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this network in the top three of Paradigm's Dogfight board.\n\nThe challenge (https://www.paradigm.xyz/puzzles/dogfight, repository https://github.com/benedictbrady/dogfight-challenge): an ONNX model reads a 224-number observation and returns 3 continuous actions each tick, in one-on-one aerial combat. An upload plays about 60 matches against other players' entries. A kill counts 1, a win on hit points 0.75, a draw 0.5.\n\nWhere it stands on 2026-11-06. This network is 5th of 36 with the entry \"IMD seat 798\": 53 kills and 7 losses in 60 matches. Third place has 59 kills and a draw; the top player has won every match. The board's rating, fitted from its public numbers: -6121.625 + 400 x log10(s / (1 - s)), where s is the score per match, capped at 0.999. So third place needs 59 of 60. The board keeps a player's latest upload, not their best, so the next upload must be clearly stronger than the one it replaces. Nothing is uploaded from a task: the network's operator does that by hand, so do not look for a key.\n\nThe current entry is a public JSON file: https://api.imd.fun/artifacts/7c668643b930a45f74bdf25f8e6f0445da8f1722ea1df70d7659dbed76da2537. Its \"entry\" is the ONNX model in base64. Its \"setup\", \"tried\", \"width\" and \"next\" say how its seat built the simulator inside a task (Rust by rustup under .cache/, OpenSSL headers without root, the repository's own run_match with random spawns, about 770 matches a second), what worked (fly slowly because turn rate falls with speed, lead the aim, dodge and break after a shot) and what failed (a rule that beat every earlier version lost 27 percent to a neural opponent trained in 24 seconds). It is a hand-built rule, and it beats the repository's Starter in 2,980 of 3,000 matches.\n\nWhat nobody here knows is how the other players' entries fly: they are not public, and the seven losses were to them. The top player's entry is named for self-play and distillation. The best stand-in is a hard, varied set of opponents built inside the task.\n\nFifty seats work at once and cannot see each other, so each has its own direction or its own run of a method. Every seat reports the same four records so the results can be compared: against Starter over 3,000 matches with random spawns; against the four built-in opponents over 800; against the current entry over 1,000 matches, half from each side; and against the strongest opponent it can train against its own final model in ten minutes. A later round flies the best models against each other and one is uploaded.","parentJobId":null,"planHash":"f5f999ea32a1341c10679a701b798e6c30cf36e02c95aa366f8a721206865cfa","previousHash":"0000000000000000000000000000000000000000000000000000000000000000","projectId":"d94acc27-198d-4d50-b5c6-5cdd0575cf7a","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":"2378865b5bb2c23e4c0e14f1aeec5d2927f8b96a418b3c2241fda38a5116fe82","state":"completed","submissions":[{"artifacts":[{"bytes":35931,"hash":"6ee7ac0d99db64f0b960e0e1e624713fe128d4d53559d54b42c0d35e861ba6c2","mediaType":"application/json","name":"result","path":"artifacts/goal/items/harness-speed/result.json"},{"bytes":956,"hash":"3ee57e1e487720a29a566577925871d002acd581cb9527659a8aaab9c9c1e225","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/harness-speed/sources.json"}],"attempt":1,"bundleHash":null,"device":"4b046083653bdcb3","findings":[],"hash":"014ba9257a889481997c3aa798398290673cbd132b3a4ad658cfb10c406a1706","nodeId":"189de5dd-2a3f-4f3b-9f96-88d55196cf0d","outcome":"completed","summary":"I measured all four records for the current model and wrote `result.json` and `sources.json`. The result is 35.9 KB and its entry is the current model, byte for byte. The working folder is removed, and all five source quotes were found at their URLs.\n\n## Records\n\n| Record | Matches | Score |\n|---|---|---|\n| Starter, harness check (`current_vs_starter`) | 3,000 | 0.992 |\n| Starter, second independent run (`records.starter`) | 3,000 | 0.993 |\n| Four built-ins | 800 | 0.99875 |\n| Current model against itself (410 wins, 410 losses, 180 draws) | 1,000 | 0.5 |\n| Ten-minute exploiter | 500 | **0.17** |\n\nThe harness agrees with the earlier seat: seeds 0–49 gave the same 99 of 100, and its 0.9933 is within noise of my two runs. A larger run of 50,000 matches against Starter, using the compiled rule, gave 0.99116. That makes 60 straight wins against Starter alone about 0.587 likely, not the seat's 0.67.\n\n## Findings for later seats\n- **The entry is easy to exploit:** evolving 44 of the rule's own constants reached about 0.80 against it within 50 seconds. The exploiter's constants are in `next`, so later seats can spar against it; rebuilt from that text, it held the entry to 0.1675 over 400 more matches. It looks specialised, since it scores only 0.903 against Starter.\n- **The neural exploiter was weak:** evolving Starter's output head only got the entry down to 0.976.\n- **Speed:** the rule under ONNX Runtime costs about 425 µs per decision, which is about 45 matches a second on 4 cores, not the seat's 770.\n    - Compiling the rule to Rust brings that to 0.37 µs and 656–671 matches a second against Starter. The seat's text doesn't say how it reached 770, so a native rule is only my guess.\n    - Compiled actions matched ONNX Runtime on 20,000 sampled observations, and tallies matched over 1,000 matches. The official records stayed on ONNX Runtime.\n    - The result also has timings for the three small network sizes, thread settings and the build recipe.\n\n## Caveats\n- **Second build:** my cleanup deleted the working folder before I'd checked the exploiter constants, so I rebuilt the simulator to test them. That second build also showed the extra `CFLAGS` setting isn't needed.\n- **Not tested or assumed:** the board's own upload check wasn't tested. Random spawns are an assumption, because the board's spawn settings aren't public.\n- **Small networks:** the matches-per-second figures come from random-weight networks that crash after about 520 ticks, so they aren't comparable with the full-match rates. The cost per decision is the figure to compare.\n- **`entry_bytes`:** I took this as the decoded ONNX file size (18,930), and `approach` says so.\n\nEverything is in `artifacts/goal/items/harness-speed/`:\n- `result.json`\n- `sources.json`","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":1780158,"cost":{"source":"runtime","usdMicros":2229985},"inputTokens":46,"model":"claude-opus-5-5","outputTokens":50338,"runtime":"claude","turns":26,"wallClockMs":1846627}},{"artifacts":[{"bytes":54224,"hash":"d0f8e8fb7342e0dfea7e71d3aa2218096cbb11ee815c67f14f1e3e5d5269321f","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-distill-4/result.json"},{"bytes":201,"hash":"9ede87683bd7ad241fc3b3b468089e3fcfbb6efe6057ed26e6194fae36efbdfb","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-distill-4/sources.json"}],"attempt":1,"bundleHash":null,"device":"52902570375dd0ae","findings":[],"hash":"03655f6ec50a81527a1783affabfae6a8b2af06a4b1642ae90b7555c2456b10d","nodeId":"0888727b-4107-4852-a6db-a5b4f505d39e","outcome":"completed","summary":"I've written `artifacts/goal/items/sp-distill-4/result.json` (54,224 bytes, parses as JSON) and `sources.json`. The final network is no stronger than the current rule, and a 10-minute exploiter beats it badly, so it should not be uploaded.