment": 0.6,
+    "rescue_joint_scope_coverage": 0.62,
+    "rescue_evidence_integrity": 0.54,
+    "exact_sense_alignment": 0.7,
+    "exact_joint_scope_coverage": 0.72,
+    "exact_evidence_integrity": 0.58,
+    "exact_scope_centrality": 0.58,
+    "minimum_clause_integrity": 0.48
   },
   "type_compatibility": {
     "definition": {
@@ -291,7 +307,8 @@
       "inheritance_weight": 0.9,
       "maximum_inherited_scope": 0.92,
       "requires_accepted_evidence": true,
-      "description": "Generic multi-signal scope inheritance for grounded atomic clauses from already accepted evidence. Dense or lexical similarity alone is insufficient."
+      "description": "Generic multi-signal scope inheritance for grounded atomic clauses from already accepted evidence. Dense or lexical similarity alone is insufficient.",
+      "minimum_clause_integrity": 0.55
     }
   },
   "intent_repair": {
@@ -305,5 +322,44 @@
   "issue_clustering": {
     "pair_threshold": 0.33,
     "minimum_cluster_books_for_broad_principle": 2
+  },
+  "ruling_frame": {
+    "central_cluster_floor": 0.56,
+    "family_order": [
+      "obligation",
+      "permission",
+      "validity",
+      "sufficiency",
+      "remedy",
+      "recommendation",
+      "other"
+    ],
+    "outcome_families": {
+      "obligation": [
+        "obligatory",
+        "not_obligatory",
+        "obligation_dropped"
+      ],
+      "permission": [
+        "permissible",
+        "prohibited"
+      ],
+      "validity": [
+        "valid",
+        "invalid"
+      ],
+      "sufficiency": [
+        "sufficient",
+        "not_sufficient"
+      ],
+      "remedy": [
+        "remedy_required",
+        "no_remedy"
+      ],
+      "recommendation": [
+        "recommended",
+        "disliked"
+      ]
+    }
   }
-}
+}

hudanet_core/resources/semantic_rules.json

@@ -1,6 +1,6 @@
 {
-  "version": "2.3.0",
-  "description": "Domain-neutral hierarchical request understanding, directed relation graphs, evidence-conditioned intent repair, outcomes, and proposition roles.",
+  "version": "2.4.0",
+  "description": "Domain-neutral semantic rules for request types, outcomes, proposition roles, relation markers, contextual sense, and grounded ruling-frame synthesis.",
   "decisive_request_types": [
     "ruling",
     "validity",
@@ -986,6 +986,7 @@
       },
       "patterns": {
         "ar": [
+          "ما\\s+(?:هو\\s+)?(?:ال)?حكم",
           "ما\\s+(?:هو\\s+)?الحكم",
           "ما\\s+حكم",
           "هل\\s+(?:يجوز|يجب|يحرم|يلزم)",
@@ -1163,6 +1164,28 @@
           "\\b(?:pillar|essential pillar)\\b"
         ]
       }
+    },
+    "sufficient": {
+      "decisiveness": 0.95,
+      "patterns": {
+        "ar": [
+          "(?:يجزئ|يجزي|مجزئ|يسقط\\s+الفرض|يكفي\\s+عن)"
+        ],
+        "en": [
+          "\\b(?:sufficient|counts for|discharges the obligation|fulfills the duty)\\b"
+        ]
+      }
+    },
+    "not_sufficient": {
+      "decisiveness": 0.98,
+      "patterns": {
+        "ar": [
+          "(?:لا\\s+يجزئ|لا\\s+يجزي|غير\\s+مجزئ|لا\\s+يسقط\\s+الفرض|لا\\s+يكفي\\s+عن)"
+        ],
+        "en": [
+          "\\b(?:not sufficient|does not count for|does not discharge the obligation|does not fulfill the duty)\\b"
+        ]
+      }
     }
   },
   "incompatible_outcomes": [
@@ -1185,6 +1208,10 @@
     [
       "remedy_required",
       "no_remedy"
+    ],
+    [
+      "sufficient",
+      "not_sufficient"
     ]
   ],
   "proposition_relations": {
@@ -1309,7 +1336,7 @@
       "priority": 90,
       "patterns": {
         "ar": [
-          "^(?:اذا|إذا|إن|ان|لو|عند\\s+عدم|في\\s+حال)\\b.+(?:،|\\s)(?:ف?يجب|ف?يجوز|ف?لا|ف?يسقط|ف?يلزم|ف?يفعل|ف?تستعمل|ف?يستعمل|ف?تنوب|ف?ينوب|ف?تستنيب|ف?يستنيب|ف?ينصرف|ف?يقع|ف?يبطل|ف?يصح|ف?يجزئ|ف?يجزي|ف?لا\\s+يجزئ|ف?لا\\s+يجزي)",
+          "^(?:ف?اذا|ف?إذا|ف?إن|ف?ان|ف?لو|فعند\\s+عدم|ففي\\s+حال)\\b.+(?:،|\\s)(?:ف?يجب|ف?وجب|ف?يجوز|ف?لا|ف?يسقط|ف?يلزم|ف?يفعل|ف?تستعمل|ف?يستعمل|ف?تنوب|ف?ينوب|ف?تستنيب|ف?يستنيب|ف?ينصرف|ف?يقع|ف?يبطل|ف?يصح|ف?يجزئ|ف?يجزي|ف?لا\\s+يجزئ|ف?لا\\s+يجزي)",
           "^(?:تنصرف|ينصرف|يقع|تؤول|يؤول|تعود|يعود|يبطل|يصح)\\b"
         ],
         "en": [

