{
  "approach": "Test whether adding jump-distribution tails improves B's passive-order sizing. Replace only the NODES assignment with eight symmetric nodes: retain the original six locations, multiply their weights by 1 - 2*wt, and add nodes at -3.5 and +3.5 sigma with weight wt each. Compare wt=0.01, 0.02, and 0.04 against the exact pinned B on all 500 tuning seeds, select wt=0.01 by the largest paired tuning mean, freeze its exact source, and evaluate it against B on all 1000 disjoint holdout seeds without revision.",
  "tried": [
    {
      "variant": "tail_001",
      "constants": {
        "wt_per_tail_node": 0.01,
        "core_weight_multiplier": 0.98,
        "nodes": [
          [
            -3.5,
            0.01
          ],
          [
            -2.2,
            0.0588
          ],
          [
            -1.3,
            0.1666
          ],
          [
            -0.6,
            0.2646
          ],
          [
            0.6,
            0.2646
          ],
          [
            1.3,
            0.1666
          ],
          [
            2.2,
            0.0588
          ],
          [
            3.5,
            0.01
          ]
        ]
      },
      "code_sha256": "0b381ec04d77e85a9f2b04d03faee7cd82d1bc8c1a77e71acc79d1ffe44fb15d",
      "T": {
        "seeds": "110000..110499",
        "n": 500,
        "complete": true,
        "failures": [],
        "mean_diff": -0.2577136084328005,
        "se": 0.15055511846411382,
        "sum_d": -128.85680421640026,
        "sum_d2": 5688.585654073692,
        "candidate_mean": 95.2276549429414,
        "incumbent_mean": 95.4853685513742,
        "retail_diff": -0.4833398470043993,
        "retail_se": 0.13122311664200798,
        "arb_diff": 0.22562623857619973,
        "arb_se": 0.06219022358934055
      }
    },
    {
      "variant": "tail_002",
      "constants": {
        "wt_per_tail_node": 0.02,
        "core_weight_multiplier": 0.96,
        "nodes": [
          [
            -3.5,
            0.02
          ],
          [
            -2.2,
            0.0576
          ],
          [
            -1.3,
            0.1632
          ],
          [
            -0.6,
            0.2592
          ],
          [
            0.6,
            0.2592
          ],
          [
            1.3,
            0.1632
          ],
          [
            2.2,
            0.0576
          ],
          [
            3.5,
            0.02
          ]
        ]
      },
      "code_sha256": "d74ca201b875abaaf5dc3ec8cb2b3d8575dbf5d29fd19f92b005b3955a66cf43",
      "T": {
        "seeds": "110000..110499",
        "n": 500,
        "complete": true,
        "failures": [],
        "mean_diff": -0.44677604108720004,
        "se": 0.20207112152370305,
        "sum_d": -223.38802054360002,
        "sum_d2": 10287.572584829642,
        "candidate_mean": 95.03859251028699,
        "incumbent_mean": 95.4853685513742,
        "retail_diff": -0.8860155189697991,
        "retail_se": 0.17341530461136676,
        "arb_diff": 0.4392394778821999,
        "arb_se": 0.10198242346437694
      }
    },
    {
      "variant": "tail_004",
      "constants": {
        "wt_per_tail_node": 0.04,
        "core_weight_multiplier": 0.92,
        "nodes": [
          [
            -3.5,
            0.04
          ],
          [
            -2.2,
            0.0552
          ],
          [
            -1.3,
            0.1564
          ],
          [
            -0.6,
            0.2484
          ],
          [
            0.6,
            0.2484
          ],
          [
            1.3,
            0.1564
          ],
          [
            2.2,
            0.0552
          ],
          [
            3.5,
            0.04
          ]
        ]
      },
      "code_sha256": "2ee3406fa5ab5d9243060717ee6666720e9e42357a821910ac186fb3d04614d2",
      "T": {
        "seeds": "110000..110499",
        "n": 500,