\n\n**Harness check:** the current entry (sha256 `f39631…5b4` after decoding) scored **0.9903333** against Starter over 3,000 random-spawn matches: 2,971 kills and 29 losses. Its seat measured 0.9933.\n\n**What I built:** a 224-64-64-3 network using only Gemm, Relu and Cast. Its 18,947 weights are stored at half precision, so the file is 38,026 bytes. I made it in three steps, with every random choice seeded with 4:\n1. Copied the current rule from 125k of its decisions against Starter, the four built-ins and itself.\n2. Ran one round of correction training: the copy flew while the rule labelled its decisions.\n3. Improved it by evolution strategies for about 19 minutes against the rule, Starter, the built-ins, the first copy and itself.\n\n**How it improved (all on matches it didn't train on):**\n\n| Stage | vs Starter | vs current rule |\n|---|---|---|\n| First copy | 0.93 | 0.05 |\n| After correction training | 0.95 | 0.15 |\n| After improvement round 1 | 0.98 | 0.49–0.53 |\n| After improvement round 2 (final) | 0.989 | 0.53 |\n\n**Final records, measured on the exported ONNX file:**\n- **Starter (3,000):** 0.9865833\n- **Built-ins (800):** 0.9534375. 93 of those were wins on hit points rather than kills, which pulls it below the rule's 1.0.\n- **Current rule (1,000, half from each side):** 498 wins, 466 losses, 36 draws, score 0.516.\n- **Exploiter (500):** 0.074. I started a copy of the final network and trained it for 570 seconds only against the final model. It won 462 of the 500 matches, and it also scores 0.871 against Starter.\n\n**What fell short of the brief:**\n- I did about 20 minutes of improvement training, not the 90 minutes of work the brief aimed for. Ten minutes were lost to a training run that ran 50 times too slowly with 12 threads (2 threads fixed it), and the rest of the time went on the required measurements and the exploiter.\n- The result file's \"next\" field recommends continuing the method as a league: improve the main network against a growing set of exploiters, and judge each round against a fresh one.\n\n`sources.json` cites the current entry's artifact page. The quote is copied exactly from it, and I checked it is there in the copy I downloaded.\n\nAll working files are under `.cache/`. The repository root also shows untracked dotfiles (`.bashrc`, `.gitmodules`, `.claude/` and others). I didn't create them and left them alone.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":1843917,"inputTokens":73,"model":"claude-opus-5-5","outputTokens":31448,"runtime":"claude","turns":43,"wallClockMs":3741491}},{"artifacts":[{"bytes":55243,"hash":"2d522b0ff70fa96722922308afed5914018741490ec5d51475f1b875f5c6c678","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-starter-5/result.json"},{"bytes":15,"hash":"73608e3346e331760ac29f17144976922e84678fed8a2eb7cc66f6ef0e89a33c","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-starter-5/sources.json"}],"attempt":1,"bundleHash":null,"device":"51c5c269970612db","findings":[],"hash":"07b16aca58953e037d02e3250a4af5b1200b0d5020d84d9e3d12697f65a7f6f3","nodeId":"ac8d5608-a2d2-4593-b9e6-ef1dd01a6463","outcome":"completed","summary":"The model this seat made is much weaker than the current entry and should not be uploaded. The result is in `artifacts/goal/items/sp-starter-5/result.json` (55,243 bytes, valid JSON), with `sources.json` set to `{\"sources\": []}`.\n\n**Harness check:** the current model scored 0.9913333 against the real `starter.onnx` over 3,000 matches with random spawns (2,974 wins, 26 losses). Its seat measured 0.99333.\n\n**Final model:** a 224-64-64-3 network built like Starter, with half-precision weights. The file is 39,180 bytes and uses only Cast, Gemm, LayerNormalization, Relu, Slice, Clip and Concat, all of which the current model or Starter already uses.\n\n| Record | Matches | Score |\n|---|---|---|\n| Starter | 3,000 | 0.8744167 |\n| Four built-ins | 800 | 0.0265625 |\n| Current model | 1,000 | 0.0015 (0 wins, 997 losses, 3 draws) |\n| Exploiter | 500 | 0.123 |\n\nHow it got there, all on matches it did not train on:\n- **Imitating Starter:** the 64-wide copy scored only 0.13 against Starter over 200 matches. Shrinking from 256 to 64 lost most of Starter's strength.\n- **Self-play:** about 10 minutes of training against a league of its own past versions, Starter and the built-ins raised that to 0.91–0.92 against Starter. Storing the weights at half precision cost nothing: 0.91 before and after on the same 200 matches.\n\nIt learned to beat Starter, not to fly well. That's why it scores almost nothing against the built-ins and the current rule.\n\nDepartures from the brief, all stated in the result:\n- **One CPU.** The machine has a single CPU despite reporting 8, so the current model's runtime managed only about 8 matches a second. I left the current rule out of the training league for that reason.\n- **Shorter runs.** Training was 10 minutes, ended by the command time limit; I used the last saved snapshot. The exploiter trained for 200 seconds instead of ten minutes.\n- **Starter stand-in.** Every record except the harness check plays a Rust copy of Starter's weights rather than `starter.onnx` itself. The copy and the real file scored the same on a 40-match check.\n- **Build workaround.** There is no C compiler or linker on the machine, so I built the simulator using zig as the compiler (installed into `.cache/`) and the OpenSSL headers unpacked from Ubuntu's package.\n- **Hash.** `entry_sha256` is the hash of the base64 text, following the current entry's file.\n\nThe suggestion for the next seat: put the built-ins and the current rule into training from the start, or begin by imitating the current rule rather than Starter. Use a machine with more than one CPU.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":1352101,"cost":{"source":"runtime","usdMicros":1277112},"inputTokens":60,"model":"claude-opus-5-5","outputTokens":24817,"runtime":"claude","turns":33,"wallClockMs":2835337}},{"artifacts":[{"bytes":35960,"hash":"6fe8daeb7e2a648e038ff3326fde007501175aab0d76b83113f10f0edc6975ed","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-league-3/result.json"},{"bytes":1686,"hash":"99aa48042f6de2914e73738c2aeccc87a64d6700183daf77c21da1ef578eff43","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-league-3/sources.json"}],"attempt":1,"bundleHash":null,"device":"553a378efc6a19cb","findings":[],"hash":"07c14870cc25fbfd0d6c82ca5023a8514c48eeac663102d43bb0df3028a1d961","nodeId":"0c63b76f-f915-4ec7-9f42-d0759fc42427","outcome":"completed","summary":"Created [result.json](artifacts/goal/items/sp-league-3/result.json) and [sources.json](artifacts/goal/items/sp-league-3/sources.json). JSON, ONNX, hash, size, and source checks passed.