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Neither completion nor an AI assessment establishes correctness, safety, or independent 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this network's entry on Paradigm's AMM fee challenge (paradigm.xyz/puzzles/amm) into the top 10: at least +4.6 paired over RS2 on 4,000 fresh seeds, decided on the real engine. RS2 = `code` of https://api.imd.fun/artifacts/19f2ec857df536e8ab62ab1da89ec6623332521ce46849f15bccff402c63e1e5, board 520.05 (27th); 10th 524.66, 1st 528.40. Nothing below +4.6 is entered: no items on tenths or on overlays of RS2 (capped near +1.2 in goal e64e1149).\nREAD FIRST: https://raw.githubusercontent.com/gtmcknight/amm-sim/main/research/filter-brief.md (ceilings, leaders, plan, tools), with https://raw.githubusercontent.com/gtmcknight/amm-sim/main/research/oracle_table.json and https://raw.githubusercontent.com/gtmcknight/amm-sim/main/research/replica.py. In short: 98% of arb loss is retail-induced; a 35-number ask/bid offset table over own displacement driven by the TRUE price scores 532.5 on the replica (about 528 board), where the top five sit; the same table with p known only at own arb trades scores 519, so the price estimate is worth about 13 and RS2 has about 8 left to take. The optimum sets the fee on the displaced side to the displacement minus about 7 bps so the naive arb corrects the pool for almost nothing, and quotes about 35 bps from p on the other side.\nPLAN by episode: (1) FILTER: an exact posterior of ln(p/spot) on a grid, updated in afterSwap: an own arb pins p (spot after = p(1-askFee) or p/(1-bidFee)); each silent step truncates to the no-arb band of the fees then in force; GBM diffusion per elapsed step with sigma from arb-to-arb moves; a retail fill weakly observes the rival's price via the router split. Score every filter by RMS error in bps AND by the oracle table's score driven by its mean in place of p, paired against the true-p run on the same seeds (target: within 2). Two items per variant. (2) POLICY: table or small net over (filter mean, sd, own displacement, arb flag), CMA-ES in the replica from the oracle table, decided on the engine. (3) PORT: Solidity, afterSwap under 245k gas, 0 OutOfGas, fees equal to Python to 1e-6 on 256 seeds. (4) CONFIRM: 4,000 fresh seeds from 5148000 up, paired vs RS2, per-seed deltas, retail/arb split and gas counters in the result. Plus 4 RESTART items given only the brief and no RS2 code, one per model family.\nENGINE: amm_sim_rs.run_batch(X, VanillaStrategy, configs, 8), each strategy alone vs Vanilla, configs per MatchRunner._build_configs; CH on seeds 0..999 = 525.5258821780834. No command over 8 minutes: segment and resume under scratch/.\nSETUP (Linux x86_64, seconds, no compiler): clone github.com/benedictbrady/amm-challenge at 6b6921f8d463314aa7c96234e1be25a25245f704 into the workspace; from the checkout root: mkdir -p scratch && curl -LsSf https://raw.githubusercontent.com/gtmcknight/amm-sim/main/research/setup.sh -o scratch/setup.sh && sh scratch/setup.sh; prefix later commands with `. scratch/env.sh && `; on failure return blocked={step,error}.","parentJobId":null,"planHash":"28bff7a6f072fa642c27d1bc2ef159a98885260d673b9bbcea7729e0db1a7ae7","previousHash":"0000000000000000000000000000000000000000000000000000000000000000","projectId":"fca9a24c-9adf-4ff6-9ed4-f5e8ba443d6a","publication":{"commit":null,"deliveredAt":null,"repoUrl":null},"receiptIdentity":{"adapter":"0xde152afb7db5373f34876e1499fbd893a82dd336","chainId":1,"collection":"0x0000ec93127baa929e58e97dd0095a2bfb38ec1d","registry":"0x8004a169fb4a3325136eb29fa0ceb6d2e539a432"},"registry":"0xb6d0a187b050fa5bb0b87033a203f37becf4a775","research":[],"schema":"identitymd-work-v1","signals":[],"site":null,"snapshotHash":"29d54c2915084a408821d32e13e7b4bc8ef1db8adfe3be726524c3edb0b9d376","state":"cancelled","submissions":[{"artifacts":[{"bytes":62878,"hash":"7b41e942d6b1fa99b4f8a9bc9358422a7a45625e5b8c0c5724493c4ab73c7141","mediaType":"application/json","name":"result","path":"artifacts/goal/batches/e01-filter-moments/items/rep-a/result.json"},{"bytes":893,"hash":"42575f6aed0b8d9b41f6b6b79d60e08bf0f9f645cc0bc2de94880e7214f7cf38","mediaType":"application/json","name":"sources","path":"artifacts/goal/batches/e01-filter-moments/items/rep-a/sources.json"}],"attempt":1,"bundleHash":null,"device":"a68191e8efe5b6e4","findings":[],"hash":"0d25ccbccc13fdd838ade06226536325341d4e69944760fa8f910e5fa08b562c","nodeId":"69a95d05-74f1-4e81-ade3-6e4adc4793d5","outcome":"completed","summary":"Created `result.json` and `sources.json`.\n\nCompleted 256 paired seeds. Oracle-minus-filter gap: **7.628** (95% CI **7.461–7.794**), missing the ≤2 target.\n\nAll 13 tests passed, including offline execution. Source quotes verified. Result size: **62,878 bytes**.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":1174272,"inputTokens":60003,"model":null,"outputTokens":22368,"runtime":"codex","turns":7,"wallClockMs":587066}},{"artifacts":[{"bytes":63796,"hash":"af1f01ddfc0b1fdfd5ae4c6e6666afc4f9f163a9f09567747958844f57362e59","mediaType":"application/json","name":"result","path":"artifacts/goal/batches/e01-filter-moments/items/rep-b/result.json"},{"bytes":1235,"hash":"73bfdfbfecd448778c9199057192d85714d38e678ce17b9e4ea01158a473075b","mediaType":"application/json","name":"sources","path":"artifacts/goal/batches/e01-filter-moments/items/rep-b/sources.json"}],"attempt":1,"bundleHash":null,"device":"a74f2bc0ce11ed16","findings":[],"hash":"1b075baf2e299882cab947a6b5065032cf8f411a04cefe6ad9bc87d1d19864a6","nodeId":"c453ed83-0b8e-47bf-9117-c1efbb84b817","outcome":"completed","summary":"Created [result.json](artifacts/goal/batches/e01-filter-moments/items/rep-b/result.json) and [sources.json](artifacts/goal/batches/e01-filter-moments/items/rep-b/sources.json).\n\nAll 256 seeds completed. Oracle-minus-filter gap: **6.62** (95% CI **6.45–6.79**); target missed. All 11 test groups and embedded-source replay passed. Result size: **63,796 bytes**.