{"credit":"Taylor (gtmcknight), github.com/gtmcknight/amm-sim@4812a6382311624e1506aca131ca35c378f930d9 research/solved","item":"three-way","where":"replica_choose","gain_vs_base":0.00490337470684854,"se":0.012526799296761485,"n":256,"seeds":[64000,64255],"advance":false,"source_sha256":"701dbfd1175fee7d129de92db21b1ddc78645796c422b29d8efba2eda501ab00","source_hash_scope":"Measured integer policy implementation qlxcore.py, unchanged; arm identity also requires parameters, enginecore_f.py and tables hashes below. Solidity recipe outputs were not engine-measured.","selected_parameters":null,"selected_index":null,"metric_note":"All settings stopped. Top-level moments describe only the largest terminal choosing mean (diagnostic_index=0); it is not a selected or frozen policy, and unequal sample counts are not a ranking for promotion.","paired_moments":{"sum_difference":1.2552639249532263,"sum_squared_difference":10.249938365941373,"sum_baseline":136287.25165067613,"sum_candidate":136288.50691460108,"sum_baseline_squared":74526598.7505764,"sum_candidate_squared":74528755.14273755,"sum_cross":74527671.82168779},"baseline":{"name":"QLX5","source_sha256":"e183e5bc3fb794b7067703b10a7da9920267aa5f4caa968e16ca101b02338f40","replica_parameters":"all defaults; split_nc=5, split_window=7, pull_alpha=350000000000000000","url":"https://api.imd.fun/artifacts/1ef80bfb6d6791b877b1f4048a5e63cf2ae54153e6e8763ae3f8ab13132dfb15"},"baseline_smoke":{"passed":true,"rows":[{"seed":10010512,"expected_edge":606.1300985640944,"actual_edge":606.1300985640944,"expected_hex":"4082f10a711db056","actual_hex":"4082f10a711db056","passed":true,"calls":12227},{"seed":10010513,"expected_edge":482.77829524211705,"actual_edge":482.77829524211705,"expected_hex":"407e2c73e5b63867","actual_hex":"407e2c73e5b63867","passed":true,"calls":9988},{"seed":10010514,"expected_edge":404.7966542836623,"actual_edge":404.7966542836623,"expected_hex":"40794cbf188fe8c4","actual_hex":"40794cbf188fe8c4","passed":true,"calls":9532},{"seed":10010515,"expected_edge":450.2556494695746,"actual_edge":450.2556494695746,"expected_hex":"407c241723e5f101","actual_hex":"407c241723e5f101","passed":true,"calls":10484}],"source":"https://api.imd.fun/artifacts/362fb97f041087bf3548c302af4c0f82d2537d6a6c0157a95ac06d0cb70a5ce4","payload_member":"b2r3/runs/per_seed_512.csv","comparison":"exact binary64 equality against engine_edge_unrounded / engine_hex; not six-decimal saved rows"},"choosing":[{"index":0,"parameters":{"split_nc":3,"split_window":11,"pull_alpha":150000000000000000},"n":256,"round_count":2,"stop":true,"stop_reason":"cumulative mean+2se<0.03","rounds":[{"n":128,"seeds":[64000,64127],"gain_vs_base":0.003422025290088282,"se":0.016335784532884763,"upper_2se":0.03609359435585781,"stop":false,"paired_moments":{"sum_difference":0.4380192371313001,"sum_squared_difference":4.339540224998441,"sum_baseline":68479.72191329296,"sum_candidate":68480.15993253009,"sum_baseline_squared":37723031.65540793,"sum_candidate_squared":37723959.44074601,"sum_cross":37723493.37830686},"elapsed_s":70.23218560218811},{"n":256,"seeds":[64000,64255],"gain_vs_base":0.00490337470684854,"se":0.012526799296761485,"upper_2se":0.02995697330