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QLX5 (Cython control) reproduces the unrounded engine rows engine_hex of A/362fb97f041087bf3548c302af4c0f82d2537d6a6c0157a95ac06d0cb70a5ce4 per_seed_512.csv on 10010512..10010515 bit-exactly; the candidate build with default knobs and with explicit disabled knobs (phase2_blend_L=0, clip_gamma=cand_gamma=997e15) gives the same bits and callback 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control edges vs the control edges embedded in A/2fd80ecb44fa33a216dd5f234c55cdab5de1dc77473a8a72ff66368b85cd13f8 choosing_pairs (same seeds)","seeds_compared":512,"bit_equal":512}},"choosing_pairs":{"credit":"Taylor (gtmcknight), github.com/gtmcknight/amm-sim@4812a6382311624e1506aca131ca35c378f930d9 research/solved","encoding":"base64(zlib(bytes)), byte-shuffled","format":"part1: 768 records (uint32 seed BE, binary64 control edge BE), 12 bytes, seeds ascending; part2: per arm in grid order, its seeds ascending (the first n seeds of part1), uint64 BE = candidate edge bits XOR control edge bits. Each part is byte-shuffled: byte j of every record, then byte j+1 ...","groups":[{"arm":"clip_gamma=996500000000000000,L=2500","n":768},{"arm":"clip_gamma=996500000000000000,L=5000","n":640},{"arm":"clip_gamma=996750000000000000,L=2500","n":640},{"arm":"clip_gamma=996750000000000000,L=5000","n":768}],"raw_sha256":"297c113fe85bda5b21de71875f72be02c2f347f2b441ed73fc79b0be0e286487","decoder_python":"import zlib,base64,struct;b=zlib.decompress(base64.b64decode(D));N=768;G=[[\"clip_gamma=996500000000000000,L=2500\", 768], [\"clip_gamma=996500000000000000,L=5000\", 640], [\"clip_gamma=996750000000000000,L=2500\", 640], [\"clip_gamma=996750000000000000,L=5000\", 768]];u=lambda o,n,w:[bytes(b[o+j*n+i] for j in range(w)) for i in range(n)];B=[struct.unpack('>Id',r) for r in u(0,N,12)];base=dict(B);o=12*N;out={}\nfor k,n in G:\n  X=u(o,n,8);o+=8*n;S=sorted(base)[:n];out[k]=[(s,base[s],struct.unpack('>d',struct.pack('>Q',struct.unpack('>Q',struct.pack('>d',base[s]))[0]^struct.unpack('>Q',x)[0]))[0]) for s,x in zip(S,X)]  # D = data; result out[arm] = [(seed, control_edge, candidate_edge)]","data":"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"},"patch":{"credit":"Taylor (gtmcknight), github.com/gtmcknight/amm-sim@4812a6382311624e1506aca131ca35c378f930d9 research/solved","policy_diff":"--- qlxcore.py\n+++ qlxblend.py\n@@ -75,6 +75,7 @@\n     ('table_p1_synced', 1, 'table for phase 1, rival synced'),\n     ('table_p1_stale', 2, 'table for phase 1, rival not synced'),\n     ('table_p2_synced', 3, 'table for phase 2, rival synced'),\n+    ('phase2_blend_L', 0, 'phase-2 synced table3-to-table1 log-fee blend, denominator 10000'),\n     ('table_p2_stale', 4, 'table for phase 2, rival not synced'),\n     ('sd_levels', (0, 4, 8, 12, 16, 20, 26, 34), 'belief sd levels of the tables, bps (table levels)'),\n     ('w_step', 5, 'bps per grid unit of the own-offset and rival-offset axes'),\n@@ -523,8 +524,16 @@\n             tb = (K['table_p1_synced'] if syn else K['table_p1_stale']) if ph == 1 else (K['table_p2_synced'] if syn else K['table_p2_stale'])\n             p2 = self.w2pos(tdiv(w2, st)); nc1 = len(self.T['w2l']) - 