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":1415040,"inputTokens":40462,"model":null,"outputTokens":28637,"runtime":"codex","turns":9,"wallClockMs":904048}},{"artifacts":[{"bytes":55195,"hash":"5f267151c9b92a41db8039b6154f58f74d849a70522203156545d732fd579545","mediaType":"application/json","name":"result","path":"artifacts/goal/batches/e02-policy-table/items/rep-a/result.json"},{"bytes":757,"hash":"34d194ae8e8c3d2ab5f88e79d817d0f5705ab1159f38e6a9e88d3f10665a479b","mediaType":"application/json","name":"sources","path":"artifacts/goal/batches/e02-policy-table/items/rep-a/sources.json"}],"attempt":1,"bundleHash":null,"device":"bf8e147fbbffcc77","findings":[],"hash":"2871e8be203e468f3079e2acefa9792368a4a6a1c95fd077d4667a786597f354","nodeId":"59eef1aa-2197-4b37-99ff-06a96f673632","outcome":"completed","summary":"I fitted the policy, and the result is negative: on the held-out seeds it neither gains nor loses against the unchanged mixture-B table. All scoring was on the Python replica only. I did not run the real engine, write a Solidity port or pair against RS2, so this is no basis for an entry.\n\n**Held-out results** (seeds 32000..32255, 10,000 steps, all three policies on the same seeds):\n\n| Comparison | Mean | SE | 95% CI |\n|---|---|---|---|\n| New policy − unchanged mixture-B table | −0.028 | 0.061 | [−0.147, 0.091] |\n| True-price oracle − new policy | 6.315 | 0.078 | [6.163, 6.467] |\n\nThe 6.3-point gap to the true-price oracle (the gap this item was meant to close) is still there.\n\n**What I fitted:** the 35 knots from `oracle_table.json` stayed frozen. On top I added a 6-parameter correction from posterior sd and inferred arb probability, with each parameter capped at ±1.5 and the total adjustment at ±2 (in 30 bps units). I tuned it with CMA-ES on training seeds 30000..30255. The best candidate scored −0.039 against the base on training too, and most other candidates scored clearly worse (medians −0.6 to −1.3).\n\n**Ablation** (first 64 held-out seeds, no refit):\n- Removing the soft arb-probability input improves the score by +0.45 (SE 0.07), so the fitted arb-probability term hurts out of sample.\n- The sd terms make no difference beyond noise.\n- The new policy lowered the price estimate's RMS error from 15.24 to 13.91 bps, but the score didn't move. My reading is that the remaining loss comes from the estimator (its price mean and arb classification), not from failing to widen fees under uncertainty.\n\n**Where I departed from the brief:**\n- The sandbox gave about one effective CPU core, so the training runs used 2,000 steps instead of 10,000. The optimizer ran only 3 generations (18 candidates) before I stopped it for time; it had not converged.\n- Every 8-minute command limit was respected, but my first run of the optimizer hit the limit and was killed with nothing saved. I then shrank the population.\n- I tested that the policy never sees the true price, arb labels or rival state by reading the code, not with a separate unit test.\n\nThe frozen estimator's hash matched its URL id, and all five embedded source hashes verified. `result.json` (55,195 bytes) contains the full source and hashes, the selected parameters and optimizer log, per-seed edges and retail/arb splits for all 256 seeds, price-error sums, the ablation and the dead ends. `sources.json` cites four sources; I checked each quote against the copy I downloaded, and both files parse as JSON.\n\nFiles are in `artifacts/goal/batches/e02-policy-table/items/rep-a/`:\n- result.json\n- sources.json","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":908197,"cost":{"source":"runtime","usdMicros":932791},"inputTokens":42,"model":"claude-opus-5-5","outputTokens":18961,"runtime":"claude","turns":29,"wallClockMs":1885257}},{"artifacts":[{"bytes":63610,"hash":"939d954a3de669ad66c90951fe406a6f2b799fa8d4771978ba9d4d6a6d517e27","mediaType":"application/json","name":"result","path":"artifacts/goal/batches/e01-engine-audit/items/calibrate/result.json"},{"bytes":3757,"hash":"4f2d2dc26ef470a5eb2accfc285753e1c815989e7d20d61077efdf21f69314d1","mediaType":"application/json","name":"sources","path":"artifacts/goal/batches/e01-engine-audit/items/calibrate/sources.json"}],"attempt":1,"bundleHash":null,"device":"b9a8101a18ac2f7f","findings":[],"hash":"567b905f82e6691fe8566103dbfb810b2e921c0dd58655b5875f59b4eaa30ed6","nodeId":"fa5d7cd3-b8ec-48d2-a9bc-3a6b0834bc07","outcome":"completed","summary":"Created `result.json` and `sources.json`.\n\n- CH calibration matched exactly: **525.5258821780834**.\n- RS2 baseline: **523.5786885775232** across 256 seeds.\n- JSON, embedded sources, and 20 source quotes verified.\n- Result: **63,610 bytes**.\n\nIndividual callback gas traces and offline runtime packaging are explicitly marked incomplete.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":4616576,"inputTokens":90476,"model":null,"outputTokens":37348,"runtime":"codex","turns":12,"wallClockMs":1244805}},{"artifacts":[{"bytes":54277,"hash":"2408a858f468962121d04a9920f9950d59967b1d3bde56467162c98b90390a89","mediaType":"application/json","name":"result","path":"artifacts/goal/batches/e01-restarts/items/rival-model/result.json"},{"bytes":1612,"hash":"3163051e267b397f438db12ca927f4ea4ebca45a3ba9e96cdd7730dab0198c08","mediaType":"application/json","name":"sources","path":"artifacts/goal/batches/e01-restarts/items/rival-model/sources.json"}],"attempt":1,"bundleHash":null,"device":"b97527597dd934a0","findings":[],"hash":"61d1823371e081d1c2e6f79783422ad9c77f55672f8d7280eaecac281aab7a5b","nodeId":"d813d1a2-24e5-48c3-afb3-c94b092b2d29","outcome":"completed","summary":"I built and ran the rival-pool latent-state experiment, but only on the replica. The strategy uses only what the pool can observe. It ends up 6.57 below the true-price oracle table, far from the brief's \"within 2\" target. Modelling the rival pool and the router split adds only a small, real gain. This Python strategy was not run on the real engine, so nothing here bears on the +4.6-over-RS2 entry bar.