\n\nScores: Starter **1.0**, built-ins **0.89625**, current **0.711**, exploiter **0.1355**. The exploiter result argues against replacing the live entry yet.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":13944064,"inputTokens":111528,"model":null,"outputTokens":149777,"runtime":"codex","turns":34,"wallClockMs":6375941}},{"artifacts":[{"bytes":13619,"hash":"b688dc3925d472cd575dd039ba43300e9ebff241a4172def4c83a0f442502834","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-evolution-4/result.json"},{"bytes":1180,"hash":"75db95de96b3cdfa623bd355d61f7171c091b08144cd0bb2066c8a9415e74513","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-evolution-4/sources.json"}],"attempt":1,"bundleHash":null,"device":"5b85781dd6c37cca","findings":[],"hash":"0917bf8bfecb3dd0110b739d6813e896de65c189f0d9f802fd1a8ad03781f27e","nodeId":"b39607ea-65e3-4977-8505-1d4fa3fe78ac","outcome":"completed","summary":"Completed the run-4 [result.json](/home/imdworker/.identitymd/work/d94acc27-198d-4d50-b5c6-5cdd0575cf7a/b39607ea-65e3-4977-8505-1d4fa3fe78ac/artifacts/goal/items/sp-evolution-4/result.json) and [sources.json](/home/imdworker/.identitymd/work/d94acc27-198d-4d50-b5c6-5cdd0575cf7a/b39607ea-65e3-4977-8505-1d4fa3fe78ac/artifacts/goal/items/sp-evolution-4/sources.json). The 7,584-byte ONNX passed validation and all four match records are complete.\n\n**Keep the current upload.** This candidate won only 1 of 1,000 direct matches against it.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":76732288,"inputTokens":746905,"model":"gpt-6-sol","outputTokens":215794,"runtime":"codex","turns":56,"wallClockMs":6124372}},{"artifacts":[{"bytes":1731,"hash":"0153d5686d34a427996aa44e05d6cf9527432f5392dccf6a0cceb0482b42e4a8","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-gradient-3/result.json"},{"bytes":383,"hash":"cebde704832baa6fafb47da4b6c38f8ac62a0b6e70a5ae21ffc15da2103b49c4","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-gradient-3/sources.json"}],"attempt":1,"bundleHash":null,"device":"5f74c0ba32743d88","findings":[],"hash":"09d9e2901f6ba35c3182179dd27afa5409cb506196cebe02fd9c1c05564ac972","nodeId":"24c172dc-6b74-4a82-b7f8-719aa820c196","outcome":"completed","summary":"Created `result.json` and `sources.json`; both parse successfully and meet size limits.\n\nBlocked at simulator compilation: Cargo exited 101 because linker `cc` was unavailable. No training or matches ran; unavailable results are null. Working cache removed.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":177920,"cost":{"source":"estimated","usdMicros":123718},"inputTokens":34252,"model":"gpt-6.1-sol","outputTokens":1963,"runtime":"codex","turns":3,"wallClockMs":81436}},{"artifacts":[{"bytes":27695,"hash":"4bfe36a8d768427ec8ee29c00fcb2fde0baa999e24ff3c7e18385660823432a0","mediaType":"application/json","name":"result","path":"artifacts/goal/items/rule-opening/result.json"},{"bytes":216,"hash":"8008a7a867e5cdb9b0f79fb6b5f53cdae21ed76431fe1d6eddc3e004ef5239f8","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/rule-opening/sources.json"}],"attempt":1,"bundleHash":null,"device":"4f6165fcd0deb045","findings":[],"hash":"0a2399484821b506fd62f966301e59ab77123bca3477641300ade4ff2eeb621c","nodeId":"ed210ab1-d639-4a7f-9449-3ad94d2e7f65","outcome":"completed","summary":"Completed the requested deliverables:\n\n- [result.json](/home/imd-worker/.identitymd/work/d94acc27-198d-4d50-b5c6-5cdd0575cf7a/ed210ab1-d639-4a7f-9449-3ad94d2e7f65/artifacts/goal/items/rule-opening/result.json)\n- [sources.json](/home/imd-worker/.identitymd/work/d94acc27-198d-4d50-b5c6-5cdd0575cf7a/ed210ab1-d639-4a7f-9449-3ad94d2e7f65/artifacts/goal/items/rule-opening/sources.json)\n\nKey measured result: current entry scored `0.9888333333333334` over 3,000 random-spawn Starter matches. The result includes opening-geometry first-hit measurements, 800 built-in matches, paired current-model results, and an explicitly labeled untrained mirror lower bound.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":4108544,"inputTokens":132059,"model":null,"outputTokens":29285,"runtime":"codex","turns":10,"wallClockMs":1714685}},{"artifacts":[{"bytes":45155,"hash":"7c7dc8040118363114afc237bc5799f4f391f43a04f99de6b39b3b4406c1e3b5","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-evolution-2/result.json"},{"bytes":621,"hash":"40ea162cadb8cf035107b562fa8b9a254713076cce3b8b4d26e6ecfbb0af8f08","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-evolution-2/sources.json"}],"attempt":1,"bundleHash":null,"device":"575a41a8798743c2","findings":[],"hash":"0b236d43cfe400628139c5114c6a71c766cb1d25b4cf2cab2fc681983934a36b","nodeId":"73761296-ff12-458d-8013-2f42d31fb7a1","outcome":"completed","summary":"Created `result.json` and `sources.json`. All size, hash, ONNX, match-count, and source checks passed.\n\nFinal scores: Starter 0.68417, built-ins 0.72875, current 0.1625, exploiter 0.002.\n\nThe candidate is weaker than the current entry; the result recommends against uploading it.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":12500352,"inputTokens":99525,"model":null,"outputTokens":71849,"runtime":"codex","turns":81,"wallClockMs":5694368}},{"artifacts":[{"bytes":50760,"hash":"05d45eaf1db35677f06d1d3ec51db0a377587fbaa0b85be01d930428af1bfc80","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-curriculum-5/result.json"},{"bytes":15,"hash":"73608e3346e331760ac29f17144976922e84678fed8a2eb7cc66f6ef0e89a33c","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-curriculum-5/sources.json"}],"attempt":1,"bundleHash":null,"device":"56e50117311155be","findings":[],"hash":"1db216371956263715ba4f7d2aaa143ea2d9e018d872756dc33d99dc6b5d6d5a","nodeId":"5e730cc0-bf74-430d-9abe-30d7ae53d59e","outcome":"completed","summary":"I wrote `result.json` and `sources.json`, but the model I trained is much weaker than the current entry, and the result says it should not be uploaded in its place. `result.json` is 50,760 bytes and the model is 35,893 bytes. `sources.json` is `{\"sources\": []}`, because I relied on no web quotes.\n\nThe method was staged DAgger distillation of the current rule into a 224-64-48-3 network, seed 5. It uses fp16 weights with only Gemm, Relu and Cast, and I confirmed the exported ONNX plays identically to the Rust copy of the network. It never reached 95% against the current rule, so it did not clear the third stage of the curriculum.\n\nThe four records, scored by the board's formula:\n\n| Opponent | Matches | Score |\n|---|---|---|\n| Starter | 3,000 | 0.948 |\n| Built-ins (average of four) | 800 | 0.935 |\n| Current model | 1,000 | 0.06 (53 wins, 933 losses, 14 draws) |\n| Exploiter (my model's score) | 500 | 0.0295 |\n\n- **Harness check:** the current model scored 0.9907 against Starter over 3,000 matches in my harness, against 0.9933 from its own seat.\n- **Built-ins:** per opponent, chaser 0.905, dogfighter 0.935, ace 0.91, brawler 0.99.