hudanet_core/ruling_frame.py

@@ -0,0 +1,253 @@
+from __future__ import annotations
+
+from collections import defaultdict
+from typing import Dict, List, Mapping, Sequence, Set, Tuple
+
+from .propositions import PropositionExtractor
+from .rendering import AnswerRenderer
+from .text import TextProcessor
+from .types import PropositionCluster, QueryFrame, ScoredEvidence, SynthesisResult
+
+
+class RulingFramePlanner:
+    """Build a central ruling from grounded propositions and complementary facets.
+
+    Outcome families are generic legal/decision dimensions. The planner does not
+    know any domain entities and never stores a question or answer.
+    """
+
+    DEFAULT_FAMILIES: Mapping[str, Set[str]] = {
+        "obligation": {"obligatory", "not_obligatory", "obligation_dropped"},
+        "permission": {"permissible", "prohibited"},
+        "validity": {"valid", "invalid"},
+        "sufficiency": {"sufficient", "not_sufficient"},
+        "remedy": {"remedy_required", "no_remedy"},
+        "recommendation": {"recommended", "disliked"},
+    }
+
+    def __init__(self, text: TextProcessor, semantic: dict, templates: dict, ranking: dict):
+        self.text = text
+        self.semantic = semantic
+        self.templates = templates
+        self.ranking = ranking
+        self.extractor = PropositionExtractor(text, semantic, templates, ranking)
+        self.renderer = AnswerRenderer(templates, ranking)
+        configured = (ranking.get("ruling_frame", {}) or {}).get("outcome_families", {}) or {}
+        self.families = {
+            key: set(values) for key, values in (configured or self.DEFAULT_FAMILIES).items()
+        }
+
+    def _family(self, outcomes: Sequence[str]) -> str:
+        outcome_set = set(outcomes)
+        for family, values in self.families.items():
+            if outcome_set & values:
+                return family
+        return "other"
+
+    def _source_labels(self, cluster: PropositionCluster, lang: str) -> List[str]:
+        labels = []
+        seen = set()
+        for proposition in cluster.propositions:
+            book = proposition.source_book
+            page = proposition.source_page
+            label = f"{book} ({'ص' if lang == 'ar' else 'p.'} {page})" if page else book
+            if label and label not in seen:
+                seen.add(label)
+                labels.append(label)
+        return labels
+
+    def _centrality_for_cluster(self, cluster: PropositionCluster, selected: Sequence[ScoredEvidence]) -> float:
+        by_id = {item.evidence.record_id or str(id(item.source)): item for item in selected}
+        values = []
+        for record_id in cluster.source_record_ids:
+            item = by_id.get(record_id)
+            if item:
+                values.append(float(item.metrics.get("scope_centrality", 0.0)))
+        return max(values or [0.0])
+
+    def _cluster_outcomes(self, cluster: PropositionCluster) -> Set[str]:
+        return {
+            value
+            for proposition in cluster.propositions
+            for value in proposition.outcomes
+            if value not in {"unspecified", "disputed", "condition", "pillar"}
+        }
+
+    def _cluster_families(self, cluster: PropositionCluster) -> Set[str]:
+        families = set()
+        for outcome in self._cluster_outcomes(cluster):
+            families.add(self._family((outcome,)))
+        return families or {"other"}
+
+    def _supporting_items(
+        self,
+        families: Set[str],
+        selected: Sequence[ScoredEvidence],
+        centrality_floor: float,
+    ) -> List[ScoredEvidence]:
+        result = []
+        for item in selected:
+            if float(item.metrics.get("scope_centrality", 0.0)) < centrality_floor:
+                continue
+            item_families = {
+                self._family((value,))
+                for value in item.evidence.outcomes
+                if value not in {"unspecified", "disputed", "condition", "pillar"}
+            }
+            if families & item_families:
+                result.append(item)
+        return result
+
+    def _item_label(self, item: ScoredEvidence, lang: str) -> str:
+        book = item.evidence.book or item.evidence.book_id or item.evidence.record_id
+        page = item.evidence.page
+        return f"{book} ({'ص' if lang == 'ar' else 'p.'} {page})" if page else book
+
+    def plan(self, query: QueryFrame, selected: Sequence[ScoredEvidence]) -> SynthesisResult | None:
+        propositions, rejected = self.extractor.extract(query, selected)
+        clusters = self.extractor.cluster(query, propositions)
+        if not clusters:
+            return None
+
+        centrality_floor = float((self.ranking.get("ruling_frame", {}) or {}).get("central_cluster_floor", 0.56))
+        central_clusters = [
+            cluster for cluster in clusters
+            if self._centrality_for_cluster(cluster, selected) >= centrality_floor
+            and cluster.role != "consequence"
+        ]
+        if not central_clusters:
+            central_clusters = [cluster for cluster in clusters if cluster.role != "consequence"] or clusters[:1]
+
+        # Prefer one broad, direct proposition that already combines several ruling
+        # dimensions. This prevents a compact ruling from being exploded into four
+        # repetitive bullets when one source states it cleanly.
+        def summary_key(cluster: PropositionCluster):
+            families = self._cluster_families(cluster) - {"other"}
+            word_count = len(self.text.tokens(cluster.text, query.language))
+            compactness = max(0.0, 1.0 - max(0, word_count - 24) / 30.0)
+            return (
+                self._centrality_for_cluster(cluster, selected),
+                len(families),
+                len(cluster.source_books),
+                cluster.score,
+                compactness,
+            )
+
+        summary = max(central_clusters, key=summary_key)
+        covered_families = self._cluster_families(summary)
+
+        family_groups: Dict[str, List[PropositionCluster]] = defaultdict(list)
+        details: List[PropositionCluster] = []
+        for cluster in clusters:
+            if cluster is summary:
+                continue
+            centrality = self._centrality_for_cluster(cluster, selected)
+            if cluster.role == "consequence" or centrality < centrality_floor:
+                details.append(cluster)
+                continue
+            for family in self._cluster_families(cluster):
+                if family not in covered_families:
+                    family_groups[family].append(cluster)
+
+        additions: List[PropositionCluster] = []
+        family_order = list((self.ranking.get("ruling_frame", {}) or {}).get(
+            "family_order", ["obligation", "permission", "validity", "sufficiency", "remedy", "recommendation", "other"]
+        ))
+        for family in family_order:
+            candidates = family_groups.get(family, [])
+            if not candidates:
+                continue
+            candidates.sort(
+                key=lambda cluster: (
+                    1.0 / max(1, len(self._cluster_families(cluster))),
+                    self._centrality_for_cluster(cluster, selected),
+                    len(cluster.source_books),
+                    cluster.score,
+                ),
+                reverse=True,
+            )
+            additions.append(candidates[0])
+            covered_families.add(family)
+
+        question_label = self.templates.get("question_heading", {}).get(query.language, "Question")
+        answer_label = self.templates.get("answer_heading", {}).get(query.language, "Answer")
+        summary_label = self.templates.get("ruling_summary_heading", {}).get(query.language, "Ruling")
+        detail_label = self.templates.get("ruling_detail_heading", {}).get(query.language, "Details")
+        lines = [f"**{question_label}:** {query.raw}", "", f"**{answer_label}:**", ""]
+
+        used_records: List[str] = []
+        used_books: List[str] = []
+
+        summary_support_floor = max(
+            centrality_floor, self._centrality_for_cluster(summary, selected) - 0.10
+        )
+        summary_support = self._supporting_items(
+            self._cluster_families(summary), selected, summary_support_floor
+        )
+        summary_labels = []
+        seen_labels = set()
+        for item in summary_support:
+            label = self._item_label(item, query.language)
+            if label and label not in seen_labels:
+                seen_labels.add(label)
+                summary_labels.append(label)
+            used_records.append(item.evidence.record_id or str(id(item.source)))
+            used_books.append(item.evidence.book_id or item.evidence.book)
+        if not summary_labels:
+            summary_labels = self._source_labels(summary, query.language)
+            used_records.extend(summary.source_record_ids)
+            used_books.extend(
+                proposition.source_book_id for proposition in summary.propositions
+            )
+        summary_line = f"**{summary_label}:** {summary.text.strip()}"
+        if summary_labels:
+            summary_line += f"  _[{ '، '.join(summary_labels) }]_"
+        lines.append(summary_line)
+
+        if additions:
+            lines.extend(["", f"**{detail_label}:**"])
+            for cluster in additions:
+                families = self._cluster_families(cluster)
+                support = self._supporting_items(families, selected, centrality_floor)
+                labels = []
+                seen = set()
+                for item in support:
+                    label = self._item_label(item, query.language)
+                    if label and label not in seen:
+                        seen.add(label)
+                        labels.append(label)
+                    used_records.append(item.evidence.record_id or str(id(item.source)))
+                    used_books.append(item.evidence.book_id or item.evidence.book)
+                line = f"- {cluster.text.strip()}"
+                if labels:
+                    line += f"  _[{ '، '.join(labels) }]_"
+                lines.append(line)
+
+        chosen_texts = [summary.text, *(cluster.text for cluster in additions)]
+        useful_details = []
+        for cluster in details:
+            if any(self.text.sentence_similarity(cluster.text, text, query.language) >= 0.80 for text in chosen_texts):
+                continue
+            useful_details.append(cluster)
+        if useful_details:
+            if not additions:
+                lines.extend(["", f"**{detail_label}:**"])
+            for cluster in useful_details[:3]:
+                labels = self._source_labels(cluster, query.language)
+                line = f"- {cluster.text.strip()}"
+                if labels:
+                    line += f"  _[{ '، '.join(labels) }]_"
+                lines.append(line)
+                used_records.extend(cluster.source_record_ids)
+                used_books.extend(
+                    proposition.source_book_id for proposition in cluster.propositions
+                )
+
+        return SynthesisResult(
+            answer="\n".join(lines).strip(),
+            used_record_ids=tuple(dict.fromkeys(used_records)),
+            used_book_ids=tuple(dict.fromkeys(used_books)),
+            propositions=tuple([summary, *additions, *useful_details[:3]]),
+            rejected_fragments=tuple(dict.fromkeys(rejected)),
+        )
+

hudanet_core/synthesis.py

@@ -5,6 +5,7 @@ from typing import Dict, Iterable, List, Sequence, Set, Tuple
 
 from .propositions import PropositionExtractor
 from .rendering import AnswerRenderer
+from .ruling_frame import RulingFramePlanner
 from .text import TextProcessor
 from .types import ConsensusResult, PropositionCluster, QueryFrame, ScoredEvidence, SynthesisResult
 
@@ -19,6 +20,7 @@ class AnswerSynthesizer:
         self.ranking = ranking
         self.extractor = PropositionExtractor(text, semantic, templates, ranking)
         self.renderer = AnswerRenderer(templates, ranking)
+        self.ruling_planner = RulingFramePlanner(text, semantic, templates, ranking)
 
