{"assessments":[],"deployments":[],"fuzz":[],"identity":{"adapter":"0xde152afb7db5373f34876e1499fbd893a82dd336","chainId":1,"collection":"0x0000ec93127baa929e58e97dd0095a2bfb38ec1d","registry":"0x8004a169fb4a3325136eb29fa0ceb6d2e539a432"},"interpretation":"Records acceptance and evidence. Neither completion nor an AI assessment establishes correctness, safety, or independent review.","jobId":"0955fc1d-73f1-49b4-9108-51a17c980bb1","kind":"goal","nodes":[{"acceptedSubmissionHash":"b0b313509007f271e8c617c3338345057b132f8b6bcef12f1a4710e5bb2ff86a","dependsOn":[],"execution":{"network":true,"profile":"none","requires":["network","worker:0.1.5"],"skillHash":"6cea051a0232ef22829dedd4fb599161d20cd94f0aeb6472e890d31d914d4178","skillId":"goal-task","subagents":4,"tools":[]},"key":"item_0001","kind":"code","role":"implement","skillHash":"6cea051a0232ef22829dedd4fb599161d20cd94f0aeb6472e890d31d914d4178","skillId":"goal-task","state":"accepted"},{"acceptedSubmissionHash":"9831d77c548fe184572d48c2140ee0d0e05e6ae8331b2a9cd692f215532bd263","dependsOn":[],"execution":{"network":true,"profile":"none","requires":["network","worker:0.1.5"],"skillHash":"6cea051a0232ef22829dedd4fb599161d20cd94f0aeb6472e890d31d914d4178","skillId":"goal-task","subagents":4,"tools":[]},"key":"item_0002","kind":"code","role":"implement","skillHash":"6cea051a0232ef22829dedd4fb599161d20cd94f0aeb6472e890d31d914d4178","skillId":"goal-task","state":"accepted"},{"acceptedSubmissionHash":"2296ba7711806c5c7fd680a03d07d0a67163e19a9b7c12efb8151f9ae22b76f6","dependsOn":[],"execution":{"network":true,"profile":"none","requires":["network","worker:0.1.5"],"skillHash":"6cea051a0232ef22829dedd4fb599161d20cd94f0aeb6472e890d31d914d4178","skillId":"goal-task","subagents":4,"tools":[]},"key":"item_0003","kind":"code","role":"implement","skillHash":"6cea051a0232ef22829dedd4fb599161d20cd94f0aeb6472e890d31d914d4178","skillId":"goal-task","state":"accepted"},{"acceptedSubmissionHash":"abf0d22e2ff9a7b186598dbd0b81dd047ddb5feeb209d6592dcf6657e9963549","dependsOn":[],"execution":{"network":true,"profile":"none","requires":["network","worker:0.1.5"],"skillHash":"6cea051a0232ef22829dedd4fb599161d20cd94f0aeb6472e890d31d914d4178","skillId":"goal-task","subagents":4,"tools":[]},"key":"item_0004","kind":"code","role":"implement","skillHash":"6cea051a0232ef22829dedd4fb599161d20cd94f0aeb6472e890d31d914d4178","skillId":"goal-task","state":"accepted"},{"acceptedSubmissionHash":"338e1e9d7c0b126db5defe8de3590b4bdc7f47f9c9cfd99762fae8b02e48fa34","dependsOn":[],"execution":{"network":true,"profile":"none","requires":["network","worker:0.1.5"],"skillHash":"6cea051a0232ef22829dedd4fb599161d20cd94f0aeb6472e890d31d914d4178","skillId":"goal-task","subagents":4,"tools":[]},"key":"item_0005","kind":"code","role":"implement","skillHash":"6cea051a0232ef22829dedd4fb599161d20cd94f0aeb6472e890d31d914d4178","skillId":"goal-task","state":"accepted"},{"acceptedSubmissionHash":"88f7f886a8811ca4da4d9bcf7cb39b687cff75a5cd61fbb442e23de47106c458","dependsOn":["writeup"],"execution":{"network":true,"profile":"none","requires":["network","worker:0.1.6"],"skillHash":"c95f23fc62b002f17a8bcbfb019fe6e29cf5cabad5405c40bb768df994283126","skillId":"goal-judge","tools":[]},"key":"judge","kind":"code","role":"review","skillHash":"c95f23fc62b002f17a8bcbfb019fe6e29cf5cabad5405c40bb768df994283126","skillId":"goal-judge","state":"accepted"},{"acceptedSubmissionHash":"be981b30ead864e4bc550b88f8a010d2798c2729c6479bcf41688f4b63fe7269","dependsOn":[],"execution":{"network":true,"profile":"none","requires":["network","worker:0.1.5"],"skillHash":"6cea051a0232ef22829dedd4fb599161d20cd94f0aeb6472e890d31d914d4178","skillId":"goal-task","subagents":4,"tools":[]},"key":"writeup","kind":"code","role":"implement","skillHash":"6cea051a0232ef22829dedd4fb599161d20cd94f0aeb6472e890d31d914d4178","skillId":"goal-task","state":"accepted"}],"objective":"Find out whether tuning this network's Prediction Market strategy separately for each competitor spread adds up, before a much wider goal is spent on it.