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    "caution": "All audit labels belong to the previously researched training data. Hidden AUC remains unknown."
  },
  "stages": [
    {
      "id": "baseline",
      "date": "23-sentabr",
      "title": "Oddiy belgilardan birinchi ansamblgacha",
      "question": "Tranzaksiyalar soni va oddiy o‘rtachalar eskalatsiyani ajrata oladimi?",
      "method": "Logistik regressiya, LightGBM va CatBoost. 1–180 kunlik oynalar, tur × yo‘nalish kesimlari, kvantillar va indeks taqsimoti: 964 belgi.",
      "finding": "Oddiy logistik model OOF AUC 0.565706 berdi. Taqsimot belgilariga o‘tib, 50% CatBoost + 50% LightGBM ansambli 0.652096 ga yetdi.",
      "decision": "Faollik sonidan tashqari miqdorning taqsimotini o‘rganishga o‘tdik.",
      "protocol": "4-fold · 11 200 development · tanlash uchun ishlatilgan",
      "source": "reports/metrics.json",
      "evidence": "metrics.json",
      "score": 0.652096170207425,
      "status": "Birinchi model"
    },
    {
      "id": "model_v2",
      "date": "23-sentabr",
      "title": "Nisbiy belgilar va Model v2",
      "question": "Signal ichidagi nisbatlar va taqsimot kontrastlari foyda beradimi?",
      "method": "3 109 belgi yaratildi; 22 screening konfiguratsiyasidan 7 finalist. Yakuniy 25% LightGBM + 75% CatBoost, har biri uch seed.",
      "finding": "Bir xil 5 foldda eski ansambl 0.646270, v2 0.658484. Avval ko‘rilgan holdout: 0.627317 → 0.635409.",
      "decision": "Nisbiy miqdorlar foydali. Keyingi usullarda parametr tanlashni ichki validatsiyaga ko‘chirdik.",
      "protocol": "5-fold · 11 200 · tarixiy outer early stopping",
      "source": "reports/model_v2/metrics.json",
      "evidence": "model_v2/analysis.md",
      "score": 0.6584843501078947,
      "status": "Rivojlanish"
    },
    {
      "id": "feature_research",
      "date": "23-sentabr",
      "title": "786 yangi belgi va ablation",
      "question": "Taqsimot, ketma-ketlik yoki vaqt belgilaridan qaysi biri foydali?",
      "method": "Har belgi oilasi alohida qo‘shildi. Bir xil fixed CatBoost nazorati va 7 konfiguratsiya taqqoslandi.",
      "finding": "Taqsimotlar qo‘shilganda 0.655096; nazorat 0.654180. Barcha yangi belgilarni birdan qo‘shish 0.651972 berdi.",
      "decision": "Ko‘p belgi avtomatik ustunlik bermadi; ixcham statistikalar yo‘nalishi saqlandi.",
      "protocol": "5-fold · 11 200 · fixed daraxt soni",
      "source": "reports/feature_research/comparison.csv",
      "evidence": "feature_research/results.md",
      "key": "add_distribution",
      "status": "Ablation",
      "score": 0.6550961381572636
    },
    {
      "id": "pattern_research",
      "date": "24-sentabr",
      "title": "Patternni modelning o‘zi o‘rgansin",
      "question": "XGBoost, Deep Sets va TabPFN qo‘lda tuzilgan belgilardan ustunmi?",
      "method": "Ixcham statistikalar, density belgilar, tartibsiz tranzaksiya to‘plamlari; 10 yangi konfiguratsiya va ichki validatsiyali v2 nazorati.",
      "finding": "Ixcham CatBoost 0.656018, XGBoost 0.654065, TabPFN 0.651883–0.653303. V2 + XGBoost 50/50: 0.659945.",
      "decision": "Blending o‘sishi intervali nolni qamradi. Ichki validatsiyali v2 0.650789 bo‘lgani protokol farqini ko‘rsatdi.",
      "protocol": "5-fold · 11 200 · yangi modellarda inner selection",
      "source": "reports/pattern_research/comparison.csv",
      "evidence": "pattern_research/results.md",
      "key": "blend50_xgb_summary",
      "status": "Taqqoslash",
      "score": 0.659944649595361
    },
    {
      "id": "sequence_research",
      "date": "24-sentabr",
      "title": "Vaqt ketma-ketligi va tushuntiriladigan qoidalar",
      "question": "Kunlik tranzaksiya ketma-ketligi qo‘shimcha signal saqlaydimi?",
      "method": "MiniRocket + Ridge/boosting, temporal CNN, additiv EBM va juftlik EBM; epoch va kuch parametrlarini training ichida tanlash.",
      "finding": "MiniRocket daily hybrid 0.649051; temporal CNN + summary 0.640699; EBM additiv 0.650118.",
      "decision": "Shu konfiguratsiyalarda ketma-ketlik modellari statistik ansamblni oshirmadi.",
      "protocol": "5-fold · 11 200 · inner selection",
      "source": "reports/sequence_research/rules_experiments.json",
      "evidence": "sequence_research/verification.json",
      "key": "ebm_additive",
      "status": "Ketma-ketlik",
      "score": 0.6501175770257271
    },
    {
      "id": "dataset_research",
      "date": "24-sentabr",
      "title": "Datasetga mos 225 statistikaga qaytish",