037151,"stop":true,"paired_moments":{"sum_difference":1.2552639249532263,"sum_squared_difference":10.249938365941373,"sum_baseline":136287.25165067613,"sum_candidate":136288.50691460108,"sum_baseline_squared":74526598.7505764,"sum_candidate_squared":74528755.14273755,"sum_cross":74527671.82168779},"elapsed_s":41.986295223236084}],"solidity_recipe_output_sha256":"aa5e0c05f8c7db4d40def4076598502cf11aeca98d69edde07aed0164bcb43dd"},{"index":1,"parameters":{"split_nc":3,"split_window":11,"pull_alpha":250000000000000000},"n":128,"round_count":1,"stop":true,"stop_reason":"cumulative mean+2se<0.03","rounds":[{"n":128,"seeds":[64000,64127],"gain_vs_base":0.0031785932598400457,"se":0.011131350399255275,"upper_2se":0.025441294058350595,"stop":true,"paired_moments":{"sum_difference":0.40685993725952585,"sum_squared_difference":2.015524811828297,"sum_baseline":68479.72191329296,"sum_candidate":68480.12877323023,"sum_baseline_squared":37723031.65540793,"sum_candidate_squared":37723262.82297305,"sum_cross":37723146.23142808},"elapsed_s":70.23318481445312}],"solidity_recipe_output_sha256":"82e2c8d300d121a97a2933a8ff8d9b0a887a8fbee502909f6dd64e9c2e32bd59"},{"index":2,"parameters":{"split_nc":3,"split_window":17,"pull_alpha":150000000000000000},"n":256,"round_count":2,"stop":true,"stop_reason":"cumulative mean+2se<0.03","rounds":[{"n":128,"seeds":[64000,64127],"gain_vs_base":0.0049749442657733844,"se":0.016310002938988207,"upper_2se":0.0375949501437498,"stop":false,"paired_moments":{"sum_difference":0.6367928660189932,"sum_squared_difference":4.327527289076821,"sum_baseline":68479.72191329296,"sum_candidate":68480.35870615898,"sum_baseline_squared":37723031.65540793,"sum_candidate_squared":37724148.76709335,"sum_cross":37723588.04748699},"elapsed_s":70.23399519920349},{"n":256,"seeds":[64000,64255],"gain_vs_base":0.004318766910872851,"se":0.01230331802636983,"upper_2se":0.028925402963612512,"stop":true,"paired_moments":{"sum_difference":1.1056043291834499,"sum_squared_difference":9.886315144811427,"sum_baseline":136287.25165067613,"sum_candidate":136288.3572550053,"sum_baseline_squared":74526598.7505764,"sum_candidate_squared":74528687.99990857,"sum_cross":74527638.4320849},"elapsed_s":41.987560749053955}],"solidity_recipe_output_sha256":"652c8b48a4e8aed51f7138bfe8a063f040c2f02b92b9532468c3e2aba8de036d"},{"index":3,"parameters":{"split_nc":3,"split_window":17,"pull_alpha":250000000000000000},"n":128,"round_count":1,"stop":true,"stop_reason":"cumulative mean+2se<0.03","rounds":[{"n":128,"seeds":[64000,64127],"gain_vs_base":0.0005218877385924081,"se":0.011189432344431864,"upper_2se":0.022900752427456136,"stop":true,"paired_moments":{"sum_difference":0.06680163053982824,"sum_squared_difference":2.0353412714265833,"sum_baseline":68479.72191329296,"sum_candidate":68479.7887149235,"sum_baseline_squared":37723031.65540793,"sum_candidate_squared":37722849.569868475,"sum_cross":37722939.59496757},"elapsed_s":70.23494100570679}],"solidity_recipe_output_sha256":"b44de30b344029ee441e76463684704b27246707d2117003027f6de1545b4810"}],"freeze_record":null,"report":{"opened":false,"reason":"all four settings stopped on choosing data; no report run","rounds":[],"pairs":{"credit":"Taylor (gtmcknight), github.com/gtmcknight/amm-sim@4812a6382311624e1506aca131ca35c378f930d9 research/solved","encoding":"base64(zlib(little-endian records: uint32 seed, binary64 QLX5 edge, binary64 candidate edge)); 20 bytes/record, unrounded","n":0,"sha256_uncompressed":"e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855","data":"eNoDAAAAAAE="}},"method":{"workers":8,"host_inspected":false,"round_size":128,"choose_block":[64000,65023],"report_block":[10000,12047],"steps_per_seed":10000,"stop":"After each full 128-seed round stop iff cumulative paired mean + 2*SE < 0.03. No 3SE or positive early-success stop.","selection":"Survivors must finish all 1024 choosing seeds; maximize completed choosing mean, grid index breaks ties; freeze before opening report.","statistics":"Imported pinned kit score.py summarize directly on unrounded candidate-minus-QLX5 differences: statistics.mean; statistics.stdev(ddof=1)/sqrt(n). Additional paired moments use math.fsum.","statistics_source":"https://raw.githubusercontent.com/identity-md-launches/goal-3178-build-shared-evaluation-kit/45e7563d1a29ffca9d608fdf60c90bf2624d2d01/score.py","no_AGE":true,"semantics":"Only the three named knobs change; shipped integer arithmetic, production tables, and enginecore_f corrected exp are preserved. Rounded-reserve k is recomputed by shipped core."},"hashes":{"qlxcore/qlxcore.py":"701dbfd1175fee7d129de92db21b1ddc78645796c422b29d8efba2eda501ab00","qlxcore/enginecore_f.py":"60af56a7570fabd603c6374d38a38f88b3c35a8f1e8192f588769460a96bfcfd","qlxcore/qlx_tables.json":"8c6d33c5c1024bc23593f46123aad1712c0cc8af3f4a118c5875258f9d7b81a6","qlxcore/mktables.py":"2b373f0c821f501ea67538f9bbaf2f3be9c1c44a7152cb5f4d32622e1a8a8e6a","score.py":"7b9fa5e37695a5d1b048efe91a8b6c45d31bd4245a733261774c871fe9105a43","search.py":"89992ce462c26be52323a43c99d3df3f0cfc6230534d8799270cc0c5d81608fa","recipe.py":"8d0c48fa98157dc73e1aefbbf84fade881ede5c83333e3dcc3b4d44b3ce83e26","policy_patch.py":"f6e69877d902de6fbb9d553fcbe028c72a6a710f2b33da0e693e4c916ad2fda8"},"timings":{"total_through_search_s":194.56693243980408,"cap_s":2700,"completed_within_cap":true,"setup_s":5.717,"round_timings_note":"A choosing round wall time is shared across all its active arms and baseline; do not sum it across arms.","total_through_validation_s":367.93584299087524},"provenance":{"artifacts":{"replica":"https://api.imd.fun/artifacts/60dc237fa0a4d42f3b6e2d2bed61c1a2df08d27c31ccd95cce508ee9c8689de4","audit":"https://api.imd.fun/artifacts/362fb97f041087bf3548c302af4c0f82d2537d6a6c0157a95ac06d0cb70a5ce4","base":"https://api.imd.fun/artifacts/1ef80bfb6d6791b877b1f4048a5e63cf2ae54153e6e8763ae3f8ab13132dfb15"},"kit_commit":"45e7563d1a29ffca9d608fdf60c90bf2624d2d01","kit_setup":"https://raw.githubusercontent.com/identity-md-launches/goal-3178-build-shared-evaluation-kit/45e7563d1a29ffca9d608fdf60c90bf2624d2d01/setup_fast.sh","runtime":{"python":"3.12.16","numpy":"2.5.4","numba":"0.68.1","llvmlite":"0.50.0"},"local_evidence":"Four locally recomputed audit rows and all locally simulated choosing/report pairs; prior 512-row parity is an upstream audit claim, not re-run here."