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Neither is included twice in choose paired estimates."},"first_call_errors":{"C":{"wrong":33700,"total":204262,"coverage":"completed choose seeds only"},"Cflag":{"wrong":0,"total":202278,"coverage":"completed choose seeds only"},"moment_control":{"wrong":33788,"total":207558,"coverage":"completed choose seeds only"}},"diagnostics":{"baseline_64000":{"credit":"Taylor (gtmcknight)","rows":{"COMBO":{"seed":64000,"steps":10000,"edge":437.7100000860294,"seconds":4.779938617008156,"first_call_errors":{"wrong":1011,"total":6615}},"Flag":{"seed":64000,"steps":10000,"edge":439.38939781550437,"seconds":0.42979990399908274},"qt1":{"seed":64000,"steps":10000,"edge":436.4087645205474,"seconds":4.525256112989155}},"oracle_only":["Flag"],"flag_patch":"Insert pi = float(bool(arb)) immediately before is_arb = pi > .5 in combo.py, after the likelihood calculation and before all posterior/rival/phase/quote updates.","live_source_sha256":"120f4384ca4b91c5b4526a0fc62bd0e631d4f1169c263076e31ed14b4e632a4f","flag_source_sha256":"496fe214574c521f7a46adead5fe48ce66868b5f4a651fa73e854ed24f2e2241"},"prototype_v1_C":{"seed":64000,"steps":10000,"edge":331.13005171866,"seconds":32.3984650499915,"objective_calls":9336,"first_call_errors":{"wrong":992,"total":6384},"qualification":"prototype pilot only; no full Gate Q coverage","hidden_invariance_checked_callbacks":16,"hidden_invariance_max_abs_error":0.0},"prototype_v1_Cflag":{"policy":"Cflag","seed":64000,"steps":10000,"edge":323.28969428713833,"seconds":16.993024419003632,"first_call_errors":{"wrong":0,"total":6301},"oracle_only":true,"qualification":"one-seed pilot; not Gate Q"},"prototype_v2_C":{"seed":64000,"steps":10000,"edge":330.6821971955818,"seconds":33.949068581991014,"objective_calls":9348,"first_call_errors":{"wrong":1007,"total":6395},"qualification":"prototype pilot only; no full Gate Q coverage","hidden_invariance_checked_callbacks":16,"hidden_invariance_max_abs_error":0.0},"prototype_v1_objective_sha256":"0c0f8caac54ae2f5f94dd3dc69191d3ccc23a9ad0f1fad6439b788f0dfee5ca5","prototype_v1_objective_encoding":"zlib+base64","prototype_v1_objective_source":"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","oracle_only":["Flag","Cflag"]},"params":{"model":{"lambda":0.8,"sigma_bps":9.45,"size_mean":20.0,"H_bps":5.0,"DP_AMINL":-12,"DP_NA":43,"physics":"Unmodified pinned dp.py defaults; 62-node size quadrature."},"filter":{"price_cells":121,"cell_bps":2.5,"sigma_bps":9.45,"rho":0.26,"hard":0.995,"silent_step_cap":30,"source":"Pinned latt.py with branch-context instrumentation. COMBO carried rival and retail-count update rules."},"quote":{"physical_log_fee_grid_bps":[0,150,5],"subgrid":"One parabolic coordinate probe per ask/bid, at most half a 5bps grid cell; accepted only when the interpolated posterior objective improves.","hypothesis_state_action_shifts":"w-delta,r-delta,a-delta,b+delta","no_average_of_optimal_quotes":true,"table_domain":"4D multilinear interpolation with clipping to represented state/action domain."},"paired_statistics":"mean of same-seed differences; SE=sample_sd(differences)/sqrt(n); worst5=lowest [seed,diff], ties by seed."},"source_patch":{"credit":"Taylor (gtmcknight), base model/filter/replica.","files":{"objective.py":{"sha256":"67af0971e7c05e1752a304245465ba926735b9c6a0d59c782fd2efa13f276b4b","text":"\"\"\"Credit: Taylor (gtmcknight), dp.py solved VN and latt.py posterior.