        "complete": true,
        "failures": [],
        "mean_diff": -0.43150018407500035,
        "se": 0.28720661655149693,
        "sum_d": -215.75009203750017,
        "sum_d2": 20673.762531872548,
        "candidate_mean": 95.0538683672992,
        "incumbent_mean": 95.4853685513742,
        "retail_diff": -1.3532421379923996,
        "retail_se": 0.24367304470072573,
        "arb_diff": 0.9217419539009998,
        "arb_se": 0.14174905705246812
      }
    }
  ],
  "next": "Keep B as incumbent: H is positive but does not exceed twice its paired standard error. A separate future experiment could test tail location while holding the chosen tail mass fixed; do not retune this candidate on H.",
  "code": "\"\"\"Stale-quote defence with tick probes, a sub-tick belief and cheap YES stock.\n\nEvery step the arbitrageur removes any order on the wrong side of p, so tiny\ntwo-sided probes around the estimate locate p to a tick; a grid filter narrows\nthat further.  Quotes sit one tick inside the competitor and are sized by the\nmarginal rule: one more share is worth quoting while the chance a retail order\nreaches it, times its edge, exceeds the expected arbitrage loss.  Near p=0 a\nretail buy pays almost its whole notional as edge, so YES is held to cover low\nasks.  That YES is bought when it is cheap: a large bid rests at tick 1 while p\nis above it, so the arbitrageur fills it on the crossing for about 0.1 tick a\nshare instead of nearly a full tick once p is near zero, and smaller bids sit on\nwhichever tick p is within 0.15 of.  Below tick 1 the estimate of p decays with\nthe time spent there, since no probe can see it.\n\"\"\"\nimport math\nfrom orderbook_pm_challenge.strategy import BaseStrategy\nfrom orderbook_pm_challenge.types import CancelOrder, PlaceOrder, Side\n\nP = dict(lam_r=0.25, m=5.0, sig_n=1.2, lam_j=0.0019, sig_j=0.35, sig=0.02,\n         qmax=3000.0, rmin=2, kA=0.7, kL=0.15, pL=0.05,\n         ammo=1.0, ydem=0.4, W=200.0, fy=0.6, ammo_q=60.0, ammo_pmax=0.05, ammo_maxpay=0.02,\n         fb=0.9, fu=0.7, xu=0.1, sticky=1.0, min_ahead=2.0, keep_hi=1.5, keep_lo=0.7, Hu=300.0, bins=8.0, filt=1.0, uni=0.01, jumpinfo=1.0, dmin=0.0, kLb=0.08, kLf=0.08, pinf=1.0, pre=0.0, H1=100.0,\n         ydf=0.6, Wf=2000.0, g_hi=0.15, g_lo=0.15, qf=400.0, pmaxf=0.05, pre_gap=1.0,\n         ydf1=0.6, g1=5.0, f1=0.6, a1=100.0, nb1=0.0, gs=1.0, p1t=1.0, m1=1.0, zd=1.0, pfl=0.0005)\nP.update({'W': 50, 'ammo_pmax': 0.02, 'fy': 0.7, 'fu': 0.25, 'Hu': 500})\nC = {'W': 50, 'ammo_pmax': 0.02, 'fy': 0.7, 'reserve': 200, 'scar': 0.0025, 'fu': 0.25, 'Hu': 500}\nSQ2 = math.sqrt(2.0)\nSQ2PI = math.sqrt(2.0 * math.pi)\ndef cdf(x): return 0.5 * (1.0 + math.erf(x / SQ2))\ndef pdf(x): return math.exp(-0.5 * x * x) / SQ2PI\ndef inv(p):\n    p = min(max(p, 1e-9), 1 - 1e-9)\n    t = math.sqrt(-2.0 * math.log(min(p, 1 - p)))\n    x = t - (2.515517 + 0.802853 * t + 0.010328 * t * t) / (1 + 1.432788 * t + 0.189269 * t * t + 0.001308 * t ** 3)\n    x = -x if p < 0.5 else x\n    for _ in range(2):\n        d = pdf(x)\n        if d < 1e-12:\n            break\n        x -= (cdf(x) - p) / d\n    return x\ndef lpm(k):\n    return pdf(k) + k * cdf(k)\ndef fl(q):\n    return math.floor(q * 100 + 1e-9) / 100\n\nNODES = [(-3.5, 0.01), (-2.2, 0.0588), (-1.3, 0.1666), (-0.6, 0.2646), (0.6, 0.2646), (1.3, 0.1666), (2.2, 0.0588), (3.5, 0.01)]\nPROBE = 0.01\n\n\nclass Strategy(BaseStrategy):\n    def __init__(self):\n        self.n = 0\n        self.ahead = set()     # main orders placed strictly ahead of any competitor order at their tick\n        self.res = {}          # order id -> uncovered quantity at placement\n        self.bc = None         # belief: sub-tick grid centres (ticks)\n        self.bw = None         # belief: weights\n        self.st = 0.5          # last one-step sd in ticks\n        self.pcb = self.pca = None\n        self.pb = set()\n        self.pa = set()\n        self.tb = 0\n        self.s = None          # competitor spread parameter\n\n    def _oid(self, tag):\n        self.n += 1\n        return tag + str(self.n)\n\n    def on_step(self, state):\n        H = max(1, state.steps_remaining)\n        cb = state.competitor_best_bid_ticks or 0\n        ca = state.competitor_best_ask_ticks or 100\n        if self.s is None:\n            self.s = max(1, (ca - cb + 1) // 2)\n        lo, hi = cb, ca\n        for o in state.own_orders:\n            if o.side is Side.BUY:\n                lo = max(lo, o.price_ticks)\n            else:\n                hi = min(hi, o.price_ticks)\n        if P['pinf'] > 0:\n            sb = {o.price_ticks for o in state.own_orders if o.side is Side.BUY and o.remaining_quantity <= PROBE + 1e-9}\n            sa = {o.price_ticks for o in state.own_orders if o.side is Side.SELL and o.remaining_quantity <= PROBE + 1e-9}\n            ea = self.pa - sa\n            eb = self.pb - sb\n            if sa and ea:\n                lo = max(lo, min(max(ea), hi))\n            if eb and (sb or min(eb) <= P['p1t']):\n                hi = min(hi, max(min(eb), lo))\n        if P['jumpinfo'] > 0 and self.pcb is not None:\n            if ca > self.pca + 1:\n                lo = max(lo, ca - 1)\n            if cb < self.pcb - 1:\n                hi = min(hi, cb + 1)\n        self.pcb, self.pca = cb, ca\n        if lo > hi:\n            lo, hi = hi, lo\n        # sub-tick belief over p_(t-1): predict with one-step noise, truncate to [lo, hi]\n        wid = max(hi - lo, 1e-6)\n        nb = max(2, min(48, int(round(wid * P['bins']))))\n        cs = [lo + (i + 0.5) * wid / nb for i in range(nb)]\n        if self.bc is None or P['filt'] <= 0:\n            ws = [1.0 / nb] * nb\n        else:\n            sg = max(self.st, 0.6 * wid / nb)\n            k = -0.5 / (sg * sg)\n            ws = []\n            uni = P['uni'] * sg\n            for c in cs:\n                t = uni\n                for ci, wi in zip(self.bc, self.bw):\n                    d = c - ci\n                    d2 = d * d * k\n                    if d2 > -12.5:\n                        t += wi * math.exp(d2)\n                ws.append(t)\n            tot = sum(ws)\n            ws = [w / tot for w in ws]\n        self.bc, self.bw = cs, ws\n        mean_t = sum(c * w for c, w in zip(cs, ws))\n        var_t = max(sum(c * c * w for c, w in zip(cs, ws)) - mean_t * mean_t, 0.0)\n        phat = min(max(mean_t / 100.0, 0.002), 0.998)\n        var = P['sig'] ** 2 + P['lam_j'] * P['sig_j'] ** 2\n        sd = math.sqrt(H * var)\n        self.tb = self.tb + 1 if hi <= 1 else 0\n        if self.tb > 0 and P['zd'] > 0:\n            # below the last tick there is no probe: the score keeps walking away from it\n            phat = min(phat, max(P['pfl'], cdf(inv(0.01) - P['zd'] * 0.02 * math.sqrt(self.tb) / sd)))\n        z = inv(phat)\n        Z = z * sd\n        sp = pdf(z) / sd * P['sig']\n        self.st = sp * 100.0\n        s = math.sqrt(sp * sp + var_t / 1e4 + 1e-8)\n        sd1 = math.sqrt(max(1.0, H - 1.0) * var)\n        jp = [(cdf((Z + n * P['sig_j']) / sd1), w) for n, w in NODES]\n        lj = P['lam_j']\n\n        def AB(x):\n            k = (x - phat) / s\n            a = s * lpm(k)\n            b = a - (x - phat)\n            ja = sum(w * max(0.0, x - p) for p, w in jp)\n            jb = sum(w * max(0.0, p - x) for p, w in jp)\n            return (1 - lj) * a + lj * ja, (1 - lj) * b + lj * jb\n\n        if H == 1:\n            def AB(x):\n                return (1 - phat) * x, phat * (1 - x)\n        rho = P['lam_r'] * 0.5\n        mu = math.log(P['m']) - 0.5 * P['sig_n'] ** 2\n        kA = P['kL'] if phat < P['pL'] else P['kA']\n\n        def qsize(loss, gain, px):\n            loss *= kA\n            if gain <= 0:\n                return 0.0\n            tail = loss / (rho * gain)\n            if tail >= 1.0:\n                return 0.0\n            