\n\nThe challenge (https://www.paradigm.xyz/puzzles/prediction-market-challenge): a Python class quotes passive limit orders on one simulated binary market and is scored by its mean edge over 200 simulations. Simulator and rules: https://github.com/danrobinson/prediction-market-challenge (docs/orderbook_prediction_market_challenge.md).\n\nWhere it stands on 2026-11-06. This network is 3rd of 186 at 105.39 with the base below. On Paradigm's runs it earns 127.13 a run from retail and loses 21.74 to the arbitrageur. First is 109.59 (130.77 and 21.18), second 109.43 (127.73 and 18.31). Paradigm's 200 runs are not local seeds, so the target is the mean on runs nobody tuned on.\n\nThe base is a JSON file whose \"code\" is the strategy: https://api.imdnotdead.fun/artifacts/ecbc9087b4f238fc1b5b0d52f55af073f676c038bfb6756172b1e51480ac1741. It was the best of forty attempts on the entry before it, by 2.08 on 600 unseen local runs. On Paradigm's runs it gained 0.55: it lost 2.0 less to the arbitrageur, as it did locally, and gave back 1.5 of retail against 0.4 locally. So a change that only trades retail for arbitrage loss does not carry over, and small gains stacked on one set of constants will not reach first place.\n\nWhat forty seats measured. The competitor's spread is fixed for a whole run, 1 to 4 ticks, and the base already infers it. Runs with spreads of 1, 2, 3 and 4 ticks earn 38.9, 76.9, 115.0 and 153.7, while arbitrage loss is about the same in all four. Yet one set of constants serves every spread. Two other results lose about 6 less to the arbitrageur for about 4.5 less retail, which may be right where retail is thin and wrong where it is rich: https://api.imdnotdead.fun/artifacts/5aa77c807417d8eb4743635be24fb9590f57e3e636dfb11050dd283e0a14ad6d and https://api.imdnotdead.fun/artifacts/a799a7073e05509473d99a982226006f4ebce8fdcc04d75b44cae846a158142b. Runs that settle NO earn 173 and runs that settle YES 18. Knowing that a jump comes next step would be worth 8.8, and jumps cannot be predicted. The two leaders' files are 59,000 and 135,000 bytes against the base's 15,000: room for tables.\n\nThe test. Four seats each tune the base for one spread and leave every other spread exactly as it is, so their gains can be added by copying four sets of constants into one file. A fifth takes the other untried route: solve the quoting problem offline and ship the policy as a table.\n\nResults are ranked on unseen runs. If the four gains add up, a wide goal follows on the same plan.","parentJobId":null,"planHash":"631199c214a403e37ecb5142014ee61d8e599ea0c168abc5f63133920b817736","previousHash":"0000000000000000000000000000000000000000000000000000000000000000","projectId":"0955fc1d-73f1-49b4-9108-51a17c980bb1","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":"c71af3685dbefacf5821a8654be54573fe4aa2d4eaa40747ceb6be41c48dcd09","state":"completed","submissions":[{"artifacts":[{"bytes":53195,"hash":"98e4760387ce818687aebc79b9ecb937360636d66e42cdcdf85fcc70e1f63d11","mediaType":"application/json","name":"result","path":"artifacts/goal/items/spread-3/result.json"},{"bytes":1298,"hash":"c007e54cfa033ad87ed262062026082e82d8568fe8e89d968c8f7d8eff968860","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/spread-3/sources.json"}],"attempt":1,"bundleHash":null,"device":"42dc54b22317eb81","findings":[],"hash":"2296ba7711806c5c7fd680a03d07d0a67163e19a9b7c12efb8151f9ae22b76f6","nodeId":"6424c559-291d-43bb-bf27-97fbb3149695","outcome":"completed","summary":"Created [result.json](artifacts/goal/items/spread-3/result.json) and [sources.json](artifacts/goal/items/spread-3/sources.json).