},"limitations":["Replica evidence only. No engine E seeds used, no contract promotion and no submission.","No new Solidity compilation, deployed-byte size, gas peak, OOG or P/T/F/G/E certification for these variants; Python integers do not model EVM overflow or gas.","Only four historical baseline audit rows reproduced locally; variant contract equivalence has not been certified.","A futility-stopped arm is not proof of zero benefit; selection and optional stopping make choosing means exploratory.","Pinned Python/numpy/numba/llvmlite runtime is required to execute the replica; the runtime installation is intentionally not part of the two JSON deliverables."],"embedded":{"policy_patch_py":"# Credit: Taylor (gtmcknight), github.com/gtmcknight/amm-sim@4812a6382311624e1506aca131ca35c378f930d9 research/solved.\n\"\"\"Usage: python policy_patch.py INDEX SEED (place beside exact qlxcore.py and tables).\nINDEX=-1 is unchanged QLX5; INDEX=0..3 is the ordered bounded grid.\n\"\"\"\nimport hashlib,pathlib,json,sys\nimport qlxcore\nP=pathlib.Path(qlxcore.__file__).resolve().parent\nfor name,h in {'qlxcore.py':'701dbfd1175fee7d129de92db21b1ddc78645796c422b29d8efba2eda501ab00','enginecore_f.py':'60af56a7570fabd603c6374d38a38f88b3c35a8f1e8192f588769460a96bfcfd','qlx_tables.json':'8c6d33c5c1024bc23593f46123aad1712c0cc8af3f4a118c5875258f9d7b81a6'}.items():\n assert hashlib.sha256((P/name).read_bytes()).hexdigest()==h\nGRID=[dict(split_nc=3,split_window=w,pull_alpha=a) for w,a in [(11,150000000000000000),(11,250000000000000000),(17,150000000000000000),(17,250000000000000000)]]\ni,seed=map(int,sys.argv[1:3]);assert -1<=i<=3\nprint(json.dumps(dict(credit=qlxcore.CREDIT,parameters={} if i==-1 else GRID[i],result=qlxcore.run(seed,**({} if i==-1 else GRID[i]))),separators=(',',':')))\n","exact_solidity_edit_recipe_py":"# Credit: Taylor (gtmcknight), github.com/gtmcknight/amm-sim@4812a6382311624e1506aca131ca35c378f930d9 research/solved.\n\"\"\"Exact Solidity edit recipe. Usage: python recipe.py QLX5.sol OUT.sol WINDOW ALPHA_WAD.\nThis only writes source; it does not certify, compile, promote or submit a contract.\n\"\"\"\nimport hashlib,pathlib,sys\nsrc=pathlib.Path(sys.argv[1]).read_bytes()\nassert hashlib.sha256(src).hexdigest()=='e183e5bc3fb794b7067703b10a7da9920267aa5f4caa968e16ca101b02338f40'\nw,a=map(int,sys.argv[3:5]);assert (w,a) in [(11,150000000000000000),(11,250000000000000000),(17,150000000000000000),(17,250000000000000000)]\ns=src.decode()\nfor old,new in [('t.timestamp<(s2>>128&M64)+7','t.timestamp<(s2>>128&M64)+'+str(w)),('t.isBuy,m0,5);','t.isBuy,m0,3);'),('mul(35e16,ln(c)-lr)','mul('+str(a)+',ln(c)-lr)')]:\n assert s.count(old)==1,(old,s.count(old));s=s.replace(old,new)\npathlib.Path(sys.argv[2]).write_bytes(s.encode());print(hashlib.sha256(s.encode()).hexdigest())\n","search_driver_py":"# Credit: Taylor (gtmcknight), github.com/gtmcknight/amm-sim@4812a6382311624e1506aca131ca35c378f930d9 research/solved.