     @staticmethod
     def _clean(value: str) -> str:
@@ -292,7 +294,19 @@ class AnswerSynthesizer:
             body = self.renderer.prose_answer(query, "\n".join(parts))
             used_record_ids = tuple(dict.fromkeys(used_record_ids_list))
             used_book_ids = tuple(dict.fromkeys(used_book_ids_list))
-        elif compare_sources and query.primary_request_type in {"ruling", "validity", "remedy", "exception"}:
+        elif query.primary_request_type in {"ruling", "validity", "remedy", "exception"}:
+            frame_result = self.ruling_planner.plan(query, selected)
+            if frame_result is not None:
+                answer = self.renderer.append_sources(
+                    frame_result.answer, query, selected, frame_result.used_record_ids
+                )
+                return SynthesisResult(
+                    answer=answer,
+                    used_record_ids=frame_result.used_record_ids,
+                    used_book_ids=frame_result.used_book_ids,
+                    propositions=frame_result.propositions,
+                    rejected_fragments=frame_result.rejected_fragments,
+                )
             facet_result = self._ruling_facet_answer(query, selected, style)
             if facet_result is not None:
                 return facet_result

hudanet_core/tests/test_generic_pipeline.py

@@ -259,6 +259,35 @@ def run():
     check("exact tier requires answer contribution", exact_ids.issubset(used_ids) and bool(exact_ids), {"exact": sorted(exact_ids), "used": sorted(used_ids)})
     check("insufficient answer cannot retain extreme confidence", pipeline.resolve("ما القاعدة؟", [], "ar").confidence <= .35)
 
+    # Contextual-sense separation, OCR integrity, and central ruling-frame
+    # synthesis. The synthetic vocabulary is domain-neutral and intentionally
+    # places the same surface terms in unrelated fields.
+    sense_query = "ما هو حكم تنفيذ العامل للعملية؟"
+    sense_sources = [
+        src("sense-main", "كتاب الحكم المركزي", "حكم تنفيذ العامل للعملية", sense_query,
+            "لا يجب / يصح", "لا يجب تنفيذ العامل للعملية، لكنه يصح منه إذا فعله.",
+            .94, .93, .82, direct_probability=.94, bm25_score=.91, retriever_agreement=5),
+        src("sense-supplement", "كتاب الحكم المكمل", "تنفيذ العامل والأهلية",
+            "هل يجزئ تنفيذ العامل قبل اكتمال الأهلية؟", "صحيح غير مجزئ",
+            "يصح التنفيذ، لكنه لا يجزئ عن الالتزام الأصلي. فإذا اكتملت الأهلية وجب التنفيذ.",
+            .90, .92, .80, direct_probability=.90, bm25_score=.88, retriever_agreement=5),
+        src("sense-homograph", "مصدر اللفظ المشترك", "قبول العملية وآثارها",
+            "ما علامة الانتفاع بالعملية بعد الرجوع؟", "مقصد عام",
+            "أن يرجع العامل أصلح حالا وأكثر التزاما.",
+            .97, .95, .79, direct_probability=.97, bm25_score=.76, retriever_agreement=4),
+        src("sense-ocr", "مصدر النص المشوه", "حكم تنفيذ العامل للعملية", sense_query,
+            "تفصيل", "خلاصة السجل: لاء إلا أن يفيق مساكل أفصئ دأؤود.",
+            .91, .92, .74, direct_probability=.91, bm25_score=.80, retriever_agreement=4,
+            source_kind="raw OCR source"),
+    ]
+    sense_result = pipeline.resolve(sense_query, sense_sources, "ar")
+    check("specific ruling outranks generic what-is shell", sense_result.query.primary_request_type == "ruling", sense_result.details)
+    check("contextual homograph rejected", any(item.evidence.record_id == "sense-homograph" and not item.accepted and "contextual_sense_mismatch" in item.hard_rejections for item in sense_result.ranked), sense_result.details)
+    check("broken OCR rejected", any(item.evidence.record_id == "sense-ocr" and not item.accepted and "evidence_text_integrity_failed" in item.hard_rejections for item in sense_result.ranked), sense_result.details)
+    check("central ruling frame rendered", "**الحكم المختصر:**" in sense_result.answer and "لا يجب تنفيذ العامل للعملية" in sense_result.answer, sense_result.answer)
+    check("complementary sufficiency facet retained", "لا يجزئ عن الالتزام الأصلي" in sense_result.answer, sense_result.answer)
+    check("homograph and OCR excluded from answer", "أصلح حالا" not in sense_result.answer and "أفصئ دأؤود" not in sense_result.answer, sense_result.answer)
+
     semantic = pipeline.resources.semantic
     check("no domain anchor table", "domain_anchor_terms" not in semantic)
     check("no entity alias table", "concept_aliases" not in semantic)

hudanet_core/text_integrity.py

@@ -0,0 +1,119 @@
+from __future__ import annotations
+
+import re
+from dataclasses import dataclass
+from typing import Mapping, Sequence
+
+from .text import TextProcessor
+
+
+@dataclass(frozen=True)
+class IntegrityResult:
+    score: float
+    script_ratio: float
+    punctuation_ratio: float
+    short_token_ratio: float
+    metadata_coherence: float
+    semantic_structure: float
+    repetition_penalty: float
+
+
+class TextIntegrityEvaluator:
+    """Domain-neutral integrity gate for extracted source text.
+
+    The evaluator does not use a vocabulary of valid words. It combines writing-
+    system consistency, punctuation/fragmentation, cross-field coherence, and
+    generic legal/answer structure. This catches OCR fragments while preserving
+    rare terminology that is supported by the source metadata or a clear ruling.
+    """
+
+    def __init__(self, text: TextProcessor, semantic: Mapping[str, object]):
+        self.text = text
+        self.semantic = semantic
+
+    def _semantic_structure(self, value: str, lang: str) -> float:
+        normalized = self.text.normalize(value, lang)
+        hits = 0
+        for rule in (self.semantic.get("outcomes", {}) or {}).values():
+            patterns = (rule.get("patterns", {}) or {}).get(lang, []) or []
+            if any(self.text.phrase_hit(normalized, pattern) for pattern in patterns):
+                hits += 1
+        for rule in (self.semantic.get("proposition_roles", {}) or {}).values():
+            patterns = (rule.get("patterns", {}) or {}).get(lang, []) or []
+            if any(self.text.phrase_hit(normalized, pattern) for pattern in patterns):
+                hits += 1
+        return min(1.0, 0.34 * hits)
+
+    def assess(self, value: object, lang: str, metadata_text: object = "") -> IntegrityResult:
+        raw = re.sub(r"\s+", " ", str(value or "")).strip()
+        normalized = self.text.normalize(raw, lang)
+        words = normalized.split()
+        if not raw or not words:
+            return IntegrityResult(0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0)
+
+        visible = re.findall(r"\w", raw, flags=re.UNICODE)
+        if lang == "ar":
+            letters = re.findall(r"[\u0600-\u06FF]", raw)
+        else:
+            letters = re.findall(r"[A-Za-z]", raw)
+        script_ratio = len(letters) / max(1, len(visible))
+
+        punctuation = re.findall(r"[^\w\s\u0600-\u06FF]", raw, flags=re.UNICODE)
+        punctuation_ratio = len(punctuation) / max(1, len(raw))
+        short_token_ratio = sum(1 for word in words if len(word) <= 2) / max(1, len(words))
+        long_token_ratio = sum(1 for word in words if len(word) >= 18) / max(1, len(words))
+
+        repeated_char_tokens = 0
+        low_diversity_tokens = 0
+        for word in words:
+            if re.search(r"(.)\1{3,}", word):
+                repeated_char_tokens += 1
+            if len(word) >= 6 and len(set(word)) / len(word) < 0.34:
+                low_diversity_tokens += 1
+        repetition_penalty = min(
+            1.0,
+            (repeated_char_tokens + low_diversity_tokens) / max(1, len(words)) * 2.4,
+        )
+
+        candidate_terms = self.text.content_terms(raw, lang)
+        metadata_terms = self.text.content_terms(metadata_text, lang)
+        metadata_coherence = (
+            self.text.fuzzy_term_overlap(candidate_terms, metadata_terms)
+            if candidate_terms and metadata_terms else 0.0
+        )
+        semantic_structure = self._semantic_structure(raw, lang)
+
+        length_score = 1.0
+        if len(words) < 3:
+            length_score = 0.56
+        elif len(words) > 140:
+            length_score = 0.78
+
+        shape_score = (
+            0.48 * min(1.0, script_ratio / 0.78)
+            + 0.18 * max(0.0, 1.0 - min(1.0, punctuation_ratio / 0.18))
+            + 0.14 * max(0.0, 1.0 - min(1.0, short_token_ratio / 0.62))
+            + 0.08 * max(0.0, 1.0 - min(1.0, long_token_ratio / 0.18))
+            + 0.12 * length_score
+        )
+        support_score = max(metadata_coherence, semantic_structure)
+        score = 0.66 * shape_score + 0.34 * support_score
+        score -= 0.24 * repetition_penalty
+
+        # Clauses that are both unsupported by their own metadata and contain no
+        # recognizable answer structure should not inherit source scope merely
+        # because the source record ranked well.
+        if metadata_coherence < 0.12 and semantic_structure < 0.18 and len(words) >= 5:
+            score -= 0.18
+        if script_ratio < 0.46:
+            score -= 0.22
+
+        return IntegrityResult(
+            score=max(0.0, min(1.0, score)),
+            script_ratio=max(0.0, min(1.0, script_ratio)),
+            punctuation_ratio=max(0.0, min(1.0, punctuation_ratio)),
+            short_token_ratio=max(0.0, min(1.0, short_token_ratio)),
+            metadata_coherence=max(0.0, min(1.0, metadata_coherence)),
+            semantic_structure=max(0.0, min(1.0, semantic_structure)),
+            repetition_penalty=max(0.0, min(1.0, repetition_penalty)),
+        )