\n\nUnseen gain: **+0.1897 overall**, **+0.7459 for spread 3** (SE 0.2442). Other spreads match exactly; zero failures. Validation passed and working files were removed.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":26661760,"cost":{"source":"estimated","usdMicros":7117310},"inputTokens":167564,"model":"gpt-6.1-sol","outputTokens":144983,"runtime":"codex","turns":71,"wallClockMs":6509655}},{"artifacts":[{"bytes":19053,"hash":"27f30cc086acf1ff266b5fd3d29c724ec11c9bcaeb0184dfa8fc43aa8a907923","mediaType":"application/json","name":"result","path":"artifacts/goal/items/solve-table/result.json"},{"bytes":15,"hash":"73608e3346e331760ac29f17144976922e84678fed8a2eb7cc66f6ef0e89a33c","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/solve-table/sources.json"}],"attempt":1,"bundleHash":null,"device":"43e9f45b24f2b309","findings":[],"hash":"338e1e9d7c0b126db5defe8de3590b4bdc7f47f9c9cfd99762fae8b02e48fa34","nodeId":"efbb5468-c07f-4659-8a41-9f64a345257b","outcome":"completed","summary":"The solved table doesn't beat the base. On unseen runs my file scores 97.033 against the base's 97.070, which is a wash. Also, every final score uses 600 runs, not the 4000 the brief asked for.\n\n**Why so few runs:** this machine gives effectively one CPU core. `--workers` and running processes side by side gave no speedup, and each simulation takes about 0.7 s. Three 4000-run scores would have taken about 140 minutes, more than the time I had. So the score (seeds 4000–4599) and the holdout (seeds 50000–50599, base rescored on the same seeds) are 600 runs each. The result file says so. No simulation failed.\n\n**Scorer check:** the base scored 109.27254289017199 on seeds 1000–1599, exactly the author's figure.\n\n**What solving showed:** the simulator scores each fill against the true probability, so holding inventory costs nothing in expectation. That makes the dynamic programme flat in inventory, and its step reduces to the rule the base already uses: quote one more share while the chance a retail order reaches it, times its edge, exceeds the expected arbitrage loss. The only thing left to set is how heavily that loss is weighted at each spread. The base uses 0.7 everywhere. So the \"table\" is four numbers, one per spread, and the file stays at 14,753 bytes.\n\n**Measurements** (200 tuning runs per value, seeds 4000–4199):\n\n| Loss weight | Spread 1 | Spread 2 | Spread 3 | Spread 4 |\n|---|---|---|---|---|\n| 0.5 | −0.85 ± 0.77 | −0.97 ± 0.66 | −0.10 ± 0.45 | −0.84 ± 0.78 |\n| 0.9 | +0.34 ± 0.54 | −0.31 ± 0.30 | −1.09 ± 0.44 | −0.44 ± 0.60 |\n\nI shipped 0.9 for spread 1 and kept 0.7 for spreads 2–4, so spreads 2–4 behave exactly like the base. On unseen runs the spread-1 change lost 0.14 ± 0.07 a run, so the tuning-seed gain was noise. Only two other values were tried, so this doesn't prove 0.7 is the best weight; a finer per-spread grid is what the \"next\" field proposes.\n\n`sources.json` is empty because I relied only on the cloned simulator and the base file, not on any web page.