\nPosterior objective for a common physical quote; independent of simulator hidden inputs.\n\"\"\"\nimport math\nimport numpy as np\nfrom numba import njit\n\n@njit\ndef pos2(x,W):\n    if x<=W[0]:return 0.\n    if x>=W[-1]:return float(len(W)-1)\n    k=np.searchsorted(W,x)-1\n    return k+(x-W[k])/(W[k+1]-W[k])\n\n@njit\ndef interp(V,w,r,a,b,W,phase):\n    # Indices are in 5bps lattice units. Clamp to represented table domain.\n    z0=min(V.shape[0]-1.,max(0.,160.+w))\n    z1=min(12.,max(0.,r+6.)) if phase==0 else pos2(r,W)\n    z2=min(42.,max(0.,a+12.));z3=min(42.,max(0.,b+12.))\n    i0=min(int(z0),V.shape[0]-2);i1=min(int(z1),V.shape[1]-2)\n    i2=min(int(z2),V.shape[2]-2);i3=min(int(z3),V.shape[3]-2)\n    f0=z0-i0;f1=z1-i1;f2=z2-i2;f3=z3-i3\n    out=0.\n    for d0 in range(2):\n        for d1 in range(2):\n            wt=(f0 if d0 else 1-f0)*(f1 if d1 else 1-f1)\n            v00=V[i0+d0,i1+d1,i2,i3]*(1-f3)+V[i0+d0,i1+d1,i2,i3+1]*f3\n            v10=V[i0+d0,i1+d1,i2+1,i3]*(1-f3)+V[i0+d0,i1+d1,i2+1,i3+1]*f3\n            out+=wt*(v00*(1-f2)+v10*f2)\n    return out\n\n@njit\ndef objective(V,probs,deltas,w,r,a,b,W,phase):\n    # delta=(hypothesis log price - reference log price)/(5bps).\n    # One physical ask moves down by delta; one physical bid offset moves up.\n    out=0.;mass=0.\n    for k in range(len(probs)):\n        q=probs[k]\n        if q<=0.:continue\n        d=deltas[k]\n        out+=q*interp(V,w-d,r-d,a-d,b+d,W,phase)\n        mass+=q\n    return out/mass\n\n@njit\ndef maximize(V,probs,deltas,w,r,W,phase):\n    # Common feasible nonnegative log fees, 0..150bps in increments of 5bps.\n    best=-1e300;aa=0.;bb=0.\n    for ask_fee in range(31):\n        a=w+ask_fee\n        for bid_fee in range(31):\n            b=-w+bid_fee\n            v=objective(V,probs,deltas,w,r,a,b,W,phase)\n            if v>best:best=v;aa=a;bb=b\n    for axis in range(2):\n        fee=aa-w if axis==0 else bb+w\n        if fee>=1. and fee<=29.:\n            vm=objective(V,probs,deltas,w,r,aa-(1. if axis==0 else 0.),bb-(1. if axis==1 else 0.),W,phase)\n            vp=objective(V,probs,deltas,w,r,aa+(1. if axis==0 else 0.),bb+(1. if axis==1 else 0.),W,phase)\n            den=vm-2*best+vp\n            if den<0.:\n                shift=max(-.5,min(.5,.5*(vm-vp)/den))\n                at=aa+(shift if axis==0 else 0.);bt=bb+(shift if axis==1 else 0.)\n                v=objective(V,probs,deltas,w,r,at,bt,W,phase)\n                if v>best:aa=at;bb=bt;best=v\n    return aa,bb,best\n\n@njit\ndef joint_value(V0,V1,pi,delta_arb,retprobs,deltas,w,ra,rr,a,b,W):\n    return pi*interp(V0,w-delta_arb,ra-delta_arb,a-delta_arb,b+delta_arb,W,0)+(1-pi)*objective(V1,retprobs,deltas,w,rr,a,b,W,1)\n\n@njit\ndef maximize_joint(V0,V1,pi,delta_arb,retprobs,deltas,w,ra,rr,W):\n    best=-1e300;aa=0.;bb=0.\n    for af in range(31):\n        a=w+af\n        for bf in range(31):\n            b=-w+bf\n            va=interp(V0,w-delta_arb,ra-delta_arb,a-delta_arb,b+delta_arb,W,0)\n            vr=objective(V1,retprobs,deltas,w,rr,a,b,W,1)\n            v=pi*va+(1-pi)*vr\n            if v>best:best=v;aa=a;bb=b\n    for axis in range(2):\n        fee=aa-w if axis==0 else bb+w\n        if fee>=1. and fee<=29.:\n            vm=joint_value(V0,V1,pi,delta_arb,retprobs,deltas,w,ra,rr,aa-(1. if axis==0 else 0.),bb-(1. if axis==1 else 0.),W)\n            vp=joint_value(V0,V1,pi,delta_arb,retprobs,deltas,w,ra,rr,aa+(1. if axis==0 else 0.),bb+(1. if axis==1 else 0.),W)\n            den=vm-2*best+vp\n            if den<0.:\n                shift=max(-.5,min(.5,.5*(vm-vp)/den))\n                at=aa+(shift if axis==0 else 0.);bt=bb+(shift if axis==1 else 0.)