hudanet_core/types.py

@@ -58,6 +58,8 @@ class EvidenceFrame:
     raw: Mapping[str, Any]
     relation_edges: Tuple[Tuple[str, str, str], ...] = ()
     principle_strength: float = 0.0
+    integrity_score: float = 0.0
+    integrity_details: Mapping[str, float] = field(default_factory=dict)
 
 
 @dataclass

hudanet_v38_0_6_contextual_ruling_regression.json

@@ -0,0 +1,270 @@
+{
+  "version": "38.0.6",
+  "query": "ما هو حكم حج العبد؟",
+  "answer": "**السؤال:** ما هو حكم حج العبد؟\n\n**الإجابة:**\n\n**الحكم المختصر:** لا يجب الحج على العبد، ويصح منه إذا فعله.  _[الهداية على مذهب الإمام أحمد (ص 169)، الفروع (ص 207)، مختصر الخرقي (ص 71)، الكافي في فقه الإمام أحمد (ص 191)]_\n\n**التفصيل والاستثناءات:**\n- لكنه لا يجزئ عن حجة الإسلام.  _[الهداية على مذهب الإمام أحمد (ص 169)، مختصر الخرقي (ص 71)]_\n- فإذا بلغ الصبي أو عتق العبد وجب عليه الحج.  _[مختصر الخرقي (ص 71)]_\n\n**المصادر التي بُني عليها الجواب:**\n- الهداية على مذهب الإمام أحمد (ص 169)\n- الفروع (ص 207)\n- مختصر الخرقي (ص 71)\n- الكافي في فقه الإمام أحمد (ص 191)",
+  "checks": {
+    "request_type_is_ruling": true,
+    "main_answer_present": true,
+    "non_sufficiency_present": true,
+    "status_change_detail_present": true,
+    "homograph_rejected": true,
+    "ocr_rejected": true,
+    "bad_text_absent": true,
+    "exact_sources_contributed": true,
+    "bad_sources_distant": true
+  },
+  "passed": true,
+  "details": {
+    "request_type": "ruling",
+    "initial_request_type": "ruling",
+    "request_type_scores": {
+      "exception": 0.198796,
+      "pillars": 0.0,
+      "cause": 0.107044,
+      "ruling": 0.826066,
+      "description": 0.418356,
+      "timing": 0.140176,
+      "definition": 0.0,
+      "validity": 0.20899,
+      "evidence": 0.158017,
+      "principle": 0.234477,
+      "amount": 0.381888,
+      "location": 0.101637,
+      "comparison": 0.0,
+      "procedure": 0.110464,
+      "list": 0.125807,
+      "remedy": 0.198796,
+      "conditions": 0.090541,
+      "duties": 0.153423,
+      "components": 0.0
+    },
+    "intent_repaired": false,
+    "intent_repair_reason": "",
+    "relation_edges": [],
+    "relation_signature": "",
+    "subject_terms": [
+      "حج",
+      "عبد"
+    ],
+    "anchor_terms": [
+      "عبد"
+    ],
+    "qualifier_terms": [
+      "حج"
+    ],
+    "critical_terms": [
+      "حج"
+    ],
+    "term_document_frequency": {
+      "حج": 1.0,
+      "عبد": 1.0
+    },
+    "term_evidence_support": {
+      "حج": 1.0,
+      "عبد": 1.0
+    },
+    "term_weights": {
+      "حج": 0.65,
+      "عبد": 0.65
+    },
+    "operator_terms": [],
+    "polarity": "affirmative",
+    "accepted": 5,
+    "rejected": 2,
+    "consensus_state": "mixed",
+    "selected_cluster": "not_sufficient+valid",
+    "issue_clusters": {
+      "issue-1": {
+        "books": 4,
+        "weight": 4.5057,
+        "principle_strength": 0.514,
+        "relation_alignment": 0.75,
+        "answerability": 0.9253
+      },
+      "issue-2": {
+        "books": 1,
+        "weight": 0.9799,
+        "principle_strength": 0.746,
+        "relation_alignment": 0.75,
+        "answerability": 0.9432
+      }
+    },
+    "used_record_ids": [
+      "hidayah",
+      "furu",
+      "khiraqi",
+      "kafi"
+    ],
+    "used_book_ids": [
+      "الهداية على مذهب الإمام أحمد",
+      "الفروع",
+      "مختصر الخرقي",
+      "الكافي في فقه الإمام أحمد"
+    ],
+    "propositions": [
+      {
+        "text": "لا يجب الحج على العبد، ويصح منه إذا فعله.",
+        "score": 0.9543,
+        "source_record_ids": [
+          "furu"
+        ],
+        "source_books": [
+          "الفروع"
+        ],
+        "source_pages": [
+          "207"
+        ],
+        "role": "statement"
+      },
+      {
+        "text": "لكنه لا يجزئ عن حجة الإسلام.",
+        "score": 0.889,
+        "source_record_ids": [
+          "khiraqi"
+        ],
+        "source_books": [
+          "مختصر الخرقي"
+        ],
+        "source_pages": [
+          "71"
+        ],
+        "role": "statement"
+      },
+      {
+        "text": "فإذا بلغ الصبي أو عتق العبد وجب عليه الحج.",
+        "score": 0.937,
+        "source_record_ids": [
+          "khiraqi"
+        ],
+        "source_books": [
+          "مختصر الخرقي"
+        ],
+        "source_pages": [
+          "71"
+        ],
+        "role": "consequence"
+      }
+    ],
+    "rejected_fragments": [
+      "العبد غير مستطيع لأنه لا مال له ومنافعه مستحقة",
+      "مجزئ",
+      "صحيح غير مجزئ عن الفرض",
+      "لا يجب / يصح",
+      "لا يجزئ عن حجة الإسلام"
+    ],
+    "answerability_state": "grounded"
+  },
+  "ranking": [
+    {
+      "record_id": "furu",
+      "accepted": true,
+      "score": 0.991,
+      "rejections": [],
+      "metrics": {
+        "sense_alignment": 1.0,
+        "joint_scope_coverage": 1.0,
+        "evidence_integrity": 0.9673,
+        "scope_centrality": 0.8482,
+        "answerability": 0.9431
+      }
+    },
+    {
+      "record_id": "khiraqi",
+      "accepted": true,
+      "score": 0.99,
+      "rejections": [],
+      "metrics": {
+        "sense_alignment": 1.0,
+        "joint_scope_coverage": 1.0,
+        "evidence_integrity": 0.9599,
+        "scope_centrality": 0.7937,
+        "answerability": 0.9432
+      }
+    },
+    {
+      "record_id": "hidayah",
+      "accepted": true,
+      "score": 0.9803,
+      "rejections": [],
+      "metrics": {
+        "sense_alignment": 0.94,
+        "joint_scope_coverage": 1.0,
+        "evidence_integrity": 0.964,
+        "scope_centrality": 0.7895,
+        "answerability": 0.9378
+      }
+    },
+    {
+      "record_id": "kafi",
+      "accepted": true,
+      "score": 0.962,
+      "rejections": [],
+      "metrics": {
+        "sense_alignment": 0.94,
+        "joint_scope_coverage": 1.0,
+        "evidence_integrity": 0.7586,
+        "scope_centrality": 0.7879,
+        "answerability": 0.9057
+      }
+    },
+    {
+      "record_id": "arafah",
+      "accepted": true,
+      "score": 0.9371,
+      "rejections": [],
+      "metrics": {
+        "sense_alignment": 0.89,
+        "joint_scope_coverage": 1.0,
+        "evidence_integrity": 0.8622,
+        "scope_centrality": 0.6496,
+        "answerability": 0.9144
+      }
+    },
+    {
+      "record_id": "ocr",
+      "accepted": false,
+      "score": 0.8478,
+      "rejections": [
+        "evidence_text_integrity_failed"
+      ],
+      "metrics": {
+        "sense_alignment": 0.88,
+        "joint_scope_coverage": 1.0,
+        "evidence_integrity": 0.4347,
+        "scope_centrality": 0.7876,
+        "answerability": 0.8072
+      }
+    },
+    {
+      "record_id": "homograph",
+      "accepted": false,
+      "score": 0.7149,
+      "rejections": [
+        "contextual_sense_mismatch",
+        "query_concepts_only_distributed_across_unrelated_fields"
+      ],
+      "metrics": {
+        "sense_alignment": 0.27,
+        "joint_scope_coverage": 0.5,
+        "evidence_integrity": 0.4514,
+        "scope_centrality": 0.3872,
+        "answerability": 0.7137
+      }
+    }
+  ],
+  "tiers": {
+    "exact": [
+      "furu",
+      "khiraqi",
+      "hidayah",
+      "kafi"
+    ],
+    "related": [
+      "arafah"
+    ],
+    "distant": [
+      "ocr",
+      "homograph"
+    ]
+  }
+}