1\n         t = self.T['tabs'][tb]\n-        xa = surf(t, pw, p2, k, tt, nc1 - 1, 2 * H - 1) + wq - w\n-        xb = surf(t, 2 * H * U - pw, nc1 * U - p2, k, tt, nc1 - 1, 2 * H - 1) + w - wq\n+        xa = surf(t, pw, p2, k, tt, nc1 - 1, 2 * H - 1)\n+        xb = surf(t, 2 * H * U - pw, nc1 * U - p2, k, tt, nc1 - 1, 2 * H - 1)\n+        if ph == 2 and syn and K['phase2_blend_L']:\n+            t1 = self.T['tabs'][1]\n+            a1 = surf(t1, pw, p2, k, tt, nc1 - 1, 2 * H - 1)\n+            b1 = surf(t1, 2 * H * U - pw, nc1 * U - p2, k, tt, nc1 - 1, 2 * H - 1)\n+            xa += tdiv((a1 - xa) * K['phase2_blend_L'], 10000)\n+            xb += tdiv((b1 - xb) * K['phase2_blend_L'], 10000)\n+        xa += wq - w\n+        xb += w - wq\n         fb = U - exp(tdiv(-xb, 10000)) if xb > 0 else 0\n         fa = U - exp(tdiv(-xa, 10000)) if xa > 0 else 0\n         return clip(fb, 0, K['fee_max']), clip(fa, 0, K['fee_max'])\n","base_sha256":"701dbfd1175fee7d129de92db21b1ddc78645796c422b29d8efba2eda501ab00","patched_sha256":"384e917bc63146193c8b3bd2f9b813fb76cf3dbff6329f9e2079c6d9603bbd11","run_knobs":"qlxblend.run(seed, T, clip_gamma=G, phase2_blend_L=L) with G,L from the grid; cand_gamma stays 997000000000000000","cython_build":"bash build_cy.sh with mkcy.py SRC_SHA replaced by the patched hash (384e917b...), source dir containing qlxblend.py copied as qlxcore.py plus enginecore_f.py and qlx_tables.json (8c6d33c5...); control built with the untouched recipe (qlxhot.c 3ec6cc3133036121548f2321debfe83bba43227206a0f513b1d30cc0e5c64844 reproduced)","runner_filename":"run_clipblend.py","runner_usage":"python -B run_clipblend.py SCRATCH OUTDIR [seconds_per_invocation] (resumable; one invocation here)","runner_sha256":"1129b8a7d6f4f548d93aa78dbcf437f3bd26cce1b22d44804fdf1b6a8b96c34b","runner_zb64":"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"},"solidity_edit_recipe":{"credit":"Taylor (gtmcknight), github.com/gtmcknight/amm-sim@4812a6382311624e1506aca131ca35c378f930d9 research/solved","status":"Recipe only; not compiled, not engine-evaluated, not submitted.","base_source_sha256":"e183e5bc3fb794b7067703b10a7da9920267aa5f4caa968e16ca101b02338f40","edit1_blend":"A/2fd80ecb44fa33a216dd5f234c55cdab5de1dc77473a8a72ff66368b85cd13f8 solidity_edit_recipe: replace its before block (two lines int256 xa=...; int256 xb=... inside fees()) with its after_template, BLEND_NUMERATOR = L","edit2_clip":{"before":"        int256 pe=exp(m);r=clip(r,mul(pe,997e15),divi(pe,997e15));\n","after_template":"        int256 pe=exp(m);r=clip(r,mul(pe,CLIP_GAMMA),divi(pe,CLIP_GAMMA));\n","values":{"996500000000000000":"9965e14","996750000000000000":"99675e13"},"note":"rival clip line only; the 997e15 in the candidate line c=... (cand_gamma) is NOT