\n                v=joint_value(V0,V1,pi,delta_arb,retprobs,deltas,w,ra,rr,at,bt,W)\n                if v>best:aa=at;bb=bt;best=v\n    return aa,bb,best\n"},"candidate.py":{"sha256":"12b1ea7bc0ae32bad0b6952737c51b08e4c2e77f4016c2d6ec9036220c4b3441","text":"\"\"\"Credit: Taylor (gtmcknight), lattice filter, solved values and qm tables.\nExperimental full-posterior quotes; not qualified by full choose/report coverage.\n\"\"\"\nimport pathlib,math\nimport numpy as np\nimport joint_latt as latt\nfrom combo import Combo\nfrom qmref import INIT\nfrom objective import maximize, maximize_joint\nclass Candidate(latt.LatticePolicy):\n    def __init__(self,T,root,flag=False,control=False):\n        super().__init__(T,{'hard':.995,'oraclearb':int(flag)})\n        self.control=control\n        root=pathlib.Path(root)\n        self.ref=Combo(str(root/'blob.bin'))\n        self.values=[np.load(root/f'regenerated_VN{ph}.npy',mmap_mode='r') for ph in range(3)]\n        self.W=np.array(self.ref.W2L,dtype=float)\n        self.objective_calls=0\n    def init(self,*ignored):\n        self.fb=self.fa=INIT\n        return INIT,INIT\n    def lookup(self,w,w2,phs,sd):\n        if self.control:\n            a,b=self.ref.lookup_offsets(w,w2,phs,sd,self.synced)\n            return 1-math.exp(-max(0.,b+w)*1e-4),1-math.exp(-max(0.,a-w)*1e-4)\n        probs=np.array(self.P)\n        deltas=np.array([(self.c+(i-latt.N0)*latt.HC-self.m)/5e-4 for i in range(latt.NC)])\n        if self.quote_branch is None:\n            a,b,val=maximize(self.values[phs],probs,deltas,w/5,w2/5,self.W,phs)\n        else:\n            pi,lp,logs,retprobs,sa,sr=self.quote_branch\n            ds=np.array([(v-self.m)/5e-4 for v in logs])\n            a,b,val=maximize_joint(self.values[0],self.values[1],pi,(lp-self.m)/5e-4,np.array(retprobs),ds,w/5,(math.log(sa)-self.m)/5e-4,(math.log(sr)-self.m)/5e-4,self.W)\n        self.objective_calls+=1\n        return 1-math.exp(-max(0.,b+w/5)*5e-4),1-math.exp(-max(0.,a-w/5)*5e-4)\n"},"evaluate.py":{"sha256":"a52dd3b6fa134eec9fb21e9e89179fecfb19f8dd21434fd8c8c5df44ca6cb341","text":"\"\"\"Credit: Taylor (gtmcknight): replica, dp.py, qm tables, Gaussian and lattice filters.\nCheckpointed paired evaluator. Choose only; no report seed is read by this program.\n\"\"\"\nimport pathlib,sys,json,time,hashlib,multiprocessing\nROOT=pathlib.Path(__file__).resolve().parent\nsys.path[:0]=[str(ROOT),str(ROOT/'deps')]\nimport replica2\nfrom evalpol import load\nfrom qmref import QMRef\nfrom combo import Combo\nfrom candidate import Candidate\nT=load(str(ROOT/'pol_mid.json'));BLOB=str(ROOT/'blob.bin')\nsource=(ROOT/'combo.py').read_text();anchor='            is_arb = pi > .5\\n'\nassert source.count(anchor)==1\nflag_source=source.replace(anchor,'            pi = float(bool(arb))\\n'+anchor)\nns={};exec(compile(flag_source,'ComboFlag','exec'),ns);Flag=ns['Combo']\n\ndef one(seed):\n    started=time.monotonic();out={'seed':seed,'steps':10000,'policies':{}}\n    for name in ['COMBO','qt1','Flag','moment_control','C','Cflag']:\n        if name=='COMBO':pol=Combo(BLOB)\n        elif name=='qt1':pol=QMRef(BLOB)\n        elif name=='Flag':pol=Flag(BLOB)\n        else:pol=Candidate(T,ROOT,flag=name=='Cflag',control=name=='moment_control')\n        t=time.monotonic();r=replica2.run(seed,pol)\n        entry={'edge':r['edge'],'seconds':time.monotonic()-t}\n        if name in ['C','Cflag','moment_control']:entry['first_call_errors']={'wrong':pol.nmis,'total':pol.ncls}\n        out['policies'][name]=entry\n    out['seconds']=time.monotonic()-started\n    return out\n\nif __name__=='__main__':\n    # The file is append-only and every completed seed is independently checkpointed.\n    dest=ROOT/'choose.rows.jsonl'\n    done=set()\n    if dest.exists():\n        for line in dest.read_text().splitlines():done.add(json.loads(line)['seed'])\n    seeds=[s for s in range(64000,65024) if s not in done]\n    with dest.open('a') as f, multiprocessing.Pool(4) as pool:\n        for row in pool.imap_unordered(one,seeds,chunksize=1):\n            f.write(json.dumps(row,sort_keys=True,separators=(',',':'))+'\\n');f.flush()\n            print(json.dumps({'seed':row['seed'],'completed':len(done)+1,'seconds':row['seconds']}),flush=True)\n            done.add(row['seed'])\n"},"solve_driver.py":{"sha256":"47be5d1b623a1f0fd174a60bd5ceadfe5565f5683fd08ee50f90859025407366","text":"\"\"\"Credit: Taylor (gtmcknight); launcher for unmodified pinned dp.py.\"\"\"\nimport sys,pathlib,runpy\np=pathlib.Path(__file__).resolve().parent\nsys.path.insert(0,str(p/'deps'))\nsys.argv=[str(p/'dp.py'),'solve','0.8','9.45','20',str(p/'regenerated.json')]\nrunpy.run_path(str(p/'dp.py'),run_name='__main__')\n"},"check_objective.py":{"sha256":"17b9a21f393034eb63757967df2806479e766df75e4cc53033492d9ee593de11","text":"\"\"\"Credit: Taylor (gtmcknight), underlying solved VN conventions.\"\"\"\nimport sys,pathlib,math,json\np=pathlib.Path(__file__).resolve().parent\nsys.path[:0]=[str(p),str(p/'deps')]\nimport numpy as np\nfrom objective import objective,interp,maximize,maximize_joint\nW=np.array([-160.,-130.,-100.,-80.,-60.,-50.,-40.,-30.,-25.,-20.,-17.,-14.]+list(range(-12,13))+[14.,17.,20.,25.,30.,40.,50.,60.,80.,100.,130.,160.])\n# Deliberately cross-coupled objective distinguishes average values and average optima.\nshape=(321,13,43,43)\ni,j,a,b=np.ogrid[:321,:13,:43,:43]\nV=(.1*(i-160)+.2*(j-6)+.3*(a-12)+.4*(b-12)+.02*(a-12)*(b-12)).astype(float)\nw,r,aa,bb=2.3,.4,5.2,6.1\nprob=np.array([.3,.7]);d=np.array([-1.,2.])\nexpected=sum(q*(.1*(w-h)+.2*(r-h)+.3*(aa-h)+.4*(bb+h)+.02*(aa-h)*(bb+h)) for q,h in zip(prob,d))\ngot=objective(V,prob,d,w,r,aa,bb,W,0)\npoint=objective(V,np.array([1.]),np.array([0.]),w,r,aa,bb,W,0)\nraw=interp(V,w,r,aa,bb,W,0)\nresult={'posterior_affine_cross_term_abs_error':abs(got-expected),'point_mass_abs_error':abs(point-raw),'normalized_mass_abs_error':abs(objective(V,prob*7,d,w,r,aa,bb,W,0)-got)}\na0,b0,v0=maximize(V,np.array([1.]),np.array([0.]),0.,0.,W,0)\naj,bj,vj=maximize_joint(V,V,1.,0.,np.array([1.]),np.array([0.]),0.,0.,0.,W)\nresult['joint_point_mass_arb_value_abs_error']=abs(v0-vj)\nresult['joint_point_mass_arb_quote_max_abs_error']=max(abs(a0-aj),abs(b0-bj))\nassert max(result.values())<1e-12\n(p/'objective-tests.json').write_text(json.dumps(result))\nprint(json.dumps(result))\n"},"check_vn.py":{"sha256":"2199c5c8f47d53e6187ac4dc905ef4715332f2e892f5c00858b6d7e16c85b6f5","text":"\"\"\"Credit: Taylor (gtmcknight), solved VN and dp.py action conventions.