hudanet_v38_0_6_generic_tests.json

@@ -0,0 +1,1853 @@
+{
+  "passed": true,
+  "tested": 37,
+  "checks": [
+    {
+      "name": "request type mismatch rejected",
+      "passed": true,
+      "value": null
+    },
+    {
+      "name": "focused source selected",
+      "passed": true,
+      "value": "**Question:** What are the requirements of the process for the group?\n\n**Answer:**\n1. The companion must meet the permanent eligibility rule.  _[Source B]_\n2. conditions.  _[Source B]_\n3. The required expense is borne by the group.  _[Source B]_\n4. If the companion is unavailable, the approved substitute is appointed.  _[Source B]_\n\n**Sources used for the answer:**\n- Source B"
+    },
+    {
+      "name": "atomic propositions extracted",
+      "passed": true,
+      "value": {
+        "request_type": "conditions",
+        "initial_request_type": "conditions",
+        "request_type_scores": {
+          "cause": 0.089408,
+          "ruling": 0.212875,
+          "comparison": 0.208,
+          "evidence": 0.131983,
+          "definition": 0.144,
+          "exception": 0.166043,
+          "timing": 0.117082,
+          "description": 0.32,
+          "principle": 0.195845,
+          "duties": 0.212875,
+          "pillars": 0.095794,
+          "list": 0.523398,
+          "components": 0.149013,
+          "amount": 0.0,
+          "procedure": 0.15327,
+          "validity": 0.174558,
+          "conditions": 0.751435,
+          "location": 0.32,
+          "remedy": 0.166043
+        },
+        "intent_repaired": false,
+        "intent_repair_reason": "",
+        "relation_edges": [
+          [
+            "process",
+            "recipient",
+            "group"
+          ]
+        ],
+        "relation_signature": "process>recipient>group",
+        "subject_terms": [
+          "process",
+          "group"
+        ],
+        "anchor_terms": [
+          "process"
+        ],
+        "qualifier_terms": [
+          "group"
+        ],
+        "critical_terms": [
+          "group"
+        ],
+        "term_document_frequency": {
+          "process": 1.0,
+          "group": 0.5
+        },
+        "term_evidence_support": {
+          "process": 1.0,
+          "group": 0.4227470265645059
+        },
+        "term_weights": {
+          "process": 0.65,
+          "group": 0.651824
+        },
+        "operator_terms": [],
+        "polarity": "affirmative",
+        "accepted": 1,
+        "rejected": 1,
+        "consensus_state": "single_source",
+        "selected_cluster": "conditions",
+        "issue_clusters": {
+          "issue-1": {
+            "books": 1,
+            "weight": 0.988,
+            "principle_strength": 0.368,
+            "relation_alignment": 0.75,
+            "answerability": 0.9335
+          }
+        },
+        "used_record_ids": [
+          "focused"
+        ],
+        "used_book_ids": [
+          "Source B"
+        ],
+        "propositions": [
+          {
+            "text": "The companion must meet the permanent eligibility rule.",
+            "score": 0.9052,
+            "source_record_ids": [
+              "focused"
+            ],
+            "source_books": [
+              "Source B"
+            ],
+            "source_pages": [],
+            "role": "requirement"
+          },
+          {
+            "text": "conditions.",
+            "score": 0.8463,
+            "source_record_ids": [
+              "focused"
+            ],
+            "source_books": [
+              "Source B"
+            ],
+            "source_pages": [],
+            "role": "requirement"
+          },
+          {
+            "text": "The required expense is borne by the group.",
+            "score": 0.9293,
+            "source_record_ids": [
+              "focused"
+            ],
+            "source_books": [
+              "Source B"
+            ],
+            "source_pages": [],
+            "role": "assignment"
+          },
+          {
+            "text": "If the companion is unavailable, the approved substitute is appointed.",
+            "score": 0.8871,
+            "source_record_ids": [
+              "focused"
+            ],
+            "source_books": [
+              "Source B"
+            ],
+            "source_pages": [],
+            "role": "consequence"
+          }
+        ],
+        "rejected_fragments": [],
+        "answerability_state": "grounded"
+      }
+    },
+    {
+      "name": "question preserved verbatim",
+      "passed": true,
+      "value": null
+    },
+    {
+      "name": "source named",
+      "passed": true,
+      "value": null
+    },
+    {
+      "name": "paraphrases clustered",
+      "passed": true,
+      "value": [
+        {
+          "text": "The companion must meet the permanent eligibility rule.",
+          "score": 0.9052,
+          "source_record_ids": [
+            "focused"
+          ],
+          "source_books": [
+            "Source B"
+          ],
+          "source_pages": [],
+          "role": "requirement"
+        },
+        {
+          "text": "conditions.",
+          "score": 0.8463,
+          "source_record_ids": [
+            "focused"
+          ],
+          "source_books": [
+            "Source B"
+          ],
+          "source_pages": [],
+          "role": "requirement"
+        },
+        {
+          "text": "The required expense is borne by the group.",
+          "score": 0.9317,
+          "source_record_ids": [
+            "focused"
+          ],
+          "source_books": [
+            "Source B"
+          ],
+          "source_pages": [],
+          "role": "assignment"
+        },
+        {
+          "text": "If the companion is unavailable, the approved substitute is appointed.",
+          "score": 0.8871,
+          "source_record_ids": [
+            "focused"
+          ],
+          "source_books": [
+            "Source B"
+          ],
+          "source_pages": [],
+          "role": "consequence"
+        }
+      ]
+    },
+    {
+      "name": "boilerplate not displayed",
+      "passed": true,
+      "value": "**Question:** What are the requirements of the process for the group?\n\n**Answer:**\n1. The companion must meet the permanent eligibility rule.  _[Source B]_\n2. A verified permit is required.  _[Source D]_\n3. conditions.  _[Source B، Source D]_\n4. The required expense is borne by the group.  _[Source B]_\n5. If the companion is unavailable, the approved substitute is appointed.  _[Source B]_\n\n**Sources used for the answer:**\n- Source B\n- Source D"
+    },
+    {
+      "name": "incomplete conditional removed",
+      "passed": true,
+      "value": "**Question:** What are the requirements of the process for the group?\n\n**Answer:**\n1. The companion must meet the permanent eligibility rule.  _[Source B]_\n2. conditions.  _[Source B]_\n3. The required expense is borne by the group.  _[Source B]_\n4. If the companion is unavailable, the approved substitute is appointed.  _[Source B]_\n\n**Sources used for the answer:**\n- Source B"
+    },
+    {
+      "name": "rationale excluded from condition list",
+      "passed": true,
+      "value": "**Question:** What are the requirements of the process for the group?\n\n**Answer:**\n1. The companion must meet the permanent eligibility rule.  _[Source B]_\n2. A verified permit is required.  _[Source R]_\n3. conditions.  _[Source B، Source R]_\n4. The required expense is borne by the group.  _[Source B]_\n5. If the companion is unavailable, the approved substitute is appointed.  _[Source B]_\n\n**Sources used for the answer:**\n- Source B\n- Source R"
+    },
+    {
+      "name": "explicit source order preserved",
+      "passed": true,
+      "value": "**Question:** What are the requirements of the process?\n\n**Answer:**\n1. Identity verification.  _[Source O]_\n2. financial capacity.  _[Source O]_\n3. operational readiness.  _[Source O]_\n4. conditions.  _[Source O]_\n\n**Sources used for the answer:**\n- Source O"
+    },
+    {
+      "name": "strong conflict detected",
+      "passed": true,
+      "value": "**Question:** Is the action allowed?\n\n**Answer:** **Result:** The uploaded sources contain strong conflicting outcomes, so they should not be merged into one ruling:\n- The action is prohibited  _[Source G]_\n- The action is permissible  _[Source F]_"
+    },
+    {
+      "name": "conflict sources separated",
+      "passed": true,
+      "value": "**Question:** Is the action allowed?\n\n**Answer:** **Result:** The uploaded sources contain strong conflicting outcomes, so they should not be merged into one ruling:\n- The action is prohibited  _[Source G]_\n- The action is permissible  _[Source F]_"
+    },
+    {
+      "name": "neural score cannot override type gate",
+      "passed": true,
+      "value": {
+        "request_type": "timing",
+        "initial_request_type": "timing",
+        "request_type_scores": {
+          "cause": 0.0,
+          "ruling": 0.0,
+          "comparison": 0.208,
+          "evidence": 0.0,
+          "definition": 0.144,
+          "exception": 0.0,
+          "timing": 0.68,
+          "description": 0.32,
+          "principle": 0.1344,
+          "duties": 0.0,
+          "pillars": 0.0,
+          "list": 0.0,
+          "components": 0.0,