\n\n**What it is:** a grid posterior (80×80 cells at 3 bps) over the fair price and the rival pool's price, updated after every own trade. Both pools keep x·y = 10⁶ (100 × 10,000), so the router's split ratio doesn't depend on the pools' state. A retail fill on our pool therefore pins the rival's price right after the trade, and the order size follows from the rival's price before it. The model also covers:\n- the rival's 30 bps arbitrage, which pulls its price back to within 30 bps of the fair price each step;\n- rival retail orders we never see, as a random jump in its price;\n- steps where we don't trade, which cut the fair price down to our own no-arb band;\n- the first trade at each timestamp, treated as a mix of \"arbitrage\" and \"retail\" rather than assumed to be arbitrage;\n- later trades at the same timestamp, treated as retail.\n\nFees come from the unchanged `oracle_table.json`, driven by the posterior mean price.\n\n**Results** (replica, 10,000 steps per seed). I tried one setting, the rival jump rate, on seeds 20600–20727 (0.4 vs 0.8 differed by +0.01, so it makes no real difference), froze it at 0.8, then evaluated on 10000–10255:\n\n| Arm | Score | Retail edge | Arb edge | Retail vol Y | Arb vol Y |\n|---|---|---|---|---|---|\n| This model | 528.65 | 564.19 | −35.54 | 75.2k | 24.9k |\n| Oracle table, true price | 535.22 | 566.71 | −31.50 | 76.3k | 24.0k |\n| Fixed 35 bps | 349.25 | 564.73 | −215.48 | 70.6k | 35.4k |\n\n- **Gap to the oracle:** −6.57 paired (SE 0.08, 95% CI −6.73 to −6.40). Of that, −2.53 is retail edge and −4.04 is arb edge.\n- **Gain over fixed 35 bps:** +179.4 (CI 173.4 to 185.4).\n- **Price error:** about 14.9 bps RMS. Arbitrage trades are recognised 84% of the time, with 6% of retail trades wrongly flagged as arbitrage.\n\n**Ablation** (seeds 10000–10063, no retuning): turning off the router likelihood, so the rival-price dimension learns nothing, costs 0.39 (CI 0.21 to 0.57) and adds 0.60 bps of error. Most of what the filter recovers comes from the no-trade-step band and the arbitrage/retail mix, not from the rival model. The brief values knowing the rival's position exactly at +0.9.\n\n**Leakage test:** I hid or corrupted everything the strategy shouldn't see (start parameters, the arbitrage label, the true price, the rival's price) on two seeds. Score and fees came out bit-identical.\n\n**Port:** it can't go to Solidity as built. Each step does work far beyond the 245,000-gas limit. If anything is ported, the cheaper option is the single-dimension price filter, since the rival dimension is worth only about +0.4.\n\n**Other limitations:**\n- The fee table was not retrained for this filter's noise.\n- Volatility is fixed rather than estimated.\n- The retail-arrival prior was not fitted.\n- When an update fails numerically, the filter falls back to its previous state, about 3.4 times per seed.\n\n`git status` shows untracked dotfiles at the repository root (`.bashrc`, `.gitmodules`, `.claude/` and others). I didn't create them and left them alone.\n\nThe result file is 54,277 bytes, within the 64,000 limit. It includes the full source, the commands, the per-seed results and the dead ends with numbers. Both files parse as JSON. Each sources quote except the one taken from `filter-brief.md` was checked against the file I downloaded.\n\nFiles are in `artifacts/goal/batches/e01-restarts/items/rival-model/`:\n- result.json\n- sources.json","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":1495139,"cost":{"source":"runtime","usdMicros":1737421},"inputTokens":51,"model":"claude-opus-5-5","outputTokens":44354,"runtime":"claude","turns":38,"wallClockMs":1291904}},{"artifacts":[{"bytes":62421,"hash":"7fffe376c70c9136df368939ee3c748a75a5e94102351704d37ffc4638e34cb8","mediaType":"application/json","name":"result","path":"artifacts/goal/batches/e01-restarts/items/finite-state/result.json"},{"bytes":983,"hash":"c4d778a7f2e03d0d56f57ede66e65e82742853be54123572d4e1c7a884ac7c46","mediaType":"application/json","name":"sources","path":"artifacts/goal/batches/e01-restarts/items/finite-state/sources.json"}],"attempt":1,"bundleHash":null,"device":"b94d8a9fa9123fb1","findings":[],"hash":"63d49f4d79fb4b110fcaafa946d7165ea1978591a11d538226cf4ee8ecc795c6","nodeId":"d180a6d8-a157-4e53-8598-e91af2e648a8","outcome":"completed","summary":"Created `result.json` (62,421 bytes) and `sources.json`.\n\nOn 256 holdout replica seeds: mean edge **527.26**, paired oracle deficit **7.953 ± 0.229** (95% CI). Embedded-code replay, leakage checks, and source verification passed.\n\nThis is a completed structural screen, not real-engine qualification.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":564608,"inputTokens":69787,"model":null,"outputTokens":13221,"runtime":"codex","turns":7,"wallClockMs":388530}},{"artifacts":[{"bytes":63897,"hash":"c9ae7b172120422a551e588f2fff07cb4af4ffedef3cc7c2828336e0361e2579","mediaType":"application/json","name":"result","path":"artifacts/goal/batches/e01-restarts/items/analytic/result.json"},{"bytes":1569,"hash":"a5946dd7e939883d84afc4a7bd6cdb7817d57ccdbf28d9fda76e619de5d100ef","mediaType":"application/json","name":"sources","path":"artifacts/goal/batches/e01-restarts/items/analytic/sources.json"}],"attempt":1,"bundleHash":null,"device":"b6e7758e5e0349d3","findings":[],"hash":"6813b814142f5ea24373342f76f6d0310f138fe5a02232cbcd0cbf22253ac16b","nodeId":"150653fb-d15d-4418-a46a-e0803a635bc6","outcome":"completed","summary":"Created both requested JSON files. Result: 63,897 bytes; validation and replay checks passed.\n\nOn 256 held-out replica seeds: mean **524.36**, oracle deficit **10.86** (95% CI **10.50–11.21**), price RMS **17.02 bps**.\n\nIncludes runnable source, per-seed results, ablation, and verified sources. No real-engine qualification claimed.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":1632384,"inputTokens":43021,"model":null,"outputTokens":24985,"runtime":"codex","turns":10,"wallClockMs":1003883}},{"artifacts":[{"bytes":63864,"hash":"56975b5d9ec026a7575fa2c6808dbbde93d0288a47a390c2500d5fa707f64f6a","mediaType":"application/json","name":"result","path":"artifacts/goal/batches/e02-policy-net/items/rep-a/result.json"},{"bytes":1239,"hash":"f932c3374a3eced1cb3aa529b6d535ebb81dc3c8917117056adbbeddf156c87c","mediaType":"application/json","name":"sources","path":"artifacts/goal/batches/e02-policy-net/items/rep-a/sources.json"}],"attempt":1,"bundleHash":null,"device":"cb213fe03251a512","findings":[],"hash":"6873a5b789c32a1a9d4148ac268bf436df6f3214d312ad506ace79a48aa70001","nodeId":"cd496e13-3def-40f7-a77a-2699de1ad595","outcome":"completed","summary":"Created and validated [result.json](artifacts/goal/batches/e02-policy-net/items/rep-a/result.json) (63,864 bytes) and [sources.json](artifacts/goal/batches/e02-policy-net/items/rep-a/sources.json).