\"\"\"\nimport pathlib,sys,json,time\np=pathlib.Path(__file__).resolve().parent;sys.path[:0]=[str(p),str(p/'deps')]\nimport numpy as np\nfrom objective import maximize\nV=np.load(p/'regenerated_VN0.npy',mmap_mode='r')\nW=np.array(json.loads((p/'pol_mid.json').read_text())['W2L'],float)\nrows=[]\nfor w,r in [(0,0),(3,-2),(-3,2)]:\n t=time.monotonic();a,b,v=maximize(V,np.array([1.]),np.array([0.]),float(w),float(r),W,0)\n expected=float(np.max(V[160+w,6+r]));row={'w_lattice':w,'r_lattice':r,'a_lattice':a,'b_lattice':b,'objective':v,'dp_discrete_max':expected,'abs_error':abs(v-expected),'seconds':time.monotonic()-t};rows.append(row)\n assert row['abs_error']<1e-7\n(p/'vn-tests.json').write_text(json.dumps(rows));print(json.dumps(rows))\n"},"check_flag.py":{"sha256":"5eac3b01d19b28d48f68f6250ca0b94e9fd0c23facd55bf33951892ba0b8d16d","text":"\"\"\"Credit: Taylor (gtmcknight), replica and filters; COMBO predecessor baseline.\"\"\"\nimport pathlib,sys,json,time,hashlib\np=pathlib.Path(__file__).resolve().parent;sys.path.insert(0,str(p))\nimport replica2\nfrom qmref import QMRef\nsource=(p/'combo.py').read_text()\n# Instrument the first own callback of each step, after likelihood but before updates.\nanchor='            is_arb = pi > .5\\n'\nassert source.count(anchor)==1\nlive=source.replace(anchor,'            self.first_total = getattr(self, \"first_total\", 0) + 1\\n            self.first_wrong = getattr(self, \"first_wrong\", 0) + int((pi > .5) != bool(arb))\\n'+anchor)\nflag=source.replace(anchor,'            pi = float(bool(arb))\\n'+anchor)\nns={};exec(compile(live,'COMBO-live-diagnostics','exec'),ns);Live=ns['Combo']\nns={};exec(compile(flag,'COMBO-Flag','exec'),ns);Flag=ns['Combo']\nrows={}\nfor name,cls in [('COMBO',Live),('Flag',Flag),('qt1',QMRef)]:\n t=time.monotonic();pol=cls(str(p/'blob.bin'));r=replica2.run(64000,pol)\n rows[name]={'seed':64000,'steps':10000,'edge':r['edge'],'seconds':time.monotonic()-t}\n if name=='COMBO':rows[name]['first_call_errors']={'wrong':pol.first_wrong,'total':pol.first_total}\n print(json.dumps({name:rows[name]}),flush=True)\n(p/'flag-diagnostic.json').write_text(json.dumps({'credit':'Taylor (gtmcknight)','rows':rows,'oracle_only':['Flag'],'flag_patch':\"Insert pi = float(bool(arb)) immediately before is_arb = pi > .5 in combo.py, after the likelihood calculation and before all posterior/rival/phase/quote updates.\",'live_source_sha256':hashlib.sha256(live.encode()).hexdigest(),'flag_source_sha256':hashlib.sha256(flag.encode()).hexdigest()}))\n"},"check_invariance.py":{"sha256":"704048e7afa380d989353b1deafc14ca9ae02beffe046b21f0649f1b445c6a74","text":"\"\"\"Credit: Taylor (gtmcknight), replica and COMBO predecessor conventions.\"\"\"\nimport sys,pathlib,json,copy\np=pathlib.Path(__file__).resolve().parent\nsys.path.insert(0,str(p))\nfrom combo import Combo\nimport replica2\nclass Check:\n wants_j=True\n def __init__(self):\n  self.a=Combo(str(p/'blob.bin'));self.b=Combo(str(p/'blob.bin'));self.n=0;self.error=0.