+          "amount": 0.0,
+          "procedure": 0.0,
+          "validity": 0.0,
+          "conditions": 0.0,
+          "location": 0.32,
+          "remedy": 0.0
+        },
+        "intent_repaired": false,
+        "intent_repair_reason": "",
+        "relation_edges": [],
+        "relation_signature": "",
+        "subject_terms": [
+          "process"
+        ],
+        "anchor_terms": [
+          "process"
+        ],
+        "qualifier_terms": [],
+        "critical_terms": [
+          "process"
+        ],
+        "term_document_frequency": {
+          "process": 0.0
+        },
+        "term_evidence_support": {
+          "process": 0.0
+        },
+        "term_weights": {
+          "process": 0.7
+        },
+        "operator_terms": [],
+        "polarity": "affirmative",
+        "accepted": 0,
+        "rejected": 1,
+        "consensus_state": "insufficient",
+        "selected_cluster": "",
+        "issue_clusters": {},
+        "used_record_ids": [],
+        "used_book_ids": [],
+        "propositions": [],
+        "rejected_fragments": [],
+        "answerability_state": "insufficient"
+      }
+    },
+    {
+      "name": "compare mode includes multiple sources",
+      "passed": true,
+      "value": "**Question:** Is the action allowed?\n\n**Answer:**\n\n**Ruling summary:** The action is permissible.  _[Source I، Source J]_\n\n**Sources used for the answer:**\n- Source I\n- Source J"
+    },
+    {
+      "name": "primary mode uses one source",
+      "passed": true,
+      "value": "**Question:** Is the action allowed?\n\n**Answer:**\n\n**Ruling summary:** The action is permissible.  _[Source I]_\n\n**Sources used for the answer:**\n- Source I"
+    },
+    {
+      "name": "arabic contextual roles retained",
+      "passed": true,
+      "value": {
+        "request_type": "conditions",
+        "initial_request_type": "conditions",
+        "request_type_scores": {
+          "cause": 0.0,
+          "ruling": 0.0,
+          "comparison": 0.0,
+          "evidence": 0.0,
+          "definition": 0.0,
+          "exception": 0.176,
+          "timing": 0.0,
+          "description": 0.0,
+          "principle": 0.32,
+          "duties": 0.0,
+          "pillars": 0.144,
+          "list": 0.2624,
+          "components": 0.224,
+          "amount": 0.0,
+          "procedure": 0.0,
+          "validity": 0.1984,
+          "conditions": 0.85856,
+          "location": 0.144,
+          "remedy": 0.0
+        },
+        "intent_repaired": false,
+        "intent_repair_reason": "",
+        "relation_edges": [],
+        "relation_signature": "",
+        "subject_terms": [
+          "موضوع",
+          "فية"
+        ],
+        "anchor_terms": [
+          "فية"
+        ],
+        "qualifier_terms": [
+          "موضوع"
+        ],
+        "critical_terms": [
+          "موضوع"
+        ],
+        "term_document_frequency": {
+          "موضوع": 1.0,
+          "فية": 1.0
+        },
+        "term_evidence_support": {
+          "موضوع": 1.0,
+          "فية": 1.0
+        },
+        "term_weights": {
+          "موضوع": 0.65,
+          "فية": 0.65
+        },
+        "operator_terms": [],
+        "polarity": "affirmative",
+        "accepted": 1,
+        "rejected": 0,
+        "consensus_state": "single_source",
+        "selected_cluster": "conditions",
+        "issue_clusters": {
+          "issue-1": {
+            "books": 1,
+            "weight": 0.984,
+            "principle_strength": 0.336,
+            "relation_alignment": 0.75,
+            "answerability": 0.9113
+          }
+        },
+        "used_record_ids": [
+          "ar-atomic"
+        ],
+        "used_book_ids": [
+          "المصدر العربي"
+        ],
+        "propositions": [
+          {
+            "text": "وجود المتطلب الأول.",
+            "score": 0.8836,
+            "source_record_ids": [
+              "ar-atomic"
+            ],
+            "source_books": [
+              "المصدر العربي"
+            ],
+            "source_pages": [],
+            "role": "requirement"
+          },
+          {
+            "text": "يكون المتطلب الثاني متحققًا.",
+            "score": 0.8649,
+            "source_record_ids": [
+              "ar-atomic"
+            ],
+            "source_books": [
+              "المصدر العربي"
+            ],
+            "source_pages": [],
+            "role": "definition"
+          },
+          {
+            "text": "تتحمل الفئة النفقة اللازمة.",
+            "score": 0.8969,
+            "source_record_ids": [
+              "ar-atomic"
+            ],
+            "source_books": [
+              "المصدر العربي"
+            ],
+            "source_pages": [],
+            "role": "assignment"
+          },
+          {
+            "text": "إذا تعذر الشرط تستعمل الفئة البديل المقرر.",
+            "score": 0.9416,
+            "source_record_ids": [
+              "ar-atomic"
+            ],
+            "source_books": [
+              "المصدر العربي"
+            ],
+            "source_pages": [],
+            "role": "consequence"
+          }
+        ],
+        "rejected_fragments": [
+          "شروط"
+        ],
+        "answerability_state": "grounded"
+      }
+    },
+    {
+      "name": "complete Arabic conditional retained",
+      "passed": true,
+      "value": "**السؤال:** ما شروط الموضوع للفئة؟\n\n**الإجابة:**\n1. وجود المتطلب الأول.  _[المصدر العربي]_\n2. يكون المتطلب الثاني متحققًا.  _[المصدر العربي]_\n3. تتحمل الفئة النفقة اللازمة.  _[المصدر العربي]_\n4. إذا تعذر الشرط تستعمل الفئة البديل المقرر.  _[المصدر العربي]_\n\n**المصادر التي بُني عليها الجواب:**\n- المصدر العربي"
+    },
+    {
+      "name": "evidence conditioned operator pruning",
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+        "intent_repair_reason": "",
+        "relation_edges": [],
+        "relation_signature": "",
+        "subject_terms": [
+          "عمل",
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+          "تصريح"
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+        ],
+        "qualifier_terms": [
+          "عمل"
+        ],
+        "critical_terms": [
+          "تصريح",
+          "عمل"
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+        "term_document_frequency": {
+          "عمل": 0.5,
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+          "تصريح": 0.5
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+        "term_weights": {
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+          "تصريح": 0.752558
+        },
+        "operator_terms": [
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+          }
+        },
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+          "operator-relevant"
+        ],
+        "used_book_ids": [
+          "المصدر هـ"
+        ],
+        "propositions": [
+          {
+            "text": "لا يجوز تنفيذها بدونه.",
+            "score": 0.8999,
+            "source_record_ids": [
+              "operator-relevant"
+            ],
+            "source_books": [
+              "المصدر هـ"
+            ],
+            "source_pages": [],
+            "role": "prohibition"
+          },
+          {
+            "text": "إذا لم يوجد التصريح للفئة فلا تلزمها العملية.",
+            "score": 0.9279,
+            "source_record_ids": [
+              "operator-relevant"
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+            "source_books": [
+              "المصدر هـ"
+            ],
+            "source_pages": [],
+            "role": "consequence"
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+        ],
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+          "حكم"
+        ],
+        "answerability_state": "grounded"
+      }
+    },
+    {
+      "name": "multi signal semantic rescue accepts focused source",
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+        "intent_repair_reason": "",
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+            "source_books": [
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+            "source_pages": [],
+            "role": "prohibition"
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+          {
+            "text": "إذا لم يوجد التصريح للفئة فلا تلزمها العملية.",
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+            "source_record_ids": [
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+              "المصدر هـ"
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+            "source_pages": [],
+            "role": "consequence"
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+        ],
+        "rejected_fragments": [
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+          "حكم"
+        ],
+        "answerability_state": "grounded"
+      }
+    },
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+      "name": "high dense distractor remains rejected",
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+        "propositions": [
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+            "source_record_ids": [
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+            "source_books": [
+              "المصدر هـ"
+            ],
+            "source_pages": [],
+            "role": "prohibition"
+          },
