{"approach": "Unchanged base returned: the frozen queue-specific candidate failed both the positive-unseen-gain and no-slower acceptance conditions. That candidate changed only spread-3 ask refills at ticks 4 and 5 from a 70% to a 90% threshold, preserving the existing target size and older FIFO orders. It gained on discovery and independent validation, but lost edge over all 2035 unseen spread-3 seeds and its measured CPU cost exceeded the base in the predeclared 200-pair timing comparison. Its unseen comparison and exact timing are retained in tried; the headline holdout and zero differences belong to the unchanged returned base. Broader depth multipliers and blanket retention were unsuccessful. All 900 other-spread results of the frozen candidate matched the base exactly; returning the original code also preserves those results by identity. Timing fields use a separate 200+200 unchanged-base comparison in one process, measured with time.process_time; wall seconds are recorded in tried. Simulator: unmodified commit b244530ee2a6247b5f930830eeca9dd950da0e7e.\n", "blocked": null, "python_version": "3.12.3", "spread": 3, "base_mean_edge": 98.4694596619638, "runs": {"tuning": 1200, "unseen": 2035}, "base_unseen": {"mean_edge": 118.8166859338749, "retail_edge": 149.05823988395983, "arb_edge": -30.241553950072774}, "holdout": {"mean_edge": 118.8166859338749, "retail_edge": 149.05823988395983, "arb_edge": -30.241553950072774, "failures": 0}, "diff": {"mean": 0.0, "standard_error": 0.0, "retail": 0.0, "arb": 0.0}, "others_identical": {"runs": 900, "identical": true}, "seconds": {"base": 313.09935562699974, "yours": 301.4446419140005}, "code": "\"\"\"Crossing-cover base with isolated, untuned spread overrides.\"\"\"\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)\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 = [(-2.2, .06), (-1.3, .17), (-0.6, .27), (0.6, .27), (1.3, .17), (2.2, .06)]\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        self.flow_notional = 0.0\n        self.flow_exposure = 0\n        self.last_ask_tick = None\n        self.last_quote_exposed = False\n        self.last_flow_ca = None\n        self.last_flow_cap = 0.0\n        self.last_flow_correction = 1.0\n        self.params = P.copy()\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            if self.s == 1:\n                self.params.update({'dmin': 0.3, 'pfl': 1e-06})\n            if self.s == 3:\n                self.params.update({'flow_prior': 200, 'flow_level': 0.6, 'flow_strength': 1.0, 'flow_safety': 2.0, 'flow_exposure_min': 5.0})\n        P = self.params\n        if self.s == 3 and self.last_quote_exposed and ca <= self.last_flow_ca + 1:\n            self.flow_exposure += 1\n            self.flow_notional += min(self.last_flow_cap, state.sell_filled_quantity * self.last_ask_tick / 100.0) * self.last_flow_correction\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            return 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 = 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            stock_rate = P['ydf1']\n            if self.s == 3:\n                flow = (self.flow_notional + P['flow_prior'] * P['flow_level']) / (P['flow_prior'] + self.flow_exposure)\n                scale = max(0.5, min(2.0, flow / P['flow_level']))\n                stock_rate *= 1.0 + P['flow_strength'] * (scale - 1.0)\n            q1 = max(0.0, stock_rate * min(H, P['Wf']) / (self.s / 100.0) - Y)\n\n        # Bridge expected retail demand until another tick crossing is available.\n        if 0.02 < phat < 0.1:\n            ct = max(2, min(99, int(math.floor(ptick))))\n            wealth = state.cash + Y * phat\n            goal = min(1 * qa, 0.3 * max(wealth, 0.0) / phat)\n            if Y < goal and ct < ask_t:\n                amt, qam = ct, min(3000.0, goal - 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 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                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        if self.s == 3:\n            self.last_ask_tick = ask_t\n            self.last_flow_ca = ca\n            exposed_qty = ka + sum(o.quantity for o in out if o.side is Side.SELL and o.price_ticks == ask_t and o.quantity > PROBE)\n            self.last_flow_cap = exposed_qty * ask_t / 100.0\n            self.last_quote_exposed = ask_t / 100.0 > phat + max(0.005, P['flow_safety'] * self.st / 100.0) and self.last_flow_cap >= P['flow_exposure_min']\n            if self.last_quote_exposed:\n                logcap = math.log(self.last_flow_cap)\n                mu_flow = math.log(P['m']) - 0.5 * P['sig_n'] ** 2\n                clipped = P['m'] * cdf((logcap - mu_flow - P['sig_n'] ** 2) / P['sig_n']) + self.last_flow_cap * (1.0 - cdf((logcap - mu_flow) / P['sig_n']))\n                self.last_flow_correction = min(3.0, P['m'] / max(clipped, 0.01))\n        return actions + out\n", "code_sha256": "dcf68e0c9a7b06f90cf70a833357b60e2208b5b3e2971e7dddf02986d4747076", "tried": ["Validation budgets fixed before comparison: broader thresholds use the first 360 validation seeds; tick-4/5 restriction uses the first 240. Longer queued batches were stopped for compute budget, with only these predetermined prefixes retained. Discovery uses the first 240 tuning seeds. Listed tuning seeds are the full eligible universe; runs.tuning counts seeds scored for the returned strategy, not all strategies combined.", "keepfront (retain oversized orders ahead of the rival at its current best tick, both sides) discovery: n=240, paired mean=-0.0873261512362496, standard_error=0.32273183191694615, retail=1.8126710827695829, arb=-1.899997234005417, failures=0.", "keepask (retain oversized ahead asks even before the rival joins) discovery: n=240, paired mean=-12.637116577646667, standard_error=1.9844191580758996, retail=-6.1817628462737515, arb=-6.455353731378334, failures=0.", "depth4_5 (quadruple ask target quantity at ticks <=5) discovery: n=240, paired mean=-13.897056037547502, standard_error=8.555632966859136, retail=4.648008435547084, arb=-18.545064473085414, failures=0.", "acquirelow (quote at cb+3 when gap>3 and ca<=8, including missing bids and existing front positions) discovery: n=240, paired mean=-4.385935313980835, standard_error=1.856376534721106, retail=-0.8806312237400004, arb=-3.5053040902145836, failures=0.", "reserve (aggressive acquisition plus double depth and front-ask retention) discovery: n=240, paired mean=-11.380245658107917, standard_error=5.43257807853499, retail=2.4236649777383334, arb=-13.803910635842916, failures=0.", "frontask (retain oversized asks only when ahead at the rival current tick) discovery: n=240, paired mean=-0.3502651159000006, standard_error=0.3419086950929735, retail=0.8392933626208328, arb=-1.1895584785283335, failures=0.", "frontlow (retain oversized same-tick front asks only at ticks <=5) discovery: n=240, paired mean=0.011628208795417075, standard_error=0.015847999167938265, retail=0.021296291010000346, arb=-0.009668082212499929, failures=0.", "topup (refill asks at ticks <=5 below 99% of current target before rival joins) discovery: n=240, paired mean=0.7256462174812499, standard_error=0.3505382178069381, retail=1.18579674794875, arb=-0.46015053046666715, failures=0.", "topup (refill asks at ticks <=5 below 99% of current target before rival joins) validation: n=360, paired mean=0.18964425234583304, standard_error=0.30313177541639647, retail=0.815607174819444, arb=-0.6259629224769443, failures=0.", "acquirebid (target live rival bid replenishment while preserving existing front position; identical simulation results in discovery) discovery: n=240, paired mean=0.0, standard_error=0.0, retail=0.0, arb=0.0, failures=0.", "depthsafe2 (double ask target when estimated probability <0.015 and ask tick >=4) discovery: n=240, paired mean=-0.06110794930083336, standard_error=0.04311852469586363, retail=-0.030893228892499753, arb=-0.03021472040666673, failures=0.", "splitdepth (same depthsafe2 target, split added asks into tranches of at most 3000 shares) discovery: n=240, paired mean=-0.09496836105416696, standard_error=0.05466644010959746, retail=-0.06475364064500014, arb=-0.03021472040666673, failures=0.", "topup90 (refill asks at ticks <=5 below 90% of current target before rival joins) discovery: n=240, paired mean=0.5360358162229174, standard_error=0.33805395837354923, retail=0.8828934808662484, arb=-0.34685766463958345, failures=0.", "topup90 (refill asks at ticks <=5 below 90% of current target before rival joins) validation: n=360, paired mean=0.28529410876611, standard_error=0.28470566119867335, retail=0.8092634204069438, arb=-0.5239693116413887, failures=0.", "queue90 (refill only tick-4/5 asks below 90% of current target before rival joins) discovery: n=240, paired mean=0.3380165052745834, standard_error=0.32728450862402264, retail=0.6311560679766648, arb=-0.29313956269833336, failures=0.", "queue90 (refill only tick-4/5 asks below 90% of current target before rival joins) validation: n=240, paired mean=0.22598667970541628, standard_error=0.27006896806811664, retail=0.5875052659391661, arb=-0.3615185862345832, failures=0.", "Base instrumentation on first 100 tuning seeds: BUY, tick<=5 is