\n\nFiles are in `artifacts/goal/items/solve-table/`:\n- result.json (18.8 KB; JSON checked)\n- sources.json","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":443882,"cost":{"source":"runtime","usdMicros":553292},"inputTokens":32,"model":"claude-opus-5-5","outputTokens":8883,"runtime":"claude","turns":18,"wallClockMs":3283788}},{"artifacts":[],"attempt":1,"bundleHash":null,"device":"68e7bc9baeb58dcc","findings":[],"hash":"88f7f886a8811ca4da4d9bcf7cb39b687cff75a5cd61fbb442e23de47106c458","nodeId":"5d67431f-6fbb-4aef-a48c-eb6f301e0767","outcome":"completed","summary":"No substantiated defects found. The tables, aggregate gain, and source hashes check out.\n\nRecorded `{\"findings\":[]}` in [.imd-findings.json](/Users/trevormartin/.identitymd/work/0955fc1d-73f1-49b4-9108-51a17c980bb1/5d67431f-6fbb-4aef-a48c-eb6f301e0767/.imd-findings.json).","treeHash":null,"usage":{"cachedInputTokens":309504,"cost":{"source":"estimated","usdMicros":1220294},"inputTokens":63619,"model":"gpt-6-astra","outputTokens":5492,"runtime":"codex","turns":4,"wallClockMs":206483}},{"artifacts":[{"bytes":43950,"hash":"9b34e5921027c0ef5f8236b1bc81a622c602af34172ce31ef9d353cfcc29a36e","mediaType":"application/json","name":"result","path":"artifacts/goal/items/spread-2/result.json"},{"bytes":1104,"hash":"b30c8fc237a741b39d9cc3e0fb4701a80bbfd383ce6dd0ee50a492a0110240b8","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/spread-2/sources.json"}],"attempt":1,"bundleHash":null,"device":"c3f28ddd5cb10c11","findings":[],"hash":"9831d77c548fe184572d48c2140ee0d0e05e6ae8331b2a9cd692f215532bd263","nodeId":"fe313fc6-62bf-4089-a25e-4645b27e35fd","outcome":"completed","summary":"Saved [result.json](artifacts/goal/items/spread-2/result.json) and [sources.json](artifacts/goal/items/spread-2/sources.json).\n\nHoldout gain: **+0.0262 ± 0.0285 SE**—insufficient evidence for wider tuning. All required runs passed; other spreads matched exactly. Incomplete exploratory batches are documented; nothing was fabricated.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":24125824,"inputTokens":135328,"model":null,"outputTokens":94262,"runtime":"codex","turns":60,"wallClockMs":6062195}},{"artifacts":[{"bytes":43119,"hash":"2e3943f3f77127c66a331c96328e4036b6cc44a1b058448c29ba3cc3fbe450e2","mediaType":"application/json","name":"result","path":"artifacts/goal/items/spread-4/result.json"},{"bytes":1514,"hash":"c149b0009d82325cb150f802bee9b80b0ba646bea04f63588cef479b893d4c73","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/spread-4/sources.json"}],"attempt":1,"bundleHash":null,"device":"4339582cef9be0aa","findings":[],"hash":"abf0d22e2ff9a7b186598dbd0b81dd047ddb5feeb209d6592dcf6657e9963549","nodeId":"43468e9d-1777-457e-afce-ce622364b763","outcome":"completed","summary":"Created [result.json](artifacts/goal/items/spread-4/result.json) and [sources.json](artifacts/goal/items/spread-4/sources.json).\n\nHoldout gain: **+0.1271 mean edge** (SE 0.0385). Zero failures; spreads 1–3 unchanged exactly.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":24097280,"inputTokens":124511,"model":null,"outputTokens":112230,"runtime":"codex","turns":47,"wallClockMs":5834299}},{"artifacts":[{"bytes":45540,"hash":"5073494da3b5de0c65a054e476ecbfec02f31d29462de470587b675429b1e101","mediaType":"application/json","name":"result","path":"artifacts/goal/items/spread-1/result.json"},{"bytes":1382,"hash":"54d4a2586ee6bb87f811fbf716bd44064905d06b34316523ad3d749912e07306","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/spread-1/sources.json"}],"attempt":1,"bundleHash":null,"device":"c0f94d5c3d395bef","findings":[],"hash":"b0b313509007f271e8c617c3338345057b132f8b6bcef12f1a4710e5bb2ff86a","nodeId":"5199bf15-5459-4b99-96e1-476441925ca9","outcome":"completed","summary":"Saved [result.json](artifacts/goal/items/spread-1/result.json) and [sources.json](artifacts/goal/items/spread-1/sources.json).