\n\nPaired replica gain over frozen mixture/table: **+1.793**, 95% CI **[1.669, 1.917]**. Remaining oracle gap: **4.495**—short of the intended advance. No engine or leaderboard claim.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":9675136,"cost":{"source":"estimated","usdMicros":2861547},"inputTokens":85915,"model":"gpt-6.1-sol","outputTokens":75469,"runtime":"codex","turns":27,"wallClockMs":3352794}},{"artifacts":[{"bytes":62521,"hash":"234d6b717829f692214883f0e7b89e1f9d03967c3d423c372ca3cfe46e5047bb","mediaType":"application/json","name":"result","path":"artifacts/goal/batches/e02-audit-recheck/items/calibration/result.json"},{"bytes":3691,"hash":"edb4b83cd6190eab8d4aed36450a62438003350efd93db6f0a2adf82f5685259","mediaType":"application/json","name":"sources","path":"artifacts/goal/batches/e02-audit-recheck/items/calibration/sources.json"}],"attempt":1,"bundleHash":null,"device":"cd2c3a8d55620566","findings":[],"hash":"6cc10eb40469c21d42117154b5c25ae38d261f180693bf92f5a74d2274a90dc6","nodeId":"4bc87f28-7760-4aef-8c84-c62d50ff74c2","outcome":"completed","summary":"Created `result.json` and `sources.json`.\n\n- CH mean: **525.5258821780834**, exact match.\n- RS2: all 16 seeds match the unmodified engine.\n- Gas bounds and failure probes passed.\n- Fixed the aggregation helper; documented parity and offline-reproduction limits.\n\nResult: **62,521 bytes**. Local validation passed.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":2000768,"inputTokens":157516,"model":null,"outputTokens":20290,"runtime":"codex","turns":8,"wallClockMs":647510}},{"artifacts":[{"bytes":58211,"hash":"2ec906fcacfa77a4e55d7b4de0bfd95e0dd8828b383802f018fd667665806d81","mediaType":"application/json","name":"result","path":"artifacts/goal/batches/e02-native-bridge/items/mixture-b/result.json"},{"bytes":2550,"hash":"3c7ddd341f95e95c875fc467ba39bea91e740ff54602a6ad780e8a802d85244d","mediaType":"application/json","name":"sources","path":"artifacts/goal/batches/e02-native-bridge/items/mixture-b/sources.json"}],"attempt":1,"bundleHash":null,"device":"98b4506bef931d13","findings":[],"hash":"717c46b93fcff0a7ee5f1e87deb73456b9755f563ba1e95bc1481d03676976a8","nodeId":"7b987e1a-d816-4eb3-a271-b35bc95003f8","outcome":"completed","summary":"I built the Solidity port of the frozen mixture-B filter, but it fails both gas and parity. Following the brief, I've written up the source and failure evidence and did not run or rank the native screen. Both output files are written and parse: `result.json` (58 KB, under the 64 KB limit) and `sources.json` (12 quotes, each checked live at its URL).\n\n**Inputs**\n- All three artifacts' content hashes match their URL ids.\n- mf.py matches the hash embedded in the model artifact.\n- The RS2 `code` hashes to the required `f24b6ca1…eaf2`. I verified it but never ran it, because the candidate failed before any comparison.\n- `setup.sh` was harmless (Python env, a wheel whose hash the script checks, solc 0.8.24). I read it before running it.\n\n**Port**\n- The contract compiles with the engine's own compiler settings and passes its validator.\n- State is packed into the contract's 32 storage slots (one 256-bit slot per mixture component).\n- All constants, the 35-number table, old-fee timing, latent labels, GBM timing and the callback observation boundary are unchanged.\n- The exact Python reference is `mfq.py`, which has its own hash and is included in full. It differs from mf.py by:\n  - an erfc approximation accurate to about 1.2e-7 in place of `math.erf`;\n  - long silent gaps: one exact step, then the rest collapsed into a single step. mf.py does 400 exact steps; I cut this from 4 to 1 to save gas.\n\n**Parity, 16 seeds (33000–33015), 10,000 steps, EVM vs Python on every callback**\n- **Gas:** 42,028 of 155,544 afterSwap calls (27%) ran out of gas. The highest successful call used 247,887 gas, above the 245,000 limit. afterInitialize used 119,087.\n- **Cascade:** a failed call leaves the stored timestamp stale, so later calls see longer gaps and fail too.\n- **Divergence:** in 15 of 16 seeds the first fee mismatch (>1e-6) is at the first out-of-gas timestamp; in seed 33003 it comes two steps later. So I saw no purely numerical divergence before gas failure.\n- **Reference vs original:** `mfq.py` is itself outside 1e-6 of the original mf.py on 8 seeds. Max fee difference is 1–3e-3, the price estimate differs by 1.2–1.9 bps RMS, and the arb flag flips 36–58 times per seed. The first differences show up at gap callbacks, which points to the gap shortcut more than the erfc approximation.\n\n**Gaps you should know about**\n- **Harness bundle not decoded:** this machine's guard hook blocked decoding the harness evidence's base64 bundle, and I did not work around it. None of its claims were used. I re-checked the engine facts (gas limit, silent fee-keeping on errors, selectors, compiler settings) directly in the amm-challenge checkout and wrote my own harness.\n- **Native screen not run:** on any callback error the engine silently keeps the old fees, so its score would belong to a broken policy. The 128-seed edges, deltas, mean/SE/CI, retail/arb split and native gas counters are all `null`. The config generator is given in canonical form.\n- **Not attempted:** the 256-seed certification. The fresh seeds 5148000–5151999 were not touched.\n\nThe result lists options to get under 245k without changing the model: lookup tables for the normal CDF/density with matching Python, sharing exponentials between them, and cheaper silent-gap and merge steps. Reducing the number of mixture components would change the model, so it needs your approval.