\nimport pathlib,sys,json,time,csv,struct,hashlib,multiprocessing,math,datetime\nP=pathlib.Path(__file__).resolve().parent\nsys.path.insert(0,str(P/'qlxcore'))\nimport qlxcore,score\nCREDIT=qlxcore.CREDIT\nGRID=[dict(split_nc=3,split_window=w,pull_alpha=a) for w,a in [(11,150000000000000000),(11,250000000000000000),(17,150000000000000000),(17,250000000000000000)]]\nT=qlxcore.load_tables()\nSTART=float((P/'start').read_text())\ndef save(name,obj):\n (P/name).write_text(json.dumps(obj,separators=(',',':'),allow_nan=False))\ndef job(t):\n i,s=t; t0=time.process_time();r=qlxcore.run(s,T,**({} if i<0 else GRID[i]));return i,s,r,time.process_time()-t0\n\ndef summarize(rows):\n pairs=[(s,c-b) for s,b,c in rows];out=score.summarize(pairs)\n ds=[d for s,d in pairs];bs=[b for s,b,c in rows];cs=[c for s,b,c in rows]\n out['paired_moments']={'sum_difference':math.fsum(ds),'sum_squared_difference':math.fsum(d*d for d in ds),'sum_baseline':math.fsum(bs),'sum_candidate':math.fsum(cs),'sum_baseline_squared':math.fsum(b*b for b in bs),'sum_candidate_squared':math.fsum(c*c for c in cs),'sum_cross':math.fsum(b*c for b,c in zip(bs,cs))}\n return out\n\ndef main():\n qlxcore.run(10010512,T,n_steps=50)\n state=dict(credit=CREDIT,workers=8,grid=GRID,smoke=[],choosing=[dict(index=i,parameters=k,rounds=[],rows=[],stopped=False) for i,k in enumerate(GRID)],report=None,freeze=None,advance=False)\n with multiprocessing.get_context('fork').Pool(8) as pool:\n  audit=list(csv.DictReader(open(P/'b2r3/runs/per_seed_512.csv')))[:4]\n  smoke=pool.map(job,[(-1,int(r['seed'])) for r in audit],chunksize=1)\n  for (_,s,r,cpu),ref in zip(smoke,audit):\n   actual=struct.pack('>d',r['edge']).hex(); state['smoke'].append(dict(seed=s,expected_edge=float(ref['engine_edge_unrounded']),actual_edge=r['edge'],expected_hex=ref['engine_hex'],actual_hex=actual,passed=actual==ref['engine_hex'],calls=r['calls']))\n  state['baseline_smoke_pass']=all(r['passed'] for r in state['smoke']); save('state.json',state)\n  print('SMOKE',json.dumps(state['smoke']),flush=True)\n  if not state['baseline_smoke_pass']:return\n  for offset in range(0,1024,128):\n   active=[x for x in state['choosing'] if not x['stopped']]\n   if not active:break\n   if time.time()-START>2600:state['unfinished']=True;break\n   seeds=list(range(64000+offset,64128+offset));t0=time.time()\n   results=pool.map(job,[(i,s) for s in seeds for i in [-1]+[x['index'] for x in active]],chunksize=1)\n   by={(i,s):r['edge'] for i,s,r,cpu in results}\n   for arm in active:\n    i=arm['index'];arm['rows'].extend([[s,by[-1,s],by[i,s]] for s in seeds]);st=summarize(arm['rows']);st['stop']=st['mean_diff']+2*st['se']<.03;st['elapsed_s']=time.time()-t0;arm['rounds'].append(st);arm['stopped']=st['stop'];print('CHOOSE',i,offset+128,st['mean_diff'],st['se'],st['stop'],flush=True)\n   save('state.json',state)\n  survivors=[a for a in state['choosing'] if not a['stopped'] and len(a['rows'])==1024]\n  if survivors and not state.get('unfinished'):\n   best=max(survivors,key=lambda a:(a['rounds'][-1]['mean_diff'],-a['index']))\n   state['freeze']={'credit':CREDIT,'index':best['index'],'parameters':best['parameters'],'choosing_mean':best['rounds'][-1]['mean_diff'],'choosing_n':1024,'utc':datetime.datetime.now(datetime.timezone.utc).isoformat(),'report_opened':False}\n   save('freeze.json',state['freeze']);save('state.json',state)\n   print('FREEZE',state['freeze'],flush=True)\n   state['report']=dict(rows=[],rounds=[],stopped=False)\n   for