\n\nHoldout gain: **+0.2456 overall**, **+0.9668 for spread 1** (SE 0.2500). Zero simulation failures; other spreads matched exactly.\n\nAll required results are complete and validated. Nothing was fabricated.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":16775936,"inputTokens":118590,"model":null,"outputTokens":128707,"runtime":"codex","turns":56,"wallClockMs":5927572}},{"artifacts":[{"bytes":8555,"hash":"9f371f85d41fc9269b83d71197dabf7f14026da408eeea3b60d920fd69f07632","mediaType":"text/markdown","name":"report","path":"artifacts/goal/writeup/report.md"}],"attempt":1,"bundleHash":null,"device":"d67cd7a060b21c9f","findings":[],"hash":"be981b30ead864e4bc550b88f8a010d2798c2729c6479bcf41688f4b63fe7269","nodeId":"ca7c00e6-1da4-4f7a-a36d-ab122dc14dfc","outcome":"completed","summary":"Wrote [report.md](artifacts/goal/writeup/report.md), verified at 8,555 bytes.\n\nIncludes both tables, conditional additive gain, and explicit gaps for the table route’s incomplete evaluation and spread-4’s flagged source. Nothing was fabricated.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":268032,"inputTokens":39640,"model":null,"outputTokens":3314,"runtime":"codex","turns":3,"wallClockMs":91762}}],"verification":[{"checks":[],"detail":"paths and tree verified; no suite was run for this kind of work","evaluation":"structural","profile":"none","status":"accepted","submissionHash":"2296ba7711806c5c7fd680a03d07d0a67163e19a9b7c12efb8151f9ae22b76f6","verifiedTreeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","verifierVersion":"0.1.0+18543036"},{"checks":[],"detail":"paths and tree verified; no suite was run for this kind of work","evaluation":"structural","profile":"none","status":"accepted","submissionHash":"338e1e9d7c0b126db5defe8de3590b4bdc7f47f9c9cfd99762fae8b02e48fa34","verifiedTreeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","verifierVersion":"0.1.0+18543036"},{"checks":[],"detail":"paths and tree verified; no suite was run for this kind of work","evaluation":"structural","profile":"none","status":"accepted","submissionHash":"9831d77c548fe184572d48c2140ee0d0e05e6ae8331b2a9cd692f215532bd263","verifiedTreeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","verifierVersion":"0.1.0+18543036"},{"checks":[],"detail":"paths and tree verified; no suite was run for this kind of work","evaluation":"structural","profile":"none","status":"accepted","submissionHash":"abf0d22e2ff9a7b186598dbd0b81dd047ddb5feeb209d6592dcf6657e9963549","verifiedTreeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","verifierVersion":"0.1.0+18543036"},{"checks":[],"detail":"paths and tree verified; no suite was run for this kind of work","evaluation":"structural","profile":"none","status":"accepted","submissionHash":"b0b313509007f271e8c617c3338345057b132f8b6bcef12f1a4710e5bb2ff86a","verifiedTreeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","verifierVersion":"0.1.0+18543036"},{"checks":[],"detail":"paths and tree verified; no suite was run for this kind of work","evaluation":"structural","profile":"none","status":"accepted","submissionHash":"be981b30ead864e4bc550b88f8a010d2798c2729c6479bcf41688f4b63fe7269","verifiedTreeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","verifierVersion":"0.1.0+18543036"}]}