True: 79 front-order episodes (concurrent orders may overlap), mean duration 13.721518987341772 steps; edge while tracked per run: retail=0.9264180913956293, arb=-0.17774310730228715; ended by cancel=62, filled=17, simulation end=0. Episode begins when an older participant order first shares its tick with a rival. Tracked edge includes subsequent fills until cancellation, depletion or run end; this is descriptive, not a causal estimate of queue value.", "Base instrumentation on first 100 tuning seeds: BUY, tick<=5 is False: 10109 front-order episodes (concurrent orders may overlap), mean duration 4.724898605203284 steps; edge while tracked per run: retail=9.281147951552907, arb=-0.7267280518617595; ended by cancel=8943, filled=1166, simulation end=0. Episode begins when an older participant order first shares its tick with a rival. Tracked edge includes subsequent fills until cancellation, depletion or run end; this is descriptive, not a causal estimate of queue value.", "Base instrumentation on first 100 tuning seeds: SELL, tick<=5 is True: 145 front-order episodes (concurrent orders may overlap), mean duration 62.889655172413796 steps; edge while tracked per run: retail=18.27629285885014, arb=-5.330525595045814; ended by cancel=25, filled=114, simulation end=6. Episode begins when an older participant order first shares its tick with a rival. Tracked edge includes subsequent fills until cancellation, depletion or run end; this is descriptive, not a causal estimate of queue value.", "Base instrumentation on first 100 tuning seeds: SELL, tick<=5 is False: 10415 front-order episodes (concurrent orders may overlap), mean duration 5.101776284205473 steps; edge while tracked per run: retail=15.658733215827242, arb=-2.9195131300601314; ended by cancel=9225, filled=1190, simulation end=0. Episode begins when an older participant order first shares its tick with a rival. Tracked edge includes subsequent fills until cancellation, depletion or run end; this is descriptive, not a causal estimate of queue value.", "Broader topup90 descriptive trace on first 100 discovery seeds: 226 low-ask front-order episodes (may overlap), mean duration=71.50442477876106, retail edge/run=19.49588621597672, arb edge/run=-5.101364309007171. This trace is for broader topup90, not the tick-4/5-only queue90 candidate.", "Queue detail, first 100 tuning seeds, ask tick 4: episodes=76, mean front duration=84.6842105263158, mean quantity when rival joins=564.6411842105264, mean placement-to-join delay=48.36842105263158, retail edge/run=13.719403446188721, arb edge/run=-5.3304137999314545, cancelled=1, depleted=69.", "Queue detail, first 100 tuning seeds, ask tick 5: episodes=69, mean front duration=38.88405797101449, mean quantity when rival joins=311.0436231884058, mean placement-to-join delay=17.420289855072465, retail edge/run=4.556889412661422, arb edge/run=-0.00011179511435881313, cancelled=24, depleted=45.", "Rejected frozen queue90 unseen comparison: n=2035, paired mean=-0.27208736861449667, standard_error=0.17314863498248143, retail_diff=0.32646906127793635, arb_diff=-0.5985564298918428, failures=0. Returned code is the original base, so final diff fields are zero.", "Frozen queue90 timing: CPU seconds={'base': 303.4692482060003, 'yours': 308.28718786300004}; wall seconds={'base': 1167.1640599751845, 'yours': 1192.7339210361242}. Base fallback has a separate 200+200 in-process timing run.", "Other-spread regression: the frozen candidate and base were run on all 900 seeds and their complete result dictionaries compared. Returning the original base also preserves those results by identity.", "Tuning spread-3 seeds: 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"Unseen spread-3 seeds: 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"Timing protocol fixed before final timing: seconds reports time.process_time CPU seconds for 200 paired runs in one process, alternating order. Concurrent scoring makes wall time scheduling-dependent. Observed wall seconds: {'base': 870.3668254544027, 'yours': 837.9622590970248}.", "Selected base: unseen n=2035, paired mean=0.0, standard error=0.0; timed 200 paired spread-3 runs in one process, alternating execution order; all 900 other-spread full result dictionaries identical."], "next": "Fix the refill policy speed penalty before submission: target additions just before a rival bid is exhausted using public state, rather than every tick-4/5 refill opportunity. Measure total queue lifetime across overlapping orders and use fresh tuning/unseen seeds. Broad depth multipliers and blanket retention failed here."}