offset in range(0,2048,128):\n    if time.time()-START>2600:state['unfinished']=True;break\n    seeds=list(range(10000+offset,10128+offset));t0=time.time();i=best['index']\n    results=pool.map(job,[(j,s) for s in seeds for j in [-1,i]],chunksize=1)\n    by={(j,s):r['edge'] for j,s,r,cpu in results};rp=state['report'];rp['rows'].extend([[s,by[-1,s],by[i,s]] for s in seeds]);st=summarize(rp['rows']);st['stop']=st['mean_diff']+2*st['se']<.03;st['elapsed_s']=time.time()-t0;rp['rounds'].append(st);rp['stopped']=st['stop'];save('state.json',state)\n    print('REPORT',offset+128,st['mean_diff'],st['se'],st['stop'],flush=True)\n    if st['stop']:break\n   state['advance']=len(state['report']['rows'])==2048 and state['report']['rounds'][-1]['mean_diff']>=.03 and state['baseline_smoke_pass'] and not state.get('unfinished',False)\n state['elapsed_s']=time.time()-START;state['complete']=not state.get('unfinished',False);save('state.json',state);print('DONE',state['elapsed_s'],state['advance'],flush=True)\nif __name__=='__main__':main()\n","replica_source_bundle":{"encoding":"base64(zlib(UTF-8 JSON 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"}},"reproduce":["Decode embedded source bundle if present, or decode code_zb64 from the replica artifact (base64/zlib/tar) and verify all full hashes. Generate production tables with mktables.py from the baseline artifact and verify the table hash.","Install pinned kit replica runtime using setup_fast.sh --platform linux-x86_64 --replica-only --cache <scratch>/cache --scratch <scratch>/runtime. All downloads, caches and runtime state stay in test/scratch/.","Put embedded policy_patch_py next to qlxcore.py and run INDEX SEED. INDEX -1 is unchanged QLX5, 0..3 is grid order. The embedded search_driver_py records the actual experiment; it expects replica directory qlxcore/, score.py, start (epoch time), and decoded audit CSV under b2r3/runs/.","Exact Solidity edit recipe takes QLX5.sol OUT.sol WINDOW ALPHA_WAD; it asserts the baseline hash and exactly one occurrence per replacement. Result hashes are listed per arm. These source recipes are not contract promotions."],"choosing_pairs":{"credit":"Taylor (gtmcknight), github.com/gtmcknight/amm-sim@4812a6382311624e1506aca131ca35c378f930d9 research/solved","encoding":"base64(zlib(binary)); little endian. First baseline_n binary64 QLX5 edges for consecutive seeds from first_seed. Then one uint64 XOR(candidate_bits, baseline_bits) per candidate seed, in grid order, each starting at first_seed; arm_counts gives lengths. Decode XOR against the shared baseline bits to recover unrounded candidate edges exactly.","first_seed":64000,"baseline_n":256,"arm_counts":[256,128,256,128],"sha256_uncompressed":"e445856a37cc58584739dcd7281be19dafaa8ca53fe4c2f4b44ed69f429d530b","data":"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"},"validation":{"json_and_bytes":"pass","compressed_choosing_pairs_round_trip":768,"round_statistics_and_stop_checks":6,"live_source_quotes_verified":5,"embedded_source_hashes":"pass","embedded_policy_rerun":{"index":0,"seed":64000,"edge":449.92568721032984,"exact_match":true},"authority":"Local checks only; not independent engine certification."}}