\n def init(self,*args):\n  a=self.a.init(*args);b=self.b.init(123.,456.,789.);assert a==b;return a\n def on_trade(self,buy,ax,ay,t,x,y,arb,price,s2,j):\n  a=self.a.on_trade(buy,ax,ay,t,x,y,arb,price,s2,j)\n  b=self.b.on_trade(buy,ax,ay,t,x,y,not arb,1.234,9876.5,j+100)\n  self.error=max(self.error,max(abs(u-v) for u,v in zip(a,b)));self.n+=1\n  assert a==b\n  return a\npol=Check();replica2.run(64000,pol)\nr={'policy':'unmodified COMBO only; candidate not covered','seed':64000,'steps':10000,'callbacks':pol.n,'max_abs_fee_difference':pol.error,'hidden_init_and_callback_fields_changed':True}\n(p/'invariance.json').write_text(json.dumps(r));print(json.dumps(r))\n"},"pilot.py":{"sha256":"ba1124580f87ff9422359fc8bcc36478b66392a0b4b020f4a39a7c0761fa85f0","text":"\"\"\"Credit: Taylor (gtmcknight), replica, lattice and dynamic program.\nUnqualified prototype performance test, NOT a Gate Q evaluation.\n\"\"\"\nimport pathlib,sys,json,time\np=pathlib.Path(__file__).resolve().parent;sys.path[:0]=[str(p),str(p/'deps')]\nimport replica2\nfrom evalpol import load\nfrom candidate import Candidate\nT=load(str(p/'pol_mid.json'))\nclass CheckedCandidate(Candidate):\n    def __init__(self,T,root):\n        super().__init__(T,root)\n        self.twin=Candidate(T,root)\n        self.checked=0\n        self.hidden_max_error=0.\n    def init(self,*args):\n        a=super().init(*args);b=self.twin.init(100.,200.,300.)\n        assert a==b\n        return a\n    def on_trade(self,is_buy,ax,ay,t,x,y,arb,p,s2,j):\n        a=super().on_trade(is_buy,ax,ay,t,x,y,arb,p,s2,j)\n        if self.checked<16:\n            b=self.twin.on_trade(is_buy,ax,ay,t,x,y,not arb,1.234,9876.5,j+100)\n            self.hidden_max_error=max(self.hidden_max_error,max(abs(u-v) for u,v in zip(a,b)))\n            assert a==b\n            self.checked+=1\n        return a\npol=CheckedCandidate(T,p)\nt=time.monotonic()\nr=replica2.run(64000,pol)\nout={'seed':64000,'steps':10000,'edge':r['edge'],'seconds':time.monotonic()-t,'objective_calls':pol.objective_calls,'first_call_errors':{'wrong':pol.nmis,'total':pol.ncls},'qualification':'prototype pilot only; no full Gate Q coverage','hidden_invariance_checked_callbacks':pol.checked,'hidden_invariance_max_abs_error':pol.hidden_max_error}\n(p/'pilot.json').write_text(json.dumps(out));print(json.dumps(out),flush=True)\n"},"pilot_aux.py":{"sha256":"f3e55ba0a4177d03d575672df5e22c9abcdca314a432cd8aac5ec3de1ff839d0","text":"\"\"\"Credit: Taylor (gtmcknight), replica, lattice, qm tables, and solved VN.\"\"\"\nimport pathlib,sys,json,time\np=pathlib.Path(__file__).resolve().parent;sys.path[:0]=[str(p),str(p/'deps')]\nimport replica2\nfrom evalpol import load\nfrom candidate import Candidate\nname=sys.argv[1]\npol=Candidate(load(str(p/'pol_mid.json')),p,flag=name=='Cflag',control=name=='moment_control')\nt=time.monotonic();r=replica2.run(64000,pol)\nout={'policy':name,'seed':64000,'steps':10000,'edge':r['edge'],'seconds':time.monotonic()-t,'first_call_errors':{'wrong':pol.nmis,'total':pol.ncls},'oracle_only':name=='Cflag','qualification':'one-seed pilot; not Gate Q'}\n(p/(name+'.json')).write_text(json.dumps(out));print(json.dumps(out),flush=True)\n"}},"joint_latt_patch":"--- latt.py\n+++ joint_latt.py\n@@ -1,3 +1,4 @@\n+\"\"\"Credit: Taylor (gtmcknight); lattice filter with COMBO branch context instrumentation.\"\"\"\n \"\"\"Exact Bayes on a price lattice, real observables only.\n \n Belief = probability mass on cells of width HC around a moving centre.  