+          {
+            "text": "إذا لم يوجد التصريح للفئة فلا تلزمها العملية.",
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+            "source_record_ids": [
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+            "role": "consequence"
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+        "answerability_state": "grounded"
+      }
+    },
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+            "source_record_ids": [
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+            "source_books": [
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+            "role": "principle"
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+          {
+            "text": "إذا فعل ذلك انصرف العمل إلى نفسه ولم يجزئ عن الغير.",
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+            "role": "consequence"
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+        "rejected_fragments": [
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+          "ينفذ عن صاحبه",
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+            "weight": 2.336,
+            "principle_strength": 0.5633,
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+          "principle-rule-2"
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+        "used_book_ids": [
+          "كتاب القاعدة الأول",
+          "كتاب القاعدة الثاني"
+        ],
+        "propositions": [
+          {
+            "text": "الأصل أن يبدأ بنفسه أولا.",
+            "score": 0.8887,
+            "source_record_ids": [
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+            "source_books": [
+              "كتاب القاعدة الأول"
+            ],
+            "source_pages": [],
+            "role": "principle"
+          },
+          {
+            "text": "من لم ينفذ عن نفسه لا ينفذ عن غيره.",
+            "score": 0.8846,
+            "source_record_ids": [
+              "principle-rule-1"
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+              "كتاب القاعدة الأول"
+            ],
+            "source_pages": [],
+            "role": "principle"
+          },
+          {
+            "text": "لا يصح أن يقدم عمل غيره على عمل نفسه الواجب.",
+            "score": 0.8371,
+            "source_record_ids": [
+              "principle-rule-2"
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+            "source_books": [
+              "كتاب القاعدة الثاني"
+            ],
+            "source_pages": [],
+            "role": "principle"
+          },
+          {
+            "text": "إذا فعل ذلك انصرف العمل إلى نفسه ولم يجزئ عن الغير.",
+            "score": 0.824,
+            "source_record_ids": [
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+              "كتاب القاعدة الثاني"
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+            "source_pages": [],
+            "role": "consequence"
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+        ],
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+          "شرط النيابة",
+          "لا ينفذ عن أحد حتى ينفذ عن نفسه",
+          "ينفذ عن صاحبه",
+          "يبدأ بنفسه",
+          "لا يصح عن الغير"
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+    },
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+      "name": "dominant rule sub-issue selected",
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+            "تنفيذ"
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+            "عمل",
+            "on_behalf_of",
+            "غير"
+          ]
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+        "qualifier_terms": [
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+        ],
+        "critical_terms": [
+          "عمل"
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+          "عمل": 0.6666666666666666,
+          "غير": 1.0
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+          "عمل": 0.6975998006619147,
+          "غير": 1.0
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+          "عمل": 0.675947,
+          "غير": 0.65
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+        "operator_terms": [],
+        "polarity": "affirmative",
+        "accepted": 4,
+        "rejected": 2,
+        "consensus_state": "agreement",
+        "selected_cluster": "issue-1",
+        "issue_clusters": {
+          "issue-1": {
+            "books": 3,
+            "weight": 2.336,
+            "principle_strength": 0.5633,
+            "relation_alignment": 0.8233,
+            "answerability": 0.8293
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+          "issue-2": {
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+            "weight": 0.655,
+            "principle_strength": 0.738,
+            "relation_alignment": 0.735,
+            "answerability": 0.8049
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+        },
+        "used_record_ids": [
+          "principle-rule-1",
+          "principle-rule-2"
+        ],
+        "used_book_ids": [
+          "كتاب القاعدة الأول",
+          "كتاب القاعدة الثاني"
+        ],
+        "propositions": [
+          {
+            "text": "الأصل أن يبدأ بنفسه أولا.",
+            "score": 0.8887,
+            "source_record_ids": [
+              "principle-rule-1"
+            ],
+            "source_books": [
+              "كتاب القاعدة الأول"
+            ],
+            "source_pages": [],
+            "role": "principle"
+          },
+          {
+            "text": "من لم ينفذ عن نفسه لا ينفذ عن غيره.",
+            "score": 0.8846,
+            "source_record_ids": [
+              "principle-rule-1"
+            ],
+            "source_books": [
+              "كتاب القاعدة الأول"
+            ],
+            "source_pages": [],
+            "role": "principle"
+          },
+          {
+            "text": "لا يصح أن يقدم عمل غيره على عمل نفسه الواجب.",
+            "score": 0.8371,
+            "source_record_ids": [
+              "principle-rule-2"
+            ],
+            "source_books": [
+              "كتاب القاعدة الثاني"
+            ],
+            "source_pages": [],
+            "role": "principle"
+          },
+          {
+            "text": "إذا فعل ذلك انصرف العمل إلى نفسه ولم يجزئ عن الغير.",
+            "score": 0.824,
+            "source_record_ids": [
+              "principle-rule-2"
+            ],
+            "source_books": [
+              "كتاب القاعدة الثاني"
+            ],
+            "source_pages": [],
+            "role": "consequence"
+          }
+        ],
+        "rejected_fragments": [
+          "شرط النيابة",
+          "لا ينفذ عن أحد حتى ينفذ عن نفسه",
+          "ينفذ عن صاحبه",
+          "يبدأ بنفسه",
+          "لا يصح عن الغير"
+        ],
+        "answerability_state": "grounded"
+      }
+    },
+    {
+      "name": "principle answer uses complementary grounded sources",
+      "passed": true,
+      "value": "**السؤال:** ما هي القاعدة في تنفيذ العملية عن الغير؟\n\n**الإجابة:**\n\n**القاعدة:** من لم ينفذ عن نفسه لا ينفذ عن غيره.  _[كتاب القاعدة الأول]_\n\n**وعند مخالفة القاعدة:**\n- إذا فعل ذلك انصرف العمل إلى نفسه ولم يجزئ عن الغير.  _[كتاب القاعدة الثاني]_\n\n**التفصيل أو الدليل:**\n- الأصل أن يبدأ بنفسه أولا.  _[كتاب القاعدة الأول]_\n- لا يصح أن يقدم عمل غيره على عمل نفسه الواجب.  _[كتاب القاعدة الثاني]_\n\n**المصادر التي بُني عليها الجواب:**\n- كتاب القاعدة الثاني\n- كتاب القاعدة الأول"
+    },
+    {
+      "name": "principle answer renders violation consequence",
+      "passed": true,
+      "value": "**السؤال:** ما هي القاعدة في تنفيذ العملية عن الغير؟\n\n**الإجابة:**\n\n**القاعدة:** من لم ينفذ عن نفسه لا ينفذ عن غيره.  _[كتاب القاعدة الأول]_\n\n**وعند مخالفة القاعدة:**\n- إذا فعل ذلك انصرف العمل إلى نفسه ولم يجزئ عن الغير.  _[كتاب القاعدة الثاني]_\n\n**التفصيل أو الدليل:**\n- الأصل أن يبدأ بنفسه أولا.  _[كتاب القاعدة الأول]_\n- لا يصح أن يقدم عمل غيره على عمل نفسه الواجب.  _[كتاب القاعدة الثاني]_\n\n**المصادر التي بُني عليها الجواب:**\n- كتاب القاعدة الثاني\n- كتاب القاعدة الأول"
+    },
+    {
+      "name": "exact tier requires answer contribution",
+      "passed": true,
+      "value": {
+        "exact": [
+          "principle-rule-1"
+        ],
+        "used": [
+          "principle-rule-1",
+          "principle-rule-2"
+        ]
+      }
+    },
+    {
+      "name": "insufficient answer cannot retain extreme confidence",
+      "passed": true,
+      "value": null
+    },
+    {
+      "name": "specific ruling outranks generic what-is shell",
+      "passed": true,
+      "value": {
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+        "initial_request_type": "ruling",
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+          "procedure": 0.102406,
+          "validity": 0.220981,
+          "conditions": 0.114722,
+          "location": 0.027781,
+          "remedy": 0.210201
+        },
+        "intent_repaired": false,
+        "intent_repair_reason": "",
+        "relation_edges": [],
+        "relation_signature": "",
+        "subject_terms": [
+          "تنفيذ",
+          "عامل",
+          "عمل"
+        ],
+        "anchor_terms": [
+          "عامل",
+          "عمل"
+        ],
+        "qualifier_terms": [
+          "تنفيذ"
+        ],
+        "critical_terms": [
+          "تنفيذ"
+        ],