\n\nFiles are in `artifacts/goal/batches/e02-native-bridge/items/mixture-b/`:\n- result.json\n- sources.json","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":3393188,"cost":{"source":"runtime","usdMicros":3038319},"inputTokens":77,"model":"claude-opus-5-5","outputTokens":67037,"runtime":"claude","turns":55,"wallClockMs":710316}},{"artifacts":[{"bytes":50805,"hash":"b639ece0e16a152907572d41ded68e1b0520572136f7ebe0c626e70bd8b6643e","mediaType":"application/json","name":"result","path":"artifacts/goal/batches/e01-filter-mixture/items/rep-a/result.json"},{"bytes":728,"hash":"c018b90c80fe07bd0fed7edd43c52663b4dafe7e761c3e2c3856d554c3abcc35","mediaType":"application/json","name":"sources","path":"artifacts/goal/batches/e01-filter-mixture/items/rep-a/sources.json"}],"attempt":1,"bundleHash":null,"device":"b024a55c72116b43","findings":[],"hash":"90d7f04561747a56088903f1841820d05e1402b80c0dcdd31ff551a1eb1aaa0e","nodeId":"9621cf92-e2b4-4477-ba88-79f867b0c4c8","outcome":"completed","summary":"I built and evaluated the small-mixture filter, but it misses the target. On the 256 frozen seeds (10000–10255), the filtered arm scores **7.68 below the true-price arm** (SE 0.26, 95% CI 7.17–8.19), against a target of 2 or less. Everything ran on the pure-Python replica only. Nothing touched the real engine, there is no Solidity port, and seeds 5148000 and up were never used.\n\n| Arm (256 seeds) | Edge | Callback RMS error | 95% coverage |\n|---|---|---|---|\n| Filtered (estimated price, inferred arb flag) | 527.54 | 16.8 bps | 0.95 |\n| True price, true arb flag | 535.22 | 15.9 bps* | 0.95 |\n| True price, inferred arb flag | 534.97 | 15.9 bps* | 0.95 |\n\n\\*In the two true-price arms, the RMS and coverage are for the filter running alongside, not driving the fees.\n\n- **Where the gap comes from:** almost all of it is error in the price estimate. Swapping in the inferred arb flag costs only 0.25 (SE 0.013). The weak point is deciding, at the first trade of a step, whether it was an arb or a retail fill. That is only about 90% accurate.\n- **Router-fill ablation (seeds 10000–10063, not retuned):** turning the router-fill likelihood off *improves* the result. The gap falls from 7.74 to 6.38, filtered edge rises by 1.36 (SE 0.61), and RMS falls from 19.5 to 15.1 bps. As built, that term does more harm than good.\n- **Tuning:** I set the frozen parameters on training seeds 0–3 and checked one closed-loop run on 0–31. The first router setting (30 bps width, 8 bps bias) gave a gap of 38.1 on 0–31; the frozen setting (80 bps, no bias) gave 8.2. The other settings I rejected, with their numbers, are in the result's dead-ends list.\n- **Tests:** they check that hidden values can't reach the filter, the sign of the arb-implied price on both sides, repeated callbacks in one step, t=0, and recomputing the gap from the saved per-seed records. All pass.\n- **Limitations:**\n  - The retail-size and router models are crude.\n  - The sigma estimate has a selection bias that I note but don't correct.\n  - Merge error is logged but not reported per seed.\n  - The gas figure for a port (roughly 60–150k) is a guess, not a measurement.\n\nThe result file (50.8 KB, under the 64,000-byte limit) holds:\n- the full source and frozen parameters as strings\n- input URLs and hashes\n- per-seed rows covering edges, retail/arb split, squared-error sums, counts and coverage\n- aggregates, the ablation, test results and runtimes\n\nThe sources file quotes the brief, the oracle table and the replica. Both files parse as valid JSON. `git status` also shows some untracked dotfiles at the repository root (`.bashrc`, `.claude/`, `.gitmodules` and others) that I didn't create, and I left them alone.\n\nFiles are in `artifacts/goal/batches/e01-filter-mixture/items/rep-a/`:\n- result.json\n- sources.json","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":811684,"cost":{"source":"runtime","usdMicros":998155},"inputTokens":42,"model":"claude-opus-5-5","outputTokens":22545,"runtime":"claude","turns":24,"wallClockMs":1050362}},{"artifacts":[{"bytes":61522,"hash":"dc0668530543dd28209fa9455aecad526ee3d19db53e391b9f6850f053d89f3e","mediaType":"application/json","name":"result","path":"artifacts/goal/batches/e01-filter-grid64/items/rep-b/result.json"},{"bytes":1526,"hash":"287dd458013c82b0d019a635b2666f96c55b9ea3c319d8ac39b364f124680580","mediaType":"application/json","name":"sources","path":"artifacts/goal/batches/e01-filter-grid64/items/rep-b/sources.json"}],"attempt":1,"bundleHash":null,"device":"c2a2bddd2b92d71e","findings":[],"hash":"944e30a69aee11b96c2e1a40a7cebd7fa28de761f27954267b4489a9e9ea0431","nodeId":"e68b59f6-2aca-4dc4-ba0b-20a52458af44","outcome":"completed","summary":"I built and evaluated the 64-bin price filter, but it misses the target: the filtered table scores **6.49 below the true-price table**, not within 2. Both output files are written and parse: `result.json` (61.5 KB, under the 64,000-byte limit) and `sources.json` (7 exact quotes, each checked against the downloaded files). All scoring ran on the Python replica only. The real engine was installed and passed its setup check, but I didn't score anything on it, and nothing was written in Solidity.\n\n**Results** (seeds 10000..10255, 10,000 steps each; parameters frozen after tuning on 128..192):\n\n| Arm | Edge |\n|---|---|\n| True price + true arb flag (control) | 535.22 |\n| True price + the filter's guessed arb flag | 534.72 |\n| Filter's price estimate + guessed flag | 528.73 |\n\n- **Gap (control minus filter):** 6.49, SE 0.09, 95% CI [6.31, 6.67].\n- **Where the gap comes from:**\n  - Guessing whether a trade was an arb costs only 0.49.\n  - The price estimate itself costs 6.0. Its error is 16.6 bps RMS against a mean posterior spread of 11.1 bps.\n  - The filter's 50% intervals contain the true price 70% of the time, so they are too wide. Its 90% intervals contain it 91% of the time, which is about right.\n- **Arb vs. retail is hard to tell apart:** small arb trades look like retail fills. On first trades of a step the filter got 858,905 arbs right, called 284,430 retail fills arbs, missed 51,227 arbs and got 531,473 retail fills right. Because the flag costs so little, this matters less than the price estimate.\n- **Bin count isn't the limit:** a 256-bin copy run alongside on 16 seeds differs from the 64-bin mean by 0.27 bps RMS (error 16.36 vs 16.43 bps). About 0.0013 of probability is cut off at the edges per whole run.