\n- **Exploiter:** it was a copy of my network, evolved for 10 minutes against the final model. It beat my model 0.9705 over 500 matches, so the model is easy to exploit.\n\nI did not run any seeds beyond seed 5. The next seat should keep the current rule as the base and fine-tune on top of it, and budget for Starter evaluations, which ran at only about 14 matches a second.\n\nFiles are in `artifacts/goal/items/sp-curriculum-5/`:\n- `result.json`\n- `sources.json`","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":1261435,"cost":{"source":"runtime","usdMicros":656278},"inputTokens":55,"model":"claude-sonnet-5-5","outputTokens":26725,"runtime":"claude","turns":29,"wallClockMs":3225143}},{"artifacts":[{"bytes":27302,"hash":"6a0542b19814b65b09e6c8d954351ffb07670063175b823a8192ba51448c68d9","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-league-1/result.json"},{"bytes":152,"hash":"738b2b88181b71e643b99bc4d159365d3aed86a02d41b482abd30c0ad9564d56","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-league-1/sources.json"}],"attempt":2,"bundleHash":null,"device":"61794f1d37e04d2f","findings":[],"hash":"23fc08b6c19615ee9d3cf7f67fbd69fd18ee01c1713df908259a54b878925a14","nodeId":"af29cb01-16bc-4f12-842b-ca1cee323b44","outcome":"completed","summary":"Completed the required deliverables:\n\n- [result.json](/home/imd-worker/.identitymd/work/d94acc27-198d-4d50-b5c6-5cdd0575cf7a/af29cb01-16bc-4f12-842b-ca1cee323b44/artifacts/goal/items/sp-league-1/result.json)\n- [sources.json](/home/imd-worker/.identitymd/work/d94acc27-198d-4d50-b5c6-5cdd0575cf7a/af29cb01-16bc-4f12-842b-ca1cee323b44/artifacts/goal/items/sp-league-1/sources.json)\n\nBoth parse correctly. Result size is 27,302 bytes; embedded ONNX is 18,930 bytes and validates with permitted operators.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":3943680,"inputTokens":130453,"model":null,"outputTokens":25985,"runtime":"codex","turns":9,"wallClockMs":1563211}},{"artifacts":[{"bytes":34114,"hash":"d8394a253959a3ef0e1d02c6514838c8d79627c807ac155bc104e2d26cddd63c","mediaType":"application/json","name":"result","path":"artifacts/goal/items/harness-losses/result.json"},{"bytes":1082,"hash":"d94e6d829f07c9bd9e652091731bc5555b82678a9406363bf9c7a7d5115dc044","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/harness-losses/sources.json"}],"attempt":1,"bundleHash":null,"device":"4ba44f3d5ab7b010","findings":[],"hash":"25ca690345fc1a4aa8f1f29de08e9dace6cf9ff39545096a0ad1e7d86dc3da3c","nodeId":"55192d4e-97b4-457e-b53a-098d8af6558a","outcome":"completed","summary":"Created and validated `result.json` and `sources.json`. Entry unchanged; result is 34,114 bytes.\n\nScores: Starter **0.9923333333333333**, built-ins **1.0**, mirror **0.5**, trained exploiter **0.259**.\n\nRecorded loss causes and a dodge change that reduced mutual kills but worsened mirror score.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":3585920,"inputTokens":111452,"model":null,"outputTokens":25369,"runtime":"codex","turns":10,"wallClockMs":882439}},{"artifacts":[],"attempt":1,"bundleHash":null,"device":"52f3a4ff01dcb0e7","findings":[],"hash":"26f663f2b868d3b759b439684811d631e3e32604ebe84c51d9cc863b46df64cd","nodeId":"af29cb01-16bc-4f12-842b-ca1cee323b44","outcome":"failed","summary":"required outputs are missing or invalid:\nartifacts/goal/items/sp-league-1/result.json: ENOENT: no such file or directory, lstat '/home/imd-worker/.identitymd-2/work/d94acc27-198d-4d50-b5c6-5cdd0575cf7a/af29cb01-16bc-4f12-842b-ca1cee323b44/artifacts'\nartifacts/goal/items/sp-league-1/sources.json: ENOENT: no such file or directory, lstat '/home/imd-worker/.identitymd-2/work/d94acc27-198d-4d50-b5c6-5cdd0575cf7a/af29cb01-16bc-4f12-842b-ca1cee323b44/artifacts'\n\nthe agent stopped (max_turns, 61 turns) without a final message","treeHash":null,"usage":{"cachedInputTokens":3240179,"inputTokens":118,"model":"claude-sonnet-5","outputTokens":26960,"runtime":"claude","turns":61,"wallClockMs":1993356}},{"artifacts":[{"bytes":57811,"hash":"740c12081cdb6dbd473463a20acb1fa366fbbf3f24989d0a1aa172236ba4b4f5","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-curriculum-4/result.json"},{"bytes":1159,"hash":"7389fb891653881c9c37af7b627be981fc66e2788f4642dacf49246f7e557a66","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-curriculum-4/sources.json"}],"attempt":1,"bundleHash":null,"device":"5658a656572d8dd1","findings":[],"hash":"285e56d125de282526429630c198da294886f6bc3bb221c21fac3205e8f4ceea","nodeId":"74c1a768-e5d6-43fb-9c75-9da8aed88550","outcome":"completed","summary":"Created and locally validated `result.json` and `sources.json` in the requested directory.\n\n| Opponent | Matches | Score |\n|---|---:|---:|\n| Starter | 3,000 | 0.9928333333333333 |\n| Built-ins | 800 | 1.0 |\n| Current | 1,000 | 0.8655 |\n| Exploiter | 500 | 0.065 |\n\nModel: 36,463 bytes. Result: 57,811 bytes.\n\nThe 95% current-stage gate remained unmet, and the exploiter exposed a major weakness. This is not a proven board upgrade.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":18391296,"inputTokens":134404,"model":null,"outputTokens":152290,"runtime":"codex","turns":44,"wallClockMs":5859748}},{"artifacts":[{"bytes":37022,"hash":"7ed35247f4ffb5219cf27162be9378352ac3e01a8d3c07ea8f2dd276a717d1cc","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-curriculum-2/result.json"},{"bytes":318,"hash":"196646bbbd29c285d98ace71ca8b09d207bb09bdde05a13d3e7d557825fe7213","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-curriculum-2/sources.json"}],"attempt":1,"bundleHash":null,"device":"563355f47e653bac","findings":[],"hash":"2f03cc93826aaf4a418dfc29aced6b8f6bf9cf219ce547341cc631d9501ad6e0","nodeId":"63680570-da2e-4209-a599-71846a323090","outcome":"completed","summary":"Wrote and validated [result.json](artifacts/goal/items/sp-curriculum-2/result.json) and [sources.json](artifacts/goal/items/sp-curriculum-2/sources.json).\n\nScores: Starter **0.9755**, built-ins **1.0**, current **0.917**, exploiter **0.144**.\n\nThe 95% current-stage win gate was not met. This candidate should not replace the live entry.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":21437696,"inputTokens":117304,"model":null,"outputTokens":116729,"runtime":"codex","turns":42,"wallClockMs":5553563}},{"artifacts":[{"bytes":26742,"hash":"91361b951e140bc1d87edecf108a1b20d27c794a917075e862c55cefdb79df42","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-curriculum-3/result.json"},{"bytes":14,"hash":"4e60a0deda7ee9d829904e581ebb31ee4b4a9ae1797281044a27ed0f698a9420","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-curriculum-3/sources.json"}],"attempt":1,"bundleHash":null,"device":"564841ef9657d63c","findings":[],"hash":"3443fb3e992a6a1b6ccfce7e6b7077228d329b78433318d75a81bdadeb65f461","nodeId":"401150c0-5d85-4934-ae08-a9647d0efcc0","outcome":"completed","summary":"The simulator build was blocked while compiling `openssl-sys`: the machine lacks OpenSSL development files and `pkg-config`, and `apt-get download` could not locate either package. No training or match evaluations were run, so those scores are recorded as unavailable.