return min(P['qmax'], math.exp(mu + P['sig_n'] * inv(1 - max(tail, 1e-6))) / px)\n\n        ptk = phat * 100\n        def qbid(t):\n            if not 1 <= t <= 99:\n                return 0.0\n            a, b = AB(t / 100.0)\n            if phat * 100 - t < P['dmin']:\n                return 0.0\n            if P['nb1'] > 0 and self.s == 1 and t == 1 and ptk < 1.0:\n                return 0.0\n            if phat < P['pL']:\n                ytg = P['ydem'] * min(H, P['W']) / max(ca - 1, ptk + 1) * 100.0\n                a *= (P['kLb'] if state.yes_inventory < ytg else P['kLf']) / P['kL']\n            return qsize(a, b, max(phat, 0.05))\n\n        def qask(t):\n            if not 1 <= t <= 99:\n                return 0.0\n            a, b = AB(t / 100.0)\n            if t - phat * 100 < P['dmin']:\n                return 0.0\n            if phat < C.get('scar_p', .1):\n                a = max(0.0, a - C.get('scar', 0.0) * (1 - min(1.0, state.yes_inventory / C.get('stock', 3000.0))))\n            return min(C.get('askcap', P['qmax']), qsize(b, a, t / 100.0))\n\n        bids = [o for o in state.own_orders if o.side is Side.BUY and o.remaining_quantity > PROBE + 1e-9]\n        asks = [o for o in state.own_orders if o.side is Side.SELL and o.remaining_quantity > PROBE + 1e-9]\n        sticky = P['sticky'] > 0\n        def top(orders, comp, step):\n            if sticky and sum(o.remaining_quantity for o in orders if o.price_ticks == comp and o.order_id in self.ahead) >= P['min_ahead']:\n                return comp\n            return comp + step\n        bid_t = top(bids, cb, 1)\n        ask_t = top(asks, ca, -1)\n        qb = min(C.get('bidcap', 3000.0), qbid(bid_t))\n        qa = qask(ask_t)\n\n        Y = state.yes_inventory\n        if ask_t == 1 and H > P['H1'] and P['pre'] > 0:\n            qa = qa if Y >= P['a1'] > 0 else 0.0          # let the competitor's 2-tick ask erode instead of fronting it for nothing\n        # ammunition: hold YES so that low asks are covered\n        ptick = phat * 100\n        amt = 0\n        qam = 0.0\n        if P['ammo'] > 0 and ask_t >= 2 and phat < max(P['ammo_pmax'], P['pmaxf']):\n            wealth = state.cash + Y * phat\n            x = max(ask_t, ptick + 1) / 100.0\n            capw = P['fy'] * wealth / max(phat, 0.005)\n            target = min(P['ydem'] * min(H, P['W']) / x, capw)\n            full = min(P['ydf'] * min(H, P['Wf']) / x, capw) if phat < P['pmaxf'] else 0.0\n            t = max(1, min(99, int(math.ceil(ptick + 1e-9))))\n            g = t - ptick\n            if Y < full and g <= P['g_hi'] and bid_t <= t < ask_t:\n                amt, qam = t, min(P['qf'], full - Y)     # a tick p has just crossed: nearly free\n            elif Y < target and phat < P['ammo_pmax'] and bid_t <= t < ask_t and t / 100.0 - phat < P['ammo_maxpay']:\n                amt, qam = t, min(P['ammo_q'], target - Y)\n            elif Y < full and t >= 2 and 1.0 - g <= P['g_lo']:\n                amt, qam = t - 1, min(P['qf'], full - Y)  # rest just under p: filled only on the crossing, or by retail\n\n        if P['pre'] > 0 and ca == 2 and phat < 0.01 and H > P['H1'] and Y < P['pre'] and not qam and 1.0 - ptick < P['pre_gap']:\n            amt, qam = 1, min(P['ammo_q'], P['pre'] - Y)\n        q1 = 0.0\n        if P['ydf1'] > 0 and P['m1'] < ptick < P['g1'] + 1.0 and amt != 1:\n            q1 = max(0.0, P['ydf1'] * min(H, P['Wf']) / (self.s / 100.0) - Y)\n        # order management\n        actions = []\n        free = state.free_cash - 2.0\n        keep_ask = 0.0\n        new_b = []\n        new_a = []\n        def cancel(o):\n            nonlocal free\n            actions.append(CancelOrder(o.order_id))\n            x = o.price_ticks / 100.0\n            if o.side is Side.BUY:\n                free += x * o.remaining_quantity\n            else:\n                free += (1 - x) * min(self.res.get(o.order_id, 