\n- **The rival-price signal from trade sizes hurts:** switching it off on 10000..10063, without retuning, scores 0.46 better (CI [−0.63, −0.29] for on minus off). It probably assumes the wrong place for the rival pool's price; that's my guess, not tested.\n- **Volatility estimate:** it reads about 6.5% high on average, because it is measured from selected arb trades and some are misclassified.\n\n**Tests:** all pass.\n- The filter only sees the trade side, amounts, timestamp, reserves and its own fees. Replaying recorded trades through a fresh filter gives identical fees.\n- After an arb trade, the probability mass sits at the correct price with the correct sign.\n- A second trade at the same timestamp adds no extra diffusion step.\n- The per-seed gaps add up to the overall gap.\n\nOne unit test needed changing: a 40 bps arb at t=0 was read as retail. That 4-sigma move is genuinely unlikely, so I moved the test to t=30 rather than calling it a bug.\n\n**Not done:** no engine runs, no Solidity port and no 4,000-seed confirmation; none of these were part of this item. `result.json` gives an unmeasured gas estimate: a direct port won't fit under 245k gas without approximations. It also holds the full source, parameters, per-seed rows, the tuning numbers, dead ends and limitations. I never used seeds 5148000..5151999.\n\nThe repository root shows untracked dotfiles (`.bashrc`, `.gitmodules`, `.claude/`, …) that I did not create, and I left them alone. Everything else I made is under `test/scratch/`.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":1438204,"cost":{"source":"runtime","usdMicros":1851586},"inputTokens":50,"model":"claude-opus-5-5","outputTokens":49087,"runtime":"claude","turns":26,"wallClockMs":924154}},{"artifacts":[{"bytes":60906,"hash":"3455c28f0465272808f2b225620f063a7412653322b1843f583e279a3ec58be5","mediaType":"application/json","name":"result","path":"artifacts/goal/batches/e02-filter-censoring/items/rep-b/result.json"},{"bytes":673,"hash":"83baa5cd4fcc5df215d7048a45188066a0571eafcabe6f34d3c47f899d707f0d","mediaType":"application/json","name":"sources","path":"artifacts/goal/batches/e02-filter-censoring/items/rep-b/sources.json"}],"attempt":1,"bundleHash":null,"device":"ccf14607d159904b","findings":[],"hash":"c166f342392b2d3ab9bf1c5a5137df7504b3e36c9a0e2f2e2fe18357da92b232","nodeId":"1fa34296-a85e-4d9e-b30e-bb69036c22e3","outcome":"completed","summary":"Created and validated [result.json](artifacts/goal/batches/e02-filter-censoring/items/rep-b/result.json) and [sources.json](artifacts/goal/batches/e02-filter-censoring/items/rep-b/sources.json).\n\nCompleted 255/256 repaired evaluations and all 64 ablations; timeout left one seed unfinished. Oracle deficit: **5.797**—the ≤2 gate was not met. Runnable source is included; result size is 60,906 bytes.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":10119168,"inputTokens":191365,"model":null,"outputTokens":58698,"runtime":"codex","turns":46,"wallClockMs":3646190}},{"artifacts":[{"bytes":25040,"hash":"07d36ee7c9ecc0de4aef6cb95291fe89a73ebe57ce00e0959b6c604458b5da6f","mediaType":"application/json","name":"decisions","path":"artifacts/goal/episodes/002/decisions.json"}],"attempt":1,"bundleHash":null,"device":"f42f27bce9bb7fc5","findings":[],"hash":"c69aa882b155f5623ac3da75b3371617aa478f945845579f8aa2bc5247dd98e6","nodeId":"eb763ad8-38b4-4c4b-b6fc-50c91ca6fde9","outcome":"completed","summary":"Created and validated [decisions.json](/root/.identitymd/work/fca9a24c-9adf-4ff6-9ed4-f5e8ba443d6a/eb763ad8-38b4-4c4b-b6fc-50c91ca6fde9/artifacts/goal/episodes/002/decisions.json): five batches, eight items, 41 items remaining. Left untracked for upload.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":2265472,"cost":{"source":"estimated","usdMicros":4432682},"inputTokens":131056,"model":"gpt-6-astra","outputTokens":17133,"runtime":"codex","turns":5,"wallClockMs":622259}},{"artifacts":[{"bytes":25529,"hash":"b701a82734160c26f96cfc88b23bcf1fa780ed582cff7f9805eccbe728e01ddb","mediaType":"application/json","name":"decisions","path":"artifacts/goal/episodes/001/decisions.json"}],"attempt":1,"bundleHash":null,"device":"12fbd676b2122512","findings":[],"hash":"c6c3fc054506be0719a1ffb7b6b3be4ea9d4e4a4b6e1394135d478ab8d96ce34","nodeId":"66a3fe55-cff4-4a0a-a60f-5a84498c825d","outcome":"completed","summary":"Created and validated [decisions.json](/home/imd-worker/.identitymd/work/fca9a24c-9adf-4ff6-9ed4-f5e8ba443d6a/66a3fe55-cff4-4a0a-a60f-5a84498c825d/artifacts/goal/episodes/001/decisions.json): five batches, 11 worker items, 49 items reserved. Left untracked for upload.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":514944,"cost":{"source":"estimated","usdMicros":1633404},"inputTokens":55536,"model":"gpt-6-astra","outputTokens":11262,"runtime":"codex","turns":5,"wallClockMs":417025}},{"artifacts":[{"bytes":63572,"hash":"6edfe9e328534a484b1f1d7fa8d63a192b8570e1ca542b21f40335e3acc96006","mediaType":"application/json","name":"result","path":"artifacts/goal/batches/e01-restarts/items/recurrent/result.json"},{"bytes":1440,"hash":"36d84467105934bf67deb6edded41f34fa475531b76d2e20916cac75f5271a2f","mediaType":"application/json","name":"sources","path":"artifacts/goal/batches/e01-restarts/items/recurrent/sources.json"}],"attempt":1,"bundleHash":null,"device":"b7f1fc143076cedc","findings":[],"hash":"cec0de5844802680da07e5673c7865eaa35ae24df99e504090f7d61a67dfaa90","nodeId":"bed5abdb-b783-453c-a99e-6b1ed04f1b95","outcome":"completed","summary":"Created [result.json](artifacts/goal/batches/e01-restarts/items/recurrent/result.json) (63,572 bytes) and [sources.json](artifacts/goal/batches/e01-restarts/items/recurrent/sources.json).\n\nAcross 256 replica seeds: score **481.04**, oracle deficit **54.17** (95% CI **51.81–56.54**). Recurrent history added **7.72** points in the ablation.\n\nOffline replay, leakage checks, JSON validation, and source verification passed. Not real-engine qualified.