\n\nThe required files are present and valid JSON: [result.json](/home/ubuntu/.identitymd/work/d94acc27-198d-4d50-b5c6-5cdd0575cf7a/401150c0-5d85-4934-ae08-a9647d0efcc0/artifacts/goal/items/sp-curriculum-3/result.json) and [sources.json](/home/ubuntu/.identitymd/work/d94acc27-198d-4d50-b5c6-5cdd0575cf7a/401150c0-5d85-4934-ae08-a9647d0efcc0/artifacts/goal/items/sp-curriculum-3/sources.json). The supplied ONNX entry is preserved unchanged.\n\nNot delivered: artifacts/goal/items/sp-curriculum-3/.cache/cargo/.global-cache, artifacts/goal/items/sp-curriculum-3/.cache/cargo/.package-cache, artifacts/goal/items/sp-curriculum-3/.cache/cargo/.package-cache-mutate, artifacts/goal/items/sp-curriculum-3/.cache/cargo/bin/cargo, artifacts/goal/items/sp-curriculum-3/.cache/cargo/bin/cargo-clippy, artifacts/goal/items/sp-curriculum-3/.cache/cargo/bin/cargo-fmt, artifacts/goal/items/sp-curriculum-3/.cache/cargo/bin/cargo-miri, artifacts/goal/items/sp-curriculum-3/.cache/cargo/bin/clippy-driver and 7390 more. Under artifacts/, only this step's named outputs leave the machine.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":214144,"inputTokens":25720,"model":null,"outputTokens":3230,"runtime":"codex","turns":5,"wallClockMs":104069}},{"artifacts":[{"bytes":10713,"hash":"45a86f50420510283114f59e2a59430d58210c7e1490b76fa7e133e377648d86","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-evolution-1/result.json"},{"bytes":648,"hash":"a0e60f7adc5d394698a4e83483ede30486d5ebb4cc63a57d93d92c1bb759aab5","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-evolution-1/sources.json"}],"attempt":1,"bundleHash":null,"device":"5739ce0d803a43cd","findings":[],"hash":"39141f0feb1e0cd115b123fc7a118f06fd85a28d012e48f218addf9e36288231","nodeId":"bcbc5ca7-7182-4773-a28f-31f0920b408d","outcome":"completed","summary":"I wrote both files, `result.json` (10,713 bytes) and `sources.json`, and both parse. The evolved model beats Starter and the four built-ins, but it **lost all 1,000 matches against the current entry**, so it should not be uploaded.\n\n**Harness check:** the current entry scored **0.9906666666666667** against Starter over 3,000 random-spawn matches. Its seat measured 0.9933.\n\n**What I built:** a small network (56 inputs from the current frame, 16 hidden units, 3 outputs; 963 weights, 4,592-byte ONNX). It only uses `Gather`, `Gemm` and `Relu`, which Starter or the current entry already use. I evolved it from random weights with seed 1:\n- **Search:** population 48, step size 0.05, learning rate 0.01.\n- **Fitness:** kills first, then hit-point difference, then time to kill.\n- **Opponents:** the pool started with Chaser, the weakest built-in. Each stage added its best network, the next built-in, and then Starter. From stage 36 the current entry played a quarter of the training matches.\n\nThat was about 40 minutes of matches. I picked the stage-38 network on held-out seeds, and the ONNX file gives exactly the same results as the network running natively.\n\n**Final records** (all on seeds never used in training):\n\n| Opponent | Matches | Score |\n|---|---|---|\n| Starter | 3,000 | **0.9926666666666667** (2,960 wins, 4 losses, 36 draws) |\n| Four built-ins | 800 | **0.9765625** (Brawler is the weakest at 0.946) |\n| Current entry | 1,000 (half from each side) | **0**: 0 wins, 1,000 losses |\n| 10-minute exploiter | 500 | **0** |\n\nThe exploiter used the same method, started from the final model's own weights and trained only against it. It sat at 0.50 for about 4.5 minutes, then won every held-out match from about 5.5 minutes on.\n\nSo this run roughly matches the current entry against Starter (0.9927 vs 0.9907 here) but is brittle. A quarter of training against the current entry over about 18 minutes never won a held-out match against it. The `next` field suggests what the other runs of this method could change:\n- put the current entry and exploiters in the pool from the start;\n- feed the network all four frames (224 inputs) with a bigger hidden layer;\n- keep every exploiter in the pool permanently;\n- or start by copying the current entry into the network instead of from random weights.\n\n**Sources:** three quotes, one from the repository's `RULES.md` and two from the current entry's JSON. I checked each against the copies I downloaded, not by fetching the URLs again. Every working file is under `.cache/`.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":2123322,"cost":{"source":"runtime","usdMicros":1634138},"inputTokens":83,"model":"claude-opus-5-5","outputTokens":32294,"runtime":"claude","turns":52,"wallClockMs":3343224}},{"artifacts":[{"bytes":56518,"hash":"ca9a424352e1c075306e69a301a23f4a034715fb6e66757b72e1b664dc5834f3","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-starter-1/result.json"},{"bytes":510,"hash":"a6e03f76ed0489cfc2ecd3f09f46f08af42c00a149e369d079cbad32b56f5254","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-starter-1/sources.json"}],"attempt":1,"bundleHash":null,"device":"4fcac33561ba733d","findings":[],"hash":"4f99def9b189f45c6cc442a7915576a3166dd8cd00d6f597c622b2616b3cb1cd","nodeId":"bf251672-d897-4ac7-9cd4-c6b77ce54408","outcome":"completed","summary":"The result is written: a 64-wide self-play model that beats Starter and the current entry, but a 9-minute exploiter beats it almost every match. On these numbers I wouldn't upload it over the current entry; fly it in the later round first.\n\n**Final model:** a 224-64-64-3 ReLU network with 18,755 parameters. The file is 38,122 bytes with half-precision weights, and the ops are Cast, Gemm and Relu. It passes the repository's validator.\n\n**Harness check:** the current model scores 0.99067 against Starter over 3,000 matches. The seat measured 0.9933; the gap is 8 matches, which is within noise.\n\n| Record | Matches | Score | Detail |\n|---|---|---|---|\n| Starter | 3,000 | 0.98558 | |\n| Four built-ins | 800 | 0.98938 | |\n| Current entry | 1,000 | 0.938 | 936 wins, 59 losses, 5 draws |\n| Exploiter | 500 | **0.034** | 474 elimination losses |\n\n- **The exploiter is the main finding:** PPO warm-started from the Starter imitation needed only 9 minutes to beat this model. Exploiters warm-started from the model itself or from a stored league exploiter reached only 0.67 to 0.74 for themselves, so the league's exploiters, all warm-started from the main model, never found this weakness. `next` in the result recommends adding exploiters from fresh lines.\n- **Width isn't the smallest option:** I started 32 and 64 side by side. Width 32 reached only 0.154 against the current entry at round 160, while width 64 had 0.73 by round 140, so I dropped 32.\n- **Not one clean run:** every seed is 1, but the final model comes from parallel forks that changed learning rate and reward partway. The learning rate also isn't the smallest throughout: that lineage ran at up to 3e-4.\n- **Reward change:** the final lineage penalises time-out draws. That was the fix for 90-second tail chases against Starter, which caused about 15–20% draws.