      "question": "Har signal ichida standartlashtirilgan miqdorlar barqaror pattern beradimi?",
      "method": "8 ta tur × yo‘nalish guruhi + umumiy guruh. Kvantillar, o‘rtacha, tarqoqlik va ulushlar; copula, spline va dependence variantlari.",
      "finding": "225 belgili fixed CatBoost 0.659046; 452 belgili dependence variant 0.660463. Karta chiqimi va naqd kirim patternlari choraklar kesimida kuzatildi.",
      "decision": "Ixcham, takrorlanadigan feature pipeline keyingi ansamblning asosi bo‘ldi.",
      "protocol": "5-fold · 11 200 · fixed/inner selection",
      "source": "reports/dataset_research/comparison.csv",
      "evidence": "dataset_research/results.md",
      "key": "summary_dependence_fixed",
      "status": "Asosiy topilma",
      "score": 0.6604631674165956
    },
    {
      "id": "deeper_research",
      "date": "25-sentabr",
      "title": "Attention, embedding va qoldiq xatolar",
      "question": "Murakkabroq representation yoki xatoni tuzatish qolgan signalni topadimi?",
      "method": "14 yangi variant: attention MIL, L-moment, taqsimot embeddingi, shrinkage, mixture, vaqt cutoffi, cross-fitted residual.",
      "finding": "Eng yaxshi yangi yakka model — residual summary 0.659906. Attention summary 0.641370; oldingi 0.660463 oshmadi.",
      "decision": "Summary statistikalari saqlandi. Sinovdan o‘tgan murakkablikning o‘zi foyda bermadi.",
      "protocol": "5-fold · 11 200 · inner/cross-fitted transforms",
      "source": "reports/deeper_research/comparison.csv",
      "evidence": "deeper_research/results.md",
      "key": "residual_summary",
      "status": "14 variant",
      "score": 0.6599063518937387
    },
    {
      "id": "auc_frontier",
      "date": "25-sentabr",
      "title": "Learning curve, ranking loss va TabM",
      "question": "Ko‘proq label, uzunroq tarix yoki boshqa loss yordam beradimi?",
      "method": "12 konfiguratsiya × 5 fold × 3 seed; PairLogit, balanslash, history resampling. Keyin 225/452 belgili TabM sinovlari.",
      "finding": "Yarim training subsetlari ansambli 0.660996. TabM 0.643791 / 0.645877; ranking loss 0.652800.",
      "decision": "To‘liq tarix miqdor taqsimotini aniqroq baholaydi. Kichik OOF o‘sishi ustunlikni tasdiqlamadi.",
      "protocol": "5-fold · 11 200 · 3 seed · TabM inner epochs",
      "source": "reports/auc_frontier/comparison.csv",
      "evidence": "auc_frontier/results.md",
      "key": "label0500",
      "status": "Barqarorlik",
      "score": 0.6609962815088947
    },
    {
      "id": "synthetic_research",
      "date": "25-sentabr",
      "title": "Sun’iy ko‘paytirish va RealMLP",
      "question": "Training ichida ko‘paytirish yoki MLP foydali bo‘ladimi?",
      "method": "13 augmentation/nazorat: nusxalash, oversampling, SMOTE, kNN, copula, history resampling; RealMLP-TD-S.",
      "finding": "Nusxalash 0.660851, yangi synthetic nuqtalar ichida history mild 0.660519. SMOTE 0.646226 / 0.637018; RealMLP 0.621629.",
      "decision": "Yakuniy treningga synthetic competition qatorlari qo‘shilmadi. Ular yangi mustaqil label bermaydi.",
      "protocol": "5-fold · 11 200 · augmentation faqat trainingda",
      "source": "reports/synthetic_research/comparison.csv",
      "evidence": "synthetic_research/results.md",
      "key": "repeat100",
      "status": "Nazorat tajribasi",
      "score": 0.6608513834015127
    },
    {
      "id": "geometry_research",
      "date": "25-sentabr",
      "title": "O‘lchov noaniqligi va Gaussian komponent",
      "question": "Kam tranzaksiyali signalning statistikasi noaniqroq ekanini hisobga olsak-chi?",
      "method": "281 nisbiy belgi, 8 konfiguratsiya, geometry va measurement-error Gaussian; uch-seed CatBoost bilan fixed 75/25 blend.",
      "finding": "CatBoost nazorati 0.660108. Gaussian yolg‘iz 0.651390, CatBoost bilan aralashma 0.661510.",
      "decision": "Turli xatolar qiladigan statistik komponent ansamblga kiritildi; kichik o‘sish intervali nolni qamradi.",
      "protocol": "5-fold · 11 200 · Gaussian mixing inner-only",
      "source": "reports/geometry_research/comparison.csv",
      "evidence": "geometry_research/results.md",
      "key": "blend_uncertain",
      "status": "Ansambl",
      "score": 0.6615097004408129
    },
    {
      "id": "context_research",
      "date": "25-sentabr",
      "title": "TabR va to‘liq kontekstli TabICLv2",
      "question": "Boshqa training signallari konteksti qo‘shimcha foyda beradimi?",