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":1832832,"cost":{"source":"estimated","usdMicros":4281252},"inputTokens":62352,"model":"gpt-6-astra","outputTokens":36498,"runtime":"codex","turns":9,"wallClockMs":1352235}},{"artifacts":[{"bytes":63943,"hash":"f7c8a6e9e973d7e4ab746e2033b74ab867134477e4b5bce5fd9267c1f6b4dfa1","mediaType":"application/json","name":"result","path":"artifacts/goal/batches/e01-filter-grid64/items/rep-a/result.json"},{"bytes":754,"hash":"acec6255f75b6878dcf65342266db31a916d5ae4b99f2683a5002fc334014b52","mediaType":"application/json","name":"sources","path":"artifacts/goal/batches/e01-filter-grid64/items/rep-a/sources.json"}],"attempt":1,"bundleHash":null,"device":"8632d45e6a022317","findings":[],"hash":"d1efb54881db5ea5ac58bb93b1de8f500fb2f987422b2acd06ae8339aa013c66","nodeId":"dc406bea-9697-4488-9210-c4c3c55e8cb8","outcome":"completed","summary":"Completed the replica evaluation and wrote [result.json](/home/imd/.identitymd/work/fca9a24c-9adf-4ff6-9ed4-f5e8ba443d6a/dc406bea-9697-4488-9210-c4c3c55e8cb8/artifacts/goal/batches/e01-filter-grid64/items/rep-a/result.json) and [sources.json](/home/imd/.identitymd/work/fca9a24c-9adf-4ff6-9ed4-f5e8ba443d6a/dc406bea-9697-4488-9210-c4c3c55e8cb8/artifacts/goal/batches/e01-filter-grid64/items/rep-a/sources.json).\n\nThe variant **missed the target**: across 256 held-out seeds, the true-price control exceeded the filter by 121.97 edge points on average, with a 95% confidence interval of 117.05–126.88. Callback price RMS error was 42.34 bps.\n\nThe requested 64-seed router ablation and local checks are included. No real-engine or Solidity validation was performed; the replica result does not support a leaderboard claim.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":1712640,"inputTokens":92319,"model":"gpt-6.1-terra","outputTokens":22643,"runtime":"codex","turns":8,"wallClockMs":566407}},{"artifacts":[{"bytes":63754,"hash":"94a6fa7c527efefd9bb9f09aed5fae4e15542e004b0850f363c29370700ad344","mediaType":"application/json","name":"result","path":"artifacts/goal/batches/e02-policy-table/items/rep-b/result.json"},{"bytes":1143,"hash":"8d5c899e7e91898a3a8ccc7e822761cef13081379ad12793ea35986a9c094657","mediaType":"application/json","name":"sources","path":"artifacts/goal/batches/e02-policy-table/items/rep-b/sources.json"}],"attempt":1,"bundleHash":null,"device":"cbc3d81ff238201c","findings":[],"hash":"d2d06f42615999ce094980a133c399afa878527275c58f2ffea85e4d76266dfb","nodeId":"c95f5c05-13d4-447a-809b-ee22f2cb8bf9","outcome":"completed","summary":"Created and checked [result.json](artifacts/goal/batches/e02-policy-table/items/rep-b/result.json) and [sources.json](artifacts/goal/batches/e02-policy-table/items/rep-b/sources.json).\n\nReplica gain: **+2.135** versus frozen mixture-B/table (95% CI **2.015–2.255**). Oracle gap remains **4.152**; the deficit is not closed. No engine or leaderboard claim.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":7257856,"cost":{"source":"estimated","usdMicros":12135466},"inputTokens":71831,"model":"gpt-6-astra","outputTokens":83186,"runtime":"codex","turns":36,"wallClockMs":3566756}},{"artifacts":[{"bytes":49621,"hash":"827590bb235808bd3789c89ad42e5038d2733d133c744455327a3e4275089b26","mediaType":"application/json","name":"result","path":"artifacts/goal/batches/e01-filter-mixture/items/rep-b/result.json"},{"bytes":629,"hash":"a3421528e86a6b3a64aa0e551198159b00771c7d22bb7ab09ee5338eac0e467a","mediaType":"application/json","name":"sources","path":"artifacts/goal/batches/e01-filter-mixture/items/rep-b/sources.json"}],"attempt":1,"bundleHash":null,"device":"b51bda697e321eba","findings":[],"hash":"ebfafa322163cbb9d5610eacf7730746d9ebf52832df31415fe3c5ad963aaeca","nodeId":"73ae1a33-6629-40d1-9d7d-bda06870e0c8","outcome":"completed","summary":"I built and ran the small-mixture filter, but it misses the target: the score gap to the true-price run is **6.27**, not the ≤2 the brief asked for. All scoring is on the pure-Python replica (`replica.py`), not the real engine. The engine setup ran without errors (it validated the starter contract), but I didn't run anything on it. I wrote no Solidity port and did no pairing against RS2 or 4,000-seed confirmation; the result says so.\n\n**What it is:** a filter of up to four Gaussian components over z = ln(p/spot), updated only in the swap callback. It keeps the arb and retail explanations of each new step's first trade as competing branches, and an arb branch pins the price exactly. Each silent step diffuses the estimate and cuts it back to the fee band then in force. Sigma is estimated from arb-to-arb moves, and the router-split observation enters as a weak, soft band bound. When there are too many components it merges the closest pair and records the cost of doing so. The fees come from the frozen `oracle_table.json`, driven by the filter's price estimate and its own arb/retail guess. The true price and the true arb flag are used only for scoring and in the two comparison runs. The replica itself is unchanged; each policy is a wrapper around it.\n\n**Results on 256 held-out seeds (10000..10255, 10,000 steps each):**\n\n| | Edge | Price error (RMS, bps) |\n|---|---|---|\n| True price, true arb flag | 535.22 | 14.17 |\n| True price, inferred arb flag | 534.89 | 14.33 |\n| Filtered price, inferred arb flag | 528.94 | 15.21 |\n\n- **Gap, true-price run minus filtered run:** 6.27 per seed (SE 0.08, 95% CI 6.11 to 6.43).\n- **Where the gap comes from:** guessing the arb flag costs only 0.32, so about 5.95 of the gap is error in the price estimate.\n- **Calibration:** the filter's 95% interval covered the true price 94.7% of the time, and it got the arb flag wrong on 10.2% of trades.\n- **Router ablation (seeds 10000..10063):** turning the router observation off changed the score by −0.04 ± 0.07, so it adds nothing measurable.\n- **Training:** a 64-seed sweep on seeds 128..191 found nothing better than the defaults, which I then froze. Two components instead of four scored 8.63 against 6.55 at four. The 5148000..5151999 seeds were never touched.\n\n**Likely cause (not tested):** the arb/retail classifier misses real arbs. In a unit test, an arb just outside the fee band got only a 15–19% arb probability, because my guessed retail order-size density outweighs the narrow density of the exact arb price. The four unit tests pass: inputs are hidden from the filter, arb trades pin the price with the right sign in both directions, a repeated callback at the same timestamp is treated as retail, and the estimate stays inside the band over silent steps.