0.0), o.remaining_quantity)\n            self.ahead.discard(o.order_id)\n            self.res.pop(o.order_id, None)\n        for o in state.own_orders:\n            if o.remaining_quantity <= PROBE + 1e-9:\n                cancel(o)\n        kept_ids = set()\n        def manage(orders, t, q, comp):\n            kept = 0.0\n            for o in orders:\n                if not (C.get('recover', 0) and o.side is Side.SELL and t < comp and self.res.get(o.order_id, 0.0) > 1 and state.yes_inventory > C.get('recover_yes', 1) and state.free_cash < C.get('recover_cash', 200)) and o.price_ticks == t and q > 0 and kept + o.remaining_quantity <= max(q * P['keep_hi'], 1.0):\n                    kept += o.remaining_quantity\n                    kept_ids.add(o.order_id)\n                else:\n                    cancel(o)\n            return kept\n        kb = manage(bids, bid_t, qb, cb)\n        ka = manage(asks, ask_t, qa, ca)\n        add_b = (qb - kb) if (kb < qb * P['keep_lo'] and bid_t != cb) else 0.0\n        add_a = (qa - ka) if (ka < qa * P['keep_lo'] and ask_t != ca) else 0.0\n\n        budget = P['fb'] * max(free, 0.0)\n        cost = add_b * bid_t / 100.0 + qam * amt / 100.0\n        if cost > budget:\n            f = budget / cost\n            add_b *= f\n            qam *= f\n            cost = budget\n        out = []\n        q1 = min(q1, P['f1'] * max(budget - cost, 0.0) * 100.0)\n        if q1 >= 0.01:\n            out.append(PlaceOrder(side=Side.BUY, price_ticks=1, quantity=fl(q1)))\n            free -= fl(q1) / 100.0 + 1e-6\n        if amt and qam >= 0.01:\n            q = fl(qam)\n            out.append(PlaceOrder(side=Side.BUY, price_ticks=amt, quantity=q))\n            free -= q * amt / 100.0 + 1e-6\n        if bid_t >= 1 and add_b >= 0.01:\n            q = fl(add_b)\n            oid = self._oid('b')\n            out.append(PlaceOrder(side=Side.BUY, price_ticks=bid_t, quantity=q, client_order_id=oid))\n            free -= q * bid_t / 100.0 + 1e-6\n            if bid_t > cb:\n                self.ahead.add(oid)\n        if ask_t <= 99 and add_a >= 0.01:\n            x = ask_t / 100.0\n            covk = sum(o.remaining_quantity - min(self.res.get(o.order_id, 0.0), o.remaining_quantity)\n                       for o in asks if o.order_id in kept_ids)\n            cov = max(0.0, Y - covk - 0.1)\n            unc = max(0.0, add_a - cov)\n            if unc > 0:\n                ub = max(0.0, free - (1 - P['fu']) * max(state.cash, 0.0)) if x >= P['xu'] else 0.0\n                if H < P['Hu'] and phat < 0.05:\n                    ub = max(0.0, free * (1 - H / P['Hu']))\n                ub = min(ub, max(0.0, free - C.get('reserve', 0.0) * min(1.0, H / C.get('release', 50.0))))\n                unc = min(unc, ub / (1 - x))\n            q = fl(min(add_a, cov + unc))\n            if q >= 0.01:\n                oid = self._oid('a')\n                out.append(PlaceOrder(side=Side.SELL, price_ticks=ask_t, quantity=q, client_order_id=oid))\n                r = max(0.0, q - cov)\n                self.res[oid] = r\n                free -= r * (1 - x) + 1e-6\n                if ask_t < ca:\n                    self.ahead.add(oid)\n\n        # probes: tiny orders that the arbitrageur removes when p crosses them\n        f = int(math.floor(ptick + 1e-9))\n        r = max(int(P['rmin']), int(math.ceil(2.0 * s * 100)))\n        self.pb = set()\n        self.pa = set()\n        if free > 1.0:\n            for t in range(f, f + r + 1):\n                if cb < t < ca and 1 <= t <= 99:\n                    out.append(PlaceOrder(side=Side.BUY, price_ticks=t, quantity=PROBE))\n                    self.pb.add(t)\n            for t in range(f + 1 - r, f + 2):\n                if cb < t < ca and 1 <= t <= 99:\n                    out.append(PlaceOrder(side=Side.SELL, price_ticks=t, quantity=PROBE))\n                    self.pa.add(t)\n        return actions + out\n",