Each elapsed step convolves with the\n@@ -112,6 +113,8 @@\n         if 0 <= i + 1 < NC: self.P[i + 1] += w * f; self.lo_i = min(self.lo_i, i + 1); self.hi_i = max(self.hi_i, i + 1)\n     # ---- callback\n     def on_trade(self, is_buy, ax, ay, t, x, y, arb, p, s2, j):\n+        self.quote_branch = None\n+        old_s2 = self.s2h\n         o = self.o\n         s = y / x\n         dt = t - self.tlast\n@@ -142,6 +145,17 @@\n                 l_ret = Z * o.get('rho', RHO) * phi((math.log(max(d, 1e-9)) - LD_M) / LD_S) / (max(d, 1e-9) * LD_S)\n                 pi = l_arb / (l_arb + l_ret) if (l_arb + l_ret) > 0 else 1.0\n                 if o.get('oraclearb', 0): pi = 1.0 if arb else 0.0\n+                if .005 < pi < .995 and Z > 1e-300:\n+                    logs = [self.c + (i-N0)*HC for i in range(NC)]\n+                    retprobs = [v/Z for v in self.P]\n+                    mt = sum(q*lp for q,lp in zip(retprobs,logs))\n+                    pa = math.exp(lp_arb); pr = math.exp(mt)\n+                    sa = max(pa*.997,min(pa/.997,old_s2))\n+                    sr = max(pr*.997,min(pr/.997,old_s2))\n+                    cand = s*(1-self.fb)/.997 if is_buy else s*.997/(1-self.fa)\n+                    sync = cand < sr if is_buy else cand > sr\n+                    if sync: sr = cand\n+                    self.quote_branch = (pi,lp_arb,logs,retprobs,sa,sr)\n                 hard = o.get('hard', 0.999)\n                 if pi >= hard or Z <= 1e-300:\n                     self.pin(lp_arb)\n","joint_latt_result_sha256":"30148b6571bb6b87d3074bceecbd322201d87851437038155b079e1b757f5834","base_latt_sha256":"925c0fe996b19fd51fb79a85d5e4dba4bc2f5453d0a156cc1e480f65a6a00cc8","dependencies":"Extract and hash-check qmref.py, twocomp.py, combo.py and blob.bin from the supplied artifacts. The pinned replica2.py/evalpol.py/real.py/latt.py and pol_mid.json are also required. numpy and numba are needed for the new quoting kernel and regeneration. This JSON is research source, not a bundled offline executable."},"table_model_regeneration":{"hashes":{"regenerated.json":"fafe2c68faec494c434339f78ae688bd281a3fd23f0921d0d63a39d9bd06ff04","regenerated_vd.npz":"93fa6bd794610259c73aef1cdb07721ca37ac162bfa555d9d7e1d4bd3d0db89d","regenerated_VN0.npy":"dc602095804048829da58040ce5b40dee25b25edaeeba921f45033e9894072b6","regenerated_VN1.npy":"871274b7f2ede9ee9f610c7dfaf7fe450d971259aec3cf4b74245d9cb79be0fd","regenerated_VN2.npy":"7fa4b401dde383b3a37f2ba3f79edb63702ece1f4c059f8a486850cdbb32c086"},"g":0.05368206098114671,"all_six_policy_tables_equal":true,"last_sweep":"110","stdout":"geometry 7.1s  nq 62  h [0.55067104 0.34722702 0.2331101 ]  grid NW 321 N2 49 NA 43\nsweep 1 t 24s gain/step 0.0576476 -> 576.476 per run  diff 3.40e-02\nsweep 2 t 27s gain/step 0.0583772 -> 583.772 per run  diff 3.05e-02\nsweep 3 t 31s gain/step 0.0582014 -> 582.014 per run  diff 2.82e-02\nsweep 10 t 57s gain/step 0.0551817 -> 551.817 per run  diff 2.17e-02\nsweep 20 t 95s gain/step 0.0540383 -> 540.383 per run  diff 2.00e-02\nsweep 30 t 134s gain/step 0.0537958 -> 537.958 per run  diff 1.91e-02\nsweep 40 t 171s gain/step 0.0537190 -> 537.190 per run  diff 1.81e-02\nsweep 50 t 208s gain/step 0.0536884 -> 536.884 per run  diff 1.64e-02\nsweep 60 t 244s gain/step 0.0536821 -> 536.821 per run  diff 3.17e-04\nsweep 70 t 280s gain/step 0.0536821 -> 536.821 per run  diff 1.08e-05\nsweep 80 t 316s gain/step 0.0536821 -> 536.821 per run  diff 2.26e-06\nsweep 90 t 352s gain/step 0.0536821 -> 