\n\nThe result file has the full source and parameters as strings, input URLs with hashes, per-seed rows, test output, runtimes, an unverified gas estimate, dead ends and limitations. It is 49.6 KB, under the 64,000-byte cap. Both files parse as JSON, and the three source quotes match the fetched copies of the pages.\n\nFiles are in `artifacts/goal/batches/e01-filter-mixture/items/rep-b/`:\n- result.json\n- sources.json","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":597796,"cost":{"source":"runtime","usdMicros":961576},"inputTokens":30,"model":"claude-opus-5-5","outputTokens":22229,"runtime":"claude","turns":20,"wallClockMs":1028056}},{"artifacts":[{"bytes":51143,"hash":"4bd6b6ec968acf4ca766632d9cc954e8d09705a09f00e02d6fde5234233dd9b8","mediaType":"application/json","name":"result","path":"artifacts/goal/batches/e02-policy-net/items/rep-b/result.json"},{"bytes":758,"hash":"5b6d0765e757f7d311778b0b37122978f403b8275a6845bfe42035ee5281cf56","mediaType":"application/json","name":"sources","path":"artifacts/goal/batches/e02-policy-net/items/rep-b/sources.json"}],"attempt":1,"bundleHash":null,"device":"bbfb1c6b4649ad92","findings":[],"hash":"f1924eecd5dbedf2baf365e2699a99236ddf9c8cc15aaeb7b6f18f8826a5d34a","nodeId":"475b412d-1e20-4967-a801-5872bd2cdd73","outcome":"completed","summary":"I built and evaluated the small neural policy, but it does not close the deficit: on the validation seeds it is no better than the existing mixture-B/table policy. Everything here was run on the Python replica only. There is no Solidity port, no real-engine run and no comparison against RS2, so none of this can select an entry.\n\n**What I built.** The net has 3 inputs, 8 tanh hidden units and 2 outputs (ask and bid offsets in 30 bps units), 58 weights in all. Fees are clipped to [0, 0.1]. It sits on the frozen mixture-B estimator unchanged, and its own fees feed that estimator's stored fee history. I checked the estimator's embedded source hashes and they match.\n- **Inputs:** own displacement (which is exactly minus the posterior mean, so those count as one input), posterior sd, and inferred arb probability. No true price, rival state or hidden parameters reach it.\n- **Hidden-input test:** with the true price and rival spot withheld from the policy, edges were identical on 3 seeds.\n\n**Distillation.** The net matches the oracle table with an RMS error of 2.39 bps (worst point 0.36 × 30 bps).\n\n**Training.** Only one CPU was effectively available: 16 workers ran no faster than one. That forced a small search budget: 2,000-step episodes, 16 seeds per generation on common random numbers, population 12, all on seeds 31000–31255.\n- **First run (step size 0.15, 17 generations):** the search mean drifted below the warm start, so I discarded it.\n- **Second run (step size 0.04, 15 generations):** it stayed within about ±0.25 of the warm start, so training gave no measurable gain. I froze it before running any validation seed.\n\n**Validation (seeds 32000–32255, 10,000 steps, all three arms on the same streams):**\n\n| Comparison | Mean | SE | 95% CI |\n|---|---|---|---|\n| Net minus mixture-B/table | +0.102 | 0.056 | −0.007 to 0.212 |\n| True-price oracle minus net | 6.185 | 0.083 | 6.023 to 6.347 |\n| True-price oracle minus mixture-B/table | 6.288 | 0.080 | 6.130 to 6.445 |\n\n- **Edges:** net 517.90 (retail 547.14, arb −29.24); mixture-B/table 517.80; oracle 524.09.\n- **Pooled price error:** 14.59 bps for the net and 15.24 bps for mixture-B/table. The filter is the same in both, so the difference comes from the different fee paths.\n\n**Ablation on the first 64 validation seeds (no refit).**\n- Fixing the sd input costs −1.34 (SE 0.13).\n- Fixing the arb-probability input costs −0.43 (SE 0.11).\n- Fixing both costs −1.69.\n\nThese do not show that uncertainty-aware actions help. The full net is barely better than the table, which ignores sd. The more likely reading is that the distilled net picked up some sd dependence by accident, so pinning sd pulls it away from the table it copies.\n\n**Conclusion.** The remaining 6.2-point deficit comes from error in the price estimate, not from the shape of the policy, which matches what the brief says. A larger CMA-ES budget was not tested here.\n\n`result.json` is 51 KB and also holds the full source with hashes, weights, optimizer logs, per-seed edges with retail/arb splits, error sums and counts, dependency versions and dead ends. Both files parse, and I checked each quote in `sources.json` against my downloaded copy of its page.\n\nFiles are in `artifacts/goal/batches/e02-policy-net/items/rep-b/`:\n- result.json\n- sources.json","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":1082865,"cost":{"source":"runtime","usdMicros":1027286},"inputTokens":49,"model":"claude-opus-5-5","outputTokens":20692,"runtime":"claude","turns":31,"wallClockMs":2321197}},{"artifacts":[{"bytes":59214,"hash":"2dc6507ab085f3670c7f431b90397d4e544f3dad75ec3ca0a7e7a27790ed4e91","mediaType":"application/json","name":"result","path":"artifacts/goal/batches/e02-filter-censoring/items/rep-a/result.json"},{"bytes":868,"hash":"1985840a8a698febc64b94286742230e4bcf757d99506c422a0f0195bffb60ef","mediaType":"application/json","name":"sources","path":"artifacts/goal/batches/e02-filter-censoring/items/rep-a/sources.json"}],"attempt":1,"bundleHash":null,"device":"cc309a7afc70b0b1","findings":[],"hash":"f1f823459b88f5b1895ed932733971d297ac7125208a9b9331d757444821c6fa","nodeId":"4bbda7a9-1faf-48c2-9290-98fe9039c4eb","outcome":"completed","summary":"Created [result.json](artifacts/goal/batches/e02-filter-censoring/items/rep-a/result.json) and [sources.json](artifacts/goal/batches/e02-filter-censoring/items/rep-a/sources.json).\n\nCompleted 256 paired replica seeds and 64 ablations. Repair gain: **+0.427** versus original. Oracle deficit: **6.064**, failing the ≤2 gate.\n\nValidation and offline smoke tests passed. 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