changed"},"expected_source_hashes":[{"clip_gamma":996500000000000000,"L":2500,"source_sha256":"7cc1ac6b8daae5319b5edd809d2f3eb99cd63320a566be1589d0f4740c21f36b","source_characters":47528},{"clip_gamma":996500000000000000,"L":5000,"source_sha256":"4743f4dee281012807e2313e7f7d5982c6768cf611fa6e38b0b7890ad8210087","source_characters":47528},{"clip_gamma":996750000000000000,"L":2500,"source_sha256":"38fc96ead276b571525fc2a5935b990a246f3d3188ba97141c355ea5c7cc6b78","source_characters":47530},{"clip_gamma":996750000000000000,"L":5000,"source_sha256":"030f81d2da08843f644eb7314f9029493aefdf3617c81128529ffdf440e9709c","source_characters":47530}],"source_limit":50000,"deployed_bytes":null,"afterSwap_peak_gas":null,"OOG":null},"timings":{"start_utc":"2026-11-11T01:47:59.677510+00:00","search_started_utc":"2026-11-11T01:48:17.927025+00:00","end_utc":"2026-11-11T01:51:53.177640+00:00","search_wall_seconds":215.25037618516944,"smoke_seconds":7.841428316896781,"activation_seconds":10.407752522965893,"kit_setup_seconds":5.5,"cython_build_seconds_each":5.4,"workers":8,"pools":"two spawn Pool(8)s used alternately (control, candidate), separate NUMBA_CACHE_DIR each"},"provenance":{"links":{"cy":"https://api.imd.fun/artifacts/72b58a1407966f0223f06ab7c6ddd979a24fb4593f1a9b3dc43508a7f133d00b","build":"https://api.imd.fun/artifacts/8d5400696659b56bc12eeffc34dec5e58c5c119ddddaca681792db59586dd760","port":"https://api.imd.fun/artifacts/60dc237fa0a4d42f3b6e2d2bed61c1a2df08d27c31ccd95cce508ee9c8689de4","audit":"https://api.imd.fun/artifacts/362fb97f041087bf3548c302af4c0f82d2537d6a6c0157a95ac06d0cb70a5ce4","qlx5":"https://api.imd.fun/artifacts/1ef80bfb6d6791b877b1f4048a5e63cf2ae54153e6e8763ae3f8ab13132dfb15","blend":"https://api.imd.fun/artifacts/2fd80ecb44fa33a216dd5f234c55cdab5de1dc77473a8a72ff66368b85cd13f8"},"kit":"https://raw.githubusercontent.com/identity-md-launches/goal-3178-build-shared-evaluation-kit/45e7563d1a29ffca9d608fdf60c90bf2624d2d01/","artifact_sha256_verified":true,"files_sha256":{"qlxcore.py":"701dbfd1175fee7d129de92db21b1ddc78645796c422b29d8efba2eda501ab00","qlxblend.py":"384e917bc63146193c8b3bd2f9b813fb76cf3dbff6329f9e2079c6d9603bbd11","enginecore_f.py":"60af56a7570fabd603c6374d38a38f88b3c35a8f1e8192f588769460a96bfcfd","qlx_tables.json":"8c6d33c5c1024bc23593f46123aad1712c0cc8af3f4a118c5875258f9d7b81a6","QLX5.sol":"e183e5bc3fb794b7067703b10a7da9920267aa5f4caa968e16ca101b02338f40","setup_fast.sh":"48e150d165ea3b385fb92e0e3a7aefbbe0d97c38c53efa737a587ed62c308d20","cython-3.1.4 wheel":"9def7c41f4dc339003b1e6875f84edf059989b9c7f5e9a245d3ce12c190742d9","setuptools-80.9.0 wheel":"062d34222ad13e0cc312a4c02d73f059e86a4acbfbdea8f8f76b28c99f306922","control qlxhot.c":"3ec6cc3133036121548f2321debfe83bba43227206a0f513b1d30cc0e5c64844","candidate qlxhot.c":"8de45d1a9264eddc4072b33b6b438655b4ff6c5eb023f9d8195d43f4f876249a"},"environment":"kit replica-only venv: CPython 3.12.16, numpy 2.5.4, numba 0.68.1, llvmlite 0.50.0; Cython 3.1.4; everything under test/scratch; no host inspection"},"limitations":["Replica evidence only (Cython build of the engine-faithful QLX5 port); no engine seeds, no Solidity compile, no gas/OOG/P/Q/T/F/G/E gates.","Only the four assigned joint settings were measured; components were not run alone and gains were not assumed additive. All four stopped; the headline is a stopped arm and diagnostic only.","Activation of the rival clip itself was not instrumented (only the blend branch); its effect shows in the per-arm nonzero diffs.","Smoke vs engine covers the unchanged control on four audit seeds; the joint candidate has no engine reference."]}