\n- **Half-precision check:** on identical seeds it scored 0.98725 against 0.9865 for full precision versus Starter. Versus the current entry it scored 0.9245 against 0.9355 over 500 matches, within noise, so no loss is shown but none is ruled out.\n- **Exploiter time:** it trained for 9 minutes rather than the full ten, sharing the machine with the two other exploiters.\n- **Sources not re-fetched:** I checked both quotes in `sources.json` against the copies I downloaded, not against the live pages.\n\nThe result is 56,518 bytes, and I confirmed the decoded entry matches the model file and its checksum. Working files stayed in `.cache/`, which git ignores.\n\nFiles are in `artifacts/goal/items/sp-starter-1/`:\n- `result.json`\n- `sources.json`","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":3032384,"cost":{"source":"runtime","usdMicros":3265306},"inputTokens":64,"model":"claude-opus-5-5","outputTokens":74470,"runtime":"claude","turns":42,"wallClockMs":6301673}},{"artifacts":[{"bytes":28919,"hash":"9bfb43e488d607e6d41a6e448888e02f700ddaad505b10bba4fd7695d102b17b","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-league-4/result.json"},{"bytes":404,"hash":"17052c1b40604236a0a7402ef51c2005d9c170d1f8f2aaa23b00f5ec86f450ac","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-league-4/sources.json"}],"attempt":1,"bundleHash":null,"device":"5563df7fc7719bde","findings":[],"hash":"50ba2bfd99c6c157f1b8b030dfd51e897280cf4e410d2a94168592473c631cc4","nodeId":"d6c039ae-c2aa-4278-8ecf-c905974c5366","outcome":"completed","summary":"I didn't find anything stronger than the current entry. The model I return is the current hand-built rule, unchanged, because it had the best worst-case record against the league I built. The result and sources files are written to `artifacts/goal/items/sp-league-4/`. The result is 28,919 bytes and parses as JSON, and both source quotes were checked against their URLs.\n\n**Why the league is small:** this sandbox gives about one CPU core (16 processes used 34 s of CPU in 35 s of wall time). That allows 5 to 12 matches a second, not the ~770 the earlier seat reported. The machine also has no C compiler and no OpenSSL headers, and apt couldn't find libssl-dev. So I built a small separate harness over the repo's own `run_match` with random spawns. It loads ONNX Runtime from the pip package and links with zig.\n\n**Records (scores per match by the board's formula, from the rule's side):**\n\n| Test | Matches | Score |\n|---|---|---|\n| Current model vs Starter (harness check) | 3,000 | 0.9906666666666667 (2,972 kills, 28 losses; the seat measured 0.9933) |\n| Four built-ins | 800 | 1.0 |\n| Current model (itself, half from each side) | 1,000 | 0.5 (409 wins, 409 losses, 182 draws) |\n| Exploiter trained 10 minutes against it | 500 | 1.0 |\n\nThe Starter record is the harness check itself, because the final model is the same file. The exploiter was Starter with a small added network, trained by evolution strategies.\n\n**What the league tried (seed 4, hidden size 64 as the fourth option):**\n- **Ace-based exploiter:** got no learning signal in 5 minutes, because it never scored against the rule. I added an HP-difference reward after this.\n- **Starter-based exploiter:** lost every match after 4 minutes of training, and 40 of 40 on held-out seeds.\n- **Corrected rule (the rule plus a small trained network):** did well against Starter and the exploiters, but scored only 0.225 against the unchanged rule on held-out seeds, so I rejected it.\n\nThe 10-minute exploiter run was stopped at its time limit, so its training log was lost. The result says this rather than giving a step count.\n\n**Advice for the next seat:** run this method only with real compute. Exploiters should start by imitating the rule rather than from Starter, so they are competitive from the start. Any changed rule should be checked against the original rule first, since that was the only opponent that exposed the corrected rule's weakness.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":1425394,"cost":{"source":"runtime","usdMicros":1283284},"inputTokens":66,"model":"claude-opus-5-5","outputTokens":26632,"runtime":"claude","turns":38,"wallClockMs":2726966}},{"artifacts":[],"attempt":1,"bundleHash":null,"device":"5835e48821d8827d","findings":[],"hash":"5d513f93bde6374ff7aa81ad10c9147822a717fce1dc1fc2579b606e28de1f2b","nodeId":"c2892870-9079-4835-ab70-cec132755e29","outcome":"failed","summary":"required outputs are missing or invalid:\nartifacts/goal/items/rule-losses/result.json: ENOENT: no such file or directory, lstat '/home/seat/.identitymd/work/d94acc27-198d-4d50-b5c6-5cdd0575cf7a/c2892870-9079-4835-ab70-cec132755e29/artifacts'\nartifacts/goal/items/rule-losses/sources.json: ENOENT: no such file or directory, lstat '/home/seat/.identitymd/work/d94acc27-198d-4d50-b5c6-5cdd0575cf7a/c2892870-9079-4835-ab70-cec132755e29/artifacts'\n\nthe agent stopped (max_turns, 61 turns) without a final message","treeHash":null,"usage":{"cachedInputTokens":2819806,"cost":{"source":"runtime","usdMicros":1098365},"inputTokens":116,"model":"claude-sonnet-5-5","outputTokens":38255,"runtime":"claude","turns":61,"wallClockMs":2325999}},{"artifacts":[{"bytes":28488,"hash":"8841ed8ca38c275fe3f8adbe88dcc607223513ba713974551b69b486c5154b81","mediaType":"application/json","name":"result","path":"artifacts/goal/items/rule-plus-net/result.json"},{"bytes":784,"hash":"27839406c3d38e451cbe2c379ca585d20d457a990b07c356fdef5516a0b0b986","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/rule-plus-net/sources.json"}],"attempt":1,"bundleHash":null,"device":"4fa6ac0d62fd2e01","findings":[],"hash":"6185834b9a77a5bdf598405922f59664894e9f78be2f3d40cd3c30916e2ad04a","nodeId":"4b3b8d94-194a-406d-af8f-cee19303fc1c","outcome":"completed","summary":"Implemented the residual ONNX model and recorded the benchmark results in [result.json](/Users/timhsia/.identitymd-3/work/d94acc27-198d-4d50-b5c6-5cdd0575cf7a/4b3b8d94-194a-406d-af8f-cee19303fc1c/artifacts/goal/items/rule-plus-net/result.json) and sources in [sources.json](/Users/timhsia/.identitymd-3/work/d94acc27-198d-4d50-b5c6-5cdd0575cf7a/4b3b8d94-194a-406d-af8f-cee19303fc1c/artifacts/goal/items/rule-plus-net/sources.json).\n\nFinal scores: Starter **0.990333…**, built ins **1.0**, current model **0.5765**, exploiter **0.568**. The current model’s calibration score against Starter was **0.991** on my seed set.\n\nBoth JSON files parse. The ONNX model is **19,512 bytes** and the result is **28,488 bytes**; the embedded model hash and operator list were verified.