+        "term_document_frequency": {
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+          "عامل": 1.0,
+          "عمل": 0.75
+        },
+        "term_evidence_support": {
+          "تنفيذ": 0.7575716740265707,
+          "عامل": 1.0,
+          "عمل": 0.7485815138420883
+        },
+        "term_weights": {
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+          "عامل": 0.65,
+          "عمل": 0.662074
+        },
+        "operator_terms": [],
+        "polarity": "affirmative",
+        "accepted": 2,
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+        "consensus_state": "mixed",
+        "selected_cluster": "not_obligatory+valid",
+        "issue_clusters": {
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+            "books": 1,
+            "weight": 0.9899,
+            "principle_strength": 0.562,
+            "relation_alignment": 0.75,
+            "answerability": 0.9438
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+          "issue-2": {
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+            "weight": 0.8133,
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+            "answerability": 0.8255
+          }
+        },
+        "used_record_ids": [
+          "sense-main",
+          "sense-supplement"
+        ],
+        "used_book_ids": [
+          "كتاب الحكم المركزي",
+          "كتاب الحكم المكمل"
+        ],
+        "propositions": [
+          {
+            "text": "لا يجب تنفيذ العامل للعملية، لكنه يصح منه إذا فعله.",
+            "score": 0.9631,
+            "source_record_ids": [
+              "sense-main"
+            ],
+            "source_books": [
+              "كتاب الحكم المركزي"
+            ],
+            "source_pages": [],
+            "role": "statement"
+          },
+          {
+            "text": "يصح التنفيذ، لكنه لا يجزئ عن الالتزام الأصلي.",
+            "score": 0.8179,
+            "source_record_ids": [
+              "sense-supplement"
+            ],
+            "source_books": [
+              "كتاب الحكم المكمل"
+            ],
+            "source_pages": [],
+            "role": "consequence"
+          }
+        ],
+        "rejected_fragments": [
+          "لا يجب / يصح",
+          "صحيح غير مجزئ"
+        ],
+        "answerability_state": "grounded"
+      }
+    },
+    {
+      "name": "contextual homograph rejected",
+      "passed": true,
+      "value": {
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+        "initial_request_type": "ruling",
+        "request_type_scores": {
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+          "ruling": 0.840689,
+          "comparison": 0.113811,
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+          "definition": 0.078792,
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+          "timing": 0.148219,
+          "description": 0.576566,
+          "principle": 0.24793,
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+          "pillars": 0.0,
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+          "conditions": 0.114722,
+          "location": 0.027781,
+          "remedy": 0.210201
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+        "intent_repaired": false,
+        "intent_repair_reason": "",
+        "relation_edges": [],
+        "relation_signature": "",
+        "subject_terms": [
+          "تنفيذ",
+          "عامل",
+          "عمل"
+        ],
+        "anchor_terms": [
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+          "عمل"
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+        "qualifier_terms": [
+          "تنفيذ"
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+        "critical_terms": [
+          "تنفيذ"
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+        "term_document_frequency": {
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+          "عامل": 1.0,
+          "عمل": 0.75
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+          "عامل": 0.65,
+          "عمل": 0.662074
+        },
+        "operator_terms": [],
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+        "selected_cluster": "not_obligatory+valid",
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+            "weight": 0.9899,
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+            "weight": 0.8133,
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+            "answerability": 0.8255
+          }
+        },
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+        ],
+        "used_book_ids": [
+          "كتاب الحكم المركزي",
+          "كتاب الحكم المكمل"
+        ],
+        "propositions": [
+          {
+            "text": "لا يجب تنفيذ العامل للعملية، لكنه يصح منه إذا فعله.",
+            "score": 0.9631,
+            "source_record_ids": [
+              "sense-main"
+            ],
+            "source_books": [
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+            ],
+            "source_pages": [],
+            "role": "statement"
+          },
+          {
+            "text": "يصح التنفيذ، لكنه لا يجزئ عن الالتزام الأصلي.",
+            "score": 0.8179,
+            "source_record_ids": [
+              "sense-supplement"
+            ],
+            "source_books": [
+              "كتاب الحكم المكمل"
+            ],
+            "source_pages": [],
+            "role": "consequence"
+          }
+        ],
+        "rejected_fragments": [
+          "لا يجب / يصح",
+          "صحيح غير مجزئ"
+        ],
+        "answerability_state": "grounded"
+      }
+    },
+    {
+      "name": "broken OCR rejected",
+      "passed": true,
+      "value": {
+        "request_type": "ruling",
+        "initial_request_type": "ruling",
+        "request_type_scores": {
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+          "ruling": 0.840689,
+          "comparison": 0.113811,
+          "evidence": 0.167083,
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+          "exception": 0.210201,
+          "timing": 0.148219,
+          "description": 0.576566,
+          "principle": 0.24793,
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+          "pillars": 0.0,
+          "list": 0.084789,
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+          "conditions": 0.114722,
+          "location": 0.027781,
+          "remedy": 0.210201
+        },
+        "intent_repaired": false,
+        "intent_repair_reason": "",
+        "relation_edges": [],
+        "relation_signature": "",
+        "subject_terms": [
+          "تنفيذ",
+          "عامل",
+          "عمل"
+        ],
+        "anchor_terms": [
+          "عامل",
+          "عمل"
+        ],
+        "qualifier_terms": [
+          "تنفيذ"
+        ],
+        "critical_terms": [
+          "تنفيذ"
+        ],
+        "term_document_frequency": {
+          "تنفيذ": 0.75,
+          "عامل": 1.0,
+          "عمل": 0.75
+        },
+        "term_evidence_support": {
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+          "عامل": 1.0,
+          "عمل": 0.7485815138420883
+        },
+        "term_weights": {
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+          "عامل": 0.65,
+          "عمل": 0.662074
+        },
+        "operator_terms": [],
+        "polarity": "affirmative",
+        "accepted": 2,
+        "rejected": 2,
+        "consensus_state": "mixed",
+        "selected_cluster": "not_obligatory+valid",
+        "issue_clusters": {
+          "issue-1": {
+            "books": 1,
+            "weight": 0.9899,
+            "principle_strength": 0.562,
+            "relation_alignment": 0.75,
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+          },
+          "issue-2": {
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+            "weight": 0.8133,
+            "principle_strength": 0.566,
+            "relation_alignment": 0.75,
+            "answerability": 0.8255
+          }
+        },
+        "used_record_ids": [
+          "sense-main",
+          "sense-supplement"
+        ],
+        "used_book_ids": [
+          "كتاب الحكم المركزي",
+          "كتاب الحكم المكمل"
+        ],
+        "propositions": [
+          {
+            "text": "لا يجب تنفيذ العامل للعملية، لكنه يصح منه إذا فعله.",
+            "score": 0.9631,
+            "source_record_ids": [
+              "sense-main"
+            ],
+            "source_books": [
+              "كتاب الحكم المركزي"
+            ],
+            "source_pages": [],
+            "role": "statement"
+          },
+          {
+            "text": "يصح التنفيذ، لكنه لا يجزئ عن الالتزام الأصلي.",
+            "score": 0.8179,
+            "source_record_ids": [
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+            ],
+            "source_books": [
+              "كتاب الحكم المكمل"
+            ],
+            "source_pages": [],
+            "role": "consequence"
+          }
+        ],
+        "rejected_fragments": [
+          "لا يجب / يصح",
+          "صحيح غير مجزئ"
+        ],
+        "answerability_state": "grounded"
+      }
+    },
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+}

hudanet_v38_0_6_manifest.json

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hudanet_v38_0_6_validation_report.json

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