536.821 per run  diff 7.99e-07\nsweep 100 t 389s gain/step 0.0536821 -> 536.821 per run  diff 3.69e-07\nsweep 110 t 425s gain/step 0.0536821 -> 536.821 per run  diff 1.88e-07\nSOLVED lam 0.8 sigma 9.45 size 20.0: 536.821 per run -> test/scratch/posterior-b/regenerated.json\n","credit":"Taylor (gtmcknight)","command":"DP_SAVEVN=1 DP_AMINL=-12 DP_NA=43 NUMBA_NUM_THREADS=8 timeout 470 python3 -I test/scratch/posterior-b/solve_driver.py","exit_code":0,"policy_table_comparison":"All six A0/B0/A1/B1/A2/B2 arrays equal pinned pol_mid.json, element for element.","source_url":"https://raw.githubusercontent.com/gtmcknight/amm-sim/4812a6382311624e1506aca131ca35c378f930d9/research/solved/dp.py","source_sha256":"ca4e7c378d23c46c7def4716611135c471a831d15234b984fe30d58d9eec5734","source_encoding":"zlib+base64; decoded UTF-8 bytes are unmodified pinned dp.py","source_zlib_base64":"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","source_decode_python":"import base64,zlib; dp_source=zlib.decompress(base64.b64decode(value['source_zlib_base64']))","required_python_dependencies":["numpy","numba"],"array_storage":"VN arrays are not embedded. dp.py plus solve_driver.py regenerates them; array hashes are recorded above.","timing_seconds_last_sweep":425},"commands_timings":[{"command":"git clone https://github.com/gtmcknight/amm-sim test/scratch/posterior-b/repo; git checkout pinned commit; git rev-parse HEAD","status":"success","seconds":null},{"command":"Fetch four artifact URLs with urllib.request; SHA256; extract fenced code and base64 blob.","status":"all hashes match","seconds":null},{"command":"python3 -I test/scratch/posterior-b/gates.py","status":"exact match to README at 6dp","seconds":null},{"command":"python3 -I test/scratch/posterior-b/qtgate.py","status":"exact match to all three requested qt1 values at 6dp","seconds":null},{"command":"DP_SAVEVN=1 DP_AMINL=-12 DP_NA=43 NUMBA_NUM_THREADS=8 timeout 470 python3 -I test/scratch/posterior-b/solve_driver.py","status":"success","seconds_last_sweep":425},{"command":"python3 -I check_objective.py; python3 -I check_vn.py; python3 -I check_invariance.py","status":"pass","seconds":null},{"command":"timeout 470 python3 -I test/scratch/posterior-b/evaluate.py","exit_code":124,"timeout_seconds":470,"completed_seeds":30}],"limitations":["Gate Q does not pass: full 1024 choose and 2048 report seeds were not completed.","The one-seed pilot lost substantially to COMBO; it is not evidence of a useful policy or an upper bound.","The 5bps value/action lattice, 2.5bps posterior cells, finite action search, interpolation, clipping, and sub-grid probes are approximations. No rigorous upper bound is claimed.","Actual VN point-mass equality was tested only at three phase-0 lattice states; off-grid and phase-1/2 numerical errors are not comprehensively bounded.","Cflag follows the exact requested likelihood intervention in the normal likelihood path. The inherited lattice belief-death fallback pins without evaluating a likelihood; this exceptional path is not separately certified against the flag specification.","No contract, submission, engine test, Gate F, or leaderboard-score conversion was attempted.","Execution dependencies and all large model arrays are not bundled as an offline runtime; regeneration source and hashes are included.","Unknown command timings are null rather than inferred."]}