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":85571712,"cost":{"source":"estimated","usdMicros":1058204},"inputTokens":1010189,"model":"gpt-6-luna","outputTokens":202936,"runtime":"codex","turns":62,"wallClockMs":5594335}},{"artifacts":[{"bytes":62651,"hash":"5dbf7301fbb820af18c218bac35b8214cbcfef3003d480328f50f531d5a4e69c","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-gradient-5/result.json"},{"bytes":668,"hash":"cb2ed7f006b8e0c5d6f3849c044e9a90139f6f048ce3eab1ed4206c7817c165e","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-gradient-5/sources.json"}],"attempt":1,"bundleHash":null,"device":"5dc170d977094c92","findings":[],"hash":"64900e9aa73335e95c7869132ec24d018cf4c39909306f195302d111b3f3c0f2","nodeId":"8ae5c170-a005-4aa9-9c18-56b0ecf32d2c","outcome":"completed","summary":"Created [result.json](artifacts/goal/items/sp-gradient-5/result.json) and [sources.json](artifacts/goal/items/sp-gradient-5/sources.json). Validation passed; result size is 62,651 bytes.\n\nFinal scores: Starter **0.9805**, built-ins **1.0**, current **0.794**, exploiter **0.401**.\n\nThe candidate improves the current-rule matchup but loses Starter robustness and remains exploitable.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":9659264,"inputTokens":70973,"model":null,"outputTokens":64670,"runtime":"codex","turns":75,"wallClockMs":5694076}},{"artifacts":[{"bytes":27163,"hash":"269dcb9768221e14cc381b577917f73ebfcf4f79cd017335844b8ae59a8edf7a","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-evolution-3/result.json"},{"bytes":414,"hash":"0ccf85d96071e8fcc682dd5d76c8893faadbfb55453b5ec6809aef39ad7429a0","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-evolution-3/sources.json"}],"attempt":1,"bundleHash":null,"device":"59b51c92352647b4","findings":[],"hash":"65b297cae5437283ed4f6ec0bb20c134dd6571b43c9bb0175394caaacf5e7de0","nodeId":"dacfdbc6-0cef-4ec3-8710-d8d5aa97b0ce","outcome":"completed","summary":"Created and validated:\n\n- [result.json](/home/imd-worker/.identitymd/work/d94acc27-198d-4d50-b5c6-5cdd0575cf7a/dacfdbc6-0cef-4ec3-8710-d8d5aa97b0ce/artifacts/goal/items/sp-evolution-3/result.json)\n- [sources.json](/home/imd-worker/.identitymd/work/d94acc27-198d-4d50-b5c6-5cdd0575cf7a/dacfdbc6-0cef-4ec3-8710-d8d5aa97b0ce/artifacts/goal/items/sp-evolution-3/sources.json)\n\nJSON parses, size is within limits, and the ONNX SHA-256 was verified. The result records the ONNX runtime linking blocker explicitly.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":2957184,"inputTokens":98243,"model":"gpt-5.6-luna","outputTokens":18029,"runtime":"codex","turns":6,"wallClockMs":596519}},{"artifacts":[{"bytes":58921,"hash":"1a4eb9dc661a1d8ca761aa87e4c20523e609717873681a10d9bbf3ba38db441b","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-distill-2/result.json"},{"bytes":424,"hash":"ec91488c925cdc844b0d856921a6c290c7bde0f5a8b6521df7e96ba4d2cc612f","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-distill-2/sources.json"}],"attempt":1,"bundleHash":null,"device":"523a0aade61e8902","findings":[],"hash":"67d2cec4c253c9a6a82c33be3503804ef617068cd20eb9f4e3df89387bc528ad","nodeId":"ab8b6e53-3aff-4cc1-807d-36ad012c5ab7","outcome":"completed","summary":"Completed the distillation run and wrote [result.json](/home/imd/agents/1571/data/work/d94acc27-198d-4d50-b5c6-5cdd0575cf7a/ab8b6e53-3aff-4cc1-807d-36ad012c5ab7/artifacts/goal/items/sp-distill-2/result.json) and [sources.json](/home/imd/agents/1571/data/work/d94acc27-198d-4d50-b5c6-5cdd0575cf7a/ab8b6e53-3aff-4cc1-807d-36ad012c5ab7/artifacts/goal/items/sp-distill-2/sources.json).\n\nThe final model scored **0.989** against Starter, **1.0** against built-ins, **0.518** against the current entry, and **0.51** against the searched exploiter. The current-entry baseline against Starter was **0.9933333333333333**. The candidate improves the head-to-head result but scores lower against Starter; the result records that limitation.\n\nThe ONNX validator passed. JSON schemas, source quotes, hash, and size checks passed; the result is 58,921 bytes and the model is 41,421 bytes.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":46350464,"inputTokens":393317,"model":null,"outputTokens":134579,"runtime":"codex","turns":19,"wallClockMs":5090725}},{"artifacts":[{"bytes":28152,"hash":"4781317ca935d54e04bf583a200563a3aa1b4ca691a0b95b30f8545315d7d882","mediaType":"application/json","name":"result","path":"artifacts/goal/items/rule-harden/result.json"},{"bytes":188,"hash":"001253042d4fbd4bde08b6cd2c9e59557c764c6220618970ee90f7b6f35a1bd6","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/rule-harden/sources.json"}],"attempt":1,"bundleHash":null,"device":"4dd67dae195771b6","findings":[],"hash":"69f3a7ec6ce269dd75500119f101430d0e46db05fdd5954b8bacdba0c84625c8","nodeId":"ae493e2d-969e-417d-a619-bacdd4f18ba1","outcome":"completed","summary":"I did not harden the rule. The result file re-submits the current model unchanged, so it is no stronger than the entry already on the board. I got through setup and the measurements, but not the five rounds of exploit-and-patch.\n\n- **Setup:** the simulator is built under `.cache/`. This machine has no C compiler, so I extracted gcc, binutils, libstdc++ and libssl from debs by hand. I then wrote a harness around the repository's `run_match` and `OnnxPolicy` with random spawns, both sides, scored by the board's formula.\n- **Why no patching:** the rule is a 672-node ONNX graph and I don't have its source. ONNX evaluation ran at only about 12 matches a second here, which made a patching loop too slow.\n- **Current vs Starter, 3,000 matches:** 2,968 kills, 1 draw, 31 losses, score 0.9895. The seat measured 0.9933. I report mine as `current_vs_starter`.\n- **Built-ins, 800 matches:** 799 kills and 1 loss (to ace), score 0.99875.\n- **Current vs itself, 1,000 matches:** 396 wins, 396 losses, 208 draws, score 0.5. This is a mirror, so it only shows the harness is symmetric.\n- **Exploiter:**\n  - I evolved only the 771 numbers in Starter's two output heads against the current model, over 10 generations in about 7 minutes.\n  - It took 3 kills in 500 matches, so the current model scored 0.994. No blind spot turned up.\n  - The previous seat's 24-second exploiter got the model down to 0.73. Mine is much weaker, so 0.994 says nothing about robustness.\n- **Next seat:** the result's `next` field says to speed up inference or reimplement the rule in Rust, then train a full-network exploiter with a hit-based reward. It also says to patch the rule in Python and re-export, rather than editing the graph.\n\nThe result is 28,152 bytes, under the 64,000 limit. `sources.json` lists one source, the entry URL. Both files are in `artifacts/goal/items/rule-harden/` and left untracked.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":1175295,"cost":{"source":"runtime","usdMicros":446391},"inputTokens":61,"model":"claude-sonnet-5-5","outputTokens":15852,"runtime":"claude","turns":32,"wallClockMs":1801856}},{"artifacts":[{"bytes":28397,"hash":"1b7f333a11cfe9d9ea3f6bda2f2ca7a80daa9b7263b606ac724dfc7009fe3004","mediaType":"application/json","name":"result","path":"artifacts/goal/items/rule-gunnery/result.json"},{"bytes":955,"hash":"95639f5c770ab5f5ddde5fb001356713cfd17329e5d2d1895f4ad11d0775beeb","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/rule-gunnery/sources.json"}],"attempt":1,"bundleHash":null,"device":"4dd9389cdce7acfe","findings":[],"hash":"6bbe84b9d197c7b12b0b94fc32b3a1afa14b6ba0b72b74e5ac751a4e71adc563","nodeId":"1fc3e7c4-f9b8-43d8-8969-eec0e1cb91ed","outcome":"completed","summary":"Completed `rule-gunnery`.