  "code_sha256": "0b381ec04d77e85a9f2b04d03faee7cd82d1bc8c1a77e71acc79d1ffe44fb15d",
  "incumbent_code_sha256": "baef955ec10728839f7c3357016ec9eb0550e7ec689bbd5171b1cdd5c9e37273",
  "tuning_score": {
    "seeds": "110000..110499",
    "n": 500,
    "mean_diff": -0.2577136084328005,
    "se": 0.15055511846411382
  },
  "paired": {
    "seeds": "210000..210999",
    "n": 1000,
    "complete": true,
    "failures": [],
    "mean_diff": 0.09282066064040073,
    "se": 0.15561956822617037,
    "sum_d": 92.82066064040073,
    "sum_d2": 24201.848239926516,
    "candidate_mean": 106.6294749882076,
    "incumbent_mean": 106.5366543275672,
    "total_edge_differences_in_seed_order": [
      -1.5410790224000124,
      -0.01858965580000005,
      -1.7265098130999945,
      0.2779241906999994,
      -0.03502366720000083,
      -0.0001509674000000072,
      -0.3494173361000321,
      -0.04401992039999991,
      -0.2488584438999908,
      -0.2380633268999972,
      -0.07216893620000064,
      -0.12565069060000056,
      0.13576138610000044,
      -0.006655446299998147,
      0.07787853339999995,
      -0.8814406330999986,
      1.9263047745999984,
      0.09753506260000044,
      0.028276900599999877,
      0.21501200730000036,
      -1.5070079709000197,
      -0.06269763390000094,
      -0.024504060900000013,
      -0.023463606199999987,
      -0.9908021798999869,
      7.3218731713000125,
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    "retail_diff": -0.24350139494200002,
    "retail_se": 0.09862208230009992,
    "arb_diff": 0.3363220556097999,
    "arb_se": 0.11528811509293477,
    "candidate_mean_retail_edge": 130.9423499341047,
    "incumbent_mean_retail_edge": 131.1858513290467,
    "candidate_mean_arb_edge": -24.3128749458794,
    "incumbent_mean_arb_edge": -24.6491970014892
  },
  "seconds_per_simulation": 0.5279058753279969,
  "measurement": {
    "simulator_url": "https://github.com/danrobinson/prediction-market-challenge/tree/b244530ee2a6247b5f930830eeca9dd950da0e7e",
    "simulator_commit": "b244530ee2a6247b5f930830eeca9dd950da0e7e",
    "simulator_modified": false,
    "base_config": "ChallengeConfig()",
    "variance": "ParameterVariance()",
    "steps_per_simulation": 2000,
    "entrypoint": "load_strategy_factory(path); run_batch(factory, base_config=ChallengeConfig(), variance=ParameterVariance(), n_simulations=1, seed_start=seed, workers=1)",
    "sandbox": false,
    "execution": "Four OS processes on disjoint contiguous seed ranges; each run_batch call executes serially in its own process.",
    "frozen_at_utc": "2026-11-06T19:10:52.522675+00:00",
    "selection_rule": "Largest paired mean_diff on all 500 T seeds",
    "standard_error": "Sample standard deviation of per-seed candidate-minus-B differences (ddof=1), divided by sqrt(n).",
    "seed_ranges": "Both endpoints of each seeds string are inclusive.",
    "per_seed_data": "paired.total_edge_differences_in_seed_order contains every H total-edge difference in ascending seed order, without rounding or filtering.",
    "failure_policy": "Record exceptions and SimulationResult.failed with their seeds; a failure invalidates a complete paired measurement rather than being dropped.",
    "benchmark": {
      "seeds": "110000..110019",
      "n": 20,
      "strategy": "B",
      "failures": [],
      "cpu_seconds_per_simulation": 0.5278183696500001,
      "reuse": "These 20 B seeds were rerun during T and their three edge fields matched exactly. Only the T reruns enter tuning statistics."
    },
    "tuning_simulations": 2000,
    "holdout_simulations": 2000,
    "measured_cpu_seconds_per_simulation": 0.56912945585675,
    "measured_wall_seconds_per_simulation": 0.5692163887969218,
    "unrun_seeds": "No seeds outside T=110000..110499 and H=210000..210999 were run; finalist seeds 900000..903999 remain untouched.",
    "promotion": "None; this item freezes and measures one candidate only."
  }
}