      "method": "TabR retrieval va TabICLv2, 99/225 belgi. TabICLv2 har foldda barcha 8 960 training signalidan foydalanadi; 8 estimator, fine-tuning yo‘q.",
      "finding": "TabICLv2 225 belgi bilan 0.660463. Oldingi ansambl 75% + TabICLv2 25%: 0.662776, 4/5 fold g‘alabasi.",
      "decision": "Topshirish uchun shu fixed ansambl olindi: 56.25% CatBoost + 18.75% Gaussian + 25% TabICLv2. O‘sishning 95% intervali [−0.000059; +0.002645].",
      "protocol": "5-fold · 11 200 · faqat training konteksti",
      "source": "reports/context_research/comparison.csv",
      "evidence": "context_research/results.md",
      "key": "blend_tabicl_summary",
      "status": "Tanlangan ansambl",
      "score": 0.6627759339596337
    },
    {
      "id": "deconvolution_research",
      "date": "26-sentabr",
      "title": "XD va mixture discriminant analysis",
      "question": "O‘lchov xatosi bilan birga bir nechta yashirin taqsimot kerakmi?",
      "method": "8 konfiguratsiya, 40 fold fit: extreme deconvolution va tied-covariance MDA; 8 oldindan belgilangan blend.",
      "finding": "Eng yaxshi yangi blend 0.662442. Tanlangan 0.662776 natija yaxshilanmadi.",
      "decision": "Amaldagi ansambl saqlandi va barcha 14 000 original signalda refit qilindi.",
      "protocol": "5-fold · 11 200 · fixed blends",
      "source": "reports/deconvolution_research/comparison.csv",
      "evidence": "deconvolution_research/results.md",
      "key": "blend_mda1_normal",
      "status": "Keyingi sinov",
      "score": 0.6624422648840501
    },
    {
      "id": "behavior_research",
      "date": "26-sentabr",
      "title": "Adaptiv scan, path signature va TF-IDF",
      "question": "Qisqa portlashlar va hodisalar ketma-ketligi qo‘shimcha foyda beradimi?",
      "method": "280 adaptiv scan + 480 path-signature belgi; training ichida TF-IDF/SVD; seedlar bilan follow-up.",
      "finding": "Bir seedli eng yuqori blend 0.662786 (+0.000010). Uch seedli tekshiruv 0.662608 bo‘ldi.",
      "decision": "Yaxshilanish barqaror saqlanmadi. Bir seedning eng baland raqami asosida yakuniy model almashtirilmadi.",
      "protocol": "5-fold · 11 200 · 1 seed + 3 seed follow-up",
      "source": "reports/behavior_research/comparison.csv",
      "evidence": "behavior_research/results.md",
      "key": "fixed25_summary_grammar_svd_scan_amount",
      "status": "Yutuq tasdiqlanmadi",
      "score": 0.6627857395160112
    },
    {
      "id": "evidence_research",
      "date": "26-sentabr",
      "title": "Bitta, juftlik va uchlik risk belgilar",
      "question": "Bir nechta zaif patternning qo‘shma ehtimoli foydali ustun bo‘ladimi?",
      "method": "33 alohida, 28 juftlik va 1 uchlik: 62 risk belgi. Targetga tayangan encoder har outer training ichida cross-fit qilindi; 6 variant.",
      "finding": "Yangi bitta qo‘shma ustun 0.624126. Eng yaxshi yangi fixed blend 0.662223.",
      "decision": "Tanlangan ansambl yaxshilanmadi. Target encoderdan leakage bo‘lmasligi alohida tekshirildi.",
      "protocol": "5-fold · 11 200 · ichki cross-fitting",
      "source": "reports/evidence_research/comparison.csv",
      "evidence": "evidence_research/results.md",
      "key": "fixed25_summary_evidence_main",
      "status": "Yakuniy ablation",
      "score": 0.6622226204211974
    }
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      "date": "23-sentabr",
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      "auc": 0.5657056044750094,
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      "source": "reports/experiments.json",
      "selected": false
    },
    {
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      "stage_title": "Oddiy belgilardan birinchi ansamblgacha",
      "date": "23-sentabr",
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      "auc": 0.5594379208544797,
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      "source": "reports/experiments.json",
      "selected": false
    },
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      "stage": "baseline",
      "stage_title": "Oddiy belgilardan birinchi ansamblgacha",
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      "selected": false
    },
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      "stage_title": "Oddiy belgilardan birinchi ansamblgacha",
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    },
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      "stage": "model_v2",
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