\n\n- [result.json](/home/imdworker3/.identitymd/work/d94acc27-198d-4d50-b5c6-5cdd0575cf7a/1fc3e7c4-f9b8-43d8-8969-eec0e1cb91ed/artifacts/goal/items/rule-gunnery/result.json)\n- [sources.json](/home/imdworker3/.identitymd/work/d94acc27-198d-4d50-b5c6-5cdd0575cf7a/1fc3e7c4-f9b8-43d8-8969-eec0e1cb91ed/artifacts/goal/items/rule-gunnery/sources.json)\n\nFinal records: Starter 0.993333, built-ins 0.99875, current entry 0.5345, trained exploiter 0.503. The 18,930-byte ONNX passed validation. JSON, hashes, size limits, base64, and live source quotes were verified. Temporary `.cache` material was removed.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":12236544,"inputTokens":137834,"model":"gpt-5.6-sol","outputTokens":41487,"runtime":"codex","turns":11,"wallClockMs":2324712}},{"artifacts":[{"bytes":48080,"hash":"e8ef38b4f96d6da87cdde4235fb04b0e05cfa92c701325d119e8996aeeaeb386","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-gradient-1/result.json"},{"bytes":206,"hash":"8da9648b07e265383def062d4a8d42b56c51303db39ab53a92cf3e763df66ced","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-gradient-1/sources.json"}],"attempt":1,"bundleHash":null,"device":"5c1e4764d4e20ef5","findings":[],"hash":"70bb674bf6fb4a02a5cfba09396901f871871f99b097397fdeca47c323ff772b","nodeId":"69e0bb29-8c92-49ad-acf7-dcfaa9ac3c47","outcome":"completed","summary":"I wrote both files, but the model I trained is weak and should not be uploaded. It beats Starter 80% of the time, while the current entry beats it 99%, and it lost 980 of 1,000 matches to the current entry.\n\n**Harness check:** the current entry scored **0.993** against Starter over 3,000 random-spawn matches (2,979 kills, 21 losses). Its seat measured 0.99333, so the harness agrees.\n\n**Final model's four records** (board formula, from my model's side):\n\n| Opponent | Matches | Score |\n|---|---|---|\n| Starter | 3,000 | 0.80025 |\n| Four built-ins | 800 | 0.900625 (chaser 0.885, dogfighter 0.885, ace 0.864, brawler 0.969) |\n| Current entry, half from each side | 1,000 | 0.017 (14 wins, 6 draws, 980 losses) |\n| Exploiter | 500 | 0.488 |\n\n**The model:** a 224-32-32-3 network (ReLU layers, output clipped to ±1), 8,451 parameters, in float32. The ONNX file uses only Gemm, Relu and Clip, all operators Starter uses, and its output matched the PyTorch model on 50 test inputs. I first trained it to copy the current rule (it reached 0.66 against Starter), then ran PPO self-play with seed 1. The opponent pool held recent snapshots, the copy, the current rule, Starter and the four built-ins. Rewards were ±0.1 per hit point dealt or taken plus the match result. I kept the checkpoint that did best on matches it never trained on, against Starter and Ace.\n\n**Where it fell short of the brief:**\n- **Training time:** PPO ran for only about 8 minutes, not the 90 asked for. Most of the session went on setup:\n  - This machine has no C compiler, so I used zig as the linker and pointed it at the system's C++ library.\n  - ONNX Runtime and PyTorch each ran 10 or more times slower until I limited them to one thread.\n- **Score curve:** held-out scores were still rising against Ace (0.55 to about 0.9) and had levelled off around 0.75–0.8 against Starter.\n- **Exploiter:** it trained for 150 seconds instead of ten minutes. It was a PPO policy started from the copy of the rule and trained only against my final model. It scored roughly even (0.488), so this record says little.\n\nThe result's `next` field gives the next seat the build recipe and the single-thread fixes. It also suggests a 64-wide network stored at half precision, which still fits the 44 KB limit, and at least an hour of PPO.\n\n**Sources:** `sources.json` has one entry, the current entry's JSON, quoting its 3,000-match Starter baseline. I checked the quote appears there word for word.\n\nBoth files are in `artifacts/goal/items/sp-gradient-1/`: `result.json` (48,080 bytes, model 33,850 bytes) and `sources.json`; both parse. All working files are under `.cache/`. Some dotfiles (`.bashrc`, `.claude/` and others) show as untracked in git; I didn't create them and left them alone.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":1821941,"cost":{"source":"runtime","usdMicros":1541993},"inputTokens":67,"model":"claude-opus-5-5","outputTokens":31265,"runtime":"claude","turns":37,"wallClockMs":4017317}},{"artifacts":[{"bytes":58869,"hash":"167388623a37712672a4737b2a7aae8935ffbfc7f2d879bdb738d6ee8baac15b","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-starter-2/result.json"},{"bytes":1283,"hash":"87bd509265adc02c5a152434c925b9b0dc8a32439db050f71542ceea3b72292c","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-starter-2/sources.json"}],"attempt":1,"bundleHash":null,"device":"500cef6ec1073663","findings":[],"hash":"71383783bd795daf141ba4d3e030443d28c3357870099eee74ea6a343d31f4a9","nodeId":"1fb51c56-abd4-40a9-a21e-2cff40e2dc3d","outcome":"completed","summary":"Created and validated [result.json](artifacts/goal/items/sp-starter-2/result.json) and [sources.json](artifacts/goal/items/sp-starter-2/sources.json).\n\nThe model scored **0.004 against the current entry** and **0.002 against the trained exploiter**. Do not use it as a replacement upload.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":22437632,"inputTokens":160427,"model":null,"outputTokens":162391,"runtime":"codex","turns":73,"wallClockMs":5786481}},{"artifacts":[{"bytes":41155,"hash":"92b406db6224baff99835ed131bb5d4a4acac50f2132c5fa57c32a34abf2fef7","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-response-2/result.json"},{"bytes":1663,"hash":"50832b0da7e7355c567ccad76efd5f96c25cfa7dbcc2bbeebe31fec3dc4747d2","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-response-2/sources.json"}],"attempt":1,"bundleHash":null,"device":"5e0997ddda681c32","findings":[],"hash":"78ee87d663ba14645c14dc2293ae2529f58d27e1ab4983d579dbb0d57b093f89","nodeId":"54d4e36b-0bf9-43d6-a557-2265a27fa530","outcome":"completed","summary":"Created and validated [result.json](artifacts/goal/items/sp-response-2/result.json) and [sources.json](artifacts/goal/items/sp-response-2/sources.json).\n\nScores: Starter 0.98617; built-ins 0.995; current entry 0.5785; exploiter 0.395.\n\nAll retained chain links were beaten on fresh tests. Keep this for cross-play; replacing the current upload is not yet justified.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":13533568,"inputTokens":197716,"model":null,"outputTokens":46097,"runtime":"codex","turns":31,"wallClockMs":6556759}},{"artifacts":[{"bytes":53564,"hash":"0e79a8c7b45ef329da7266f7032c8be8c8716da697f97b8eaecee8c6ef719bba","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-response-3/result.json"},{"bytes":1981,"hash":"82a8a7d1e6433fa32f7477b99e4242de4e84c05324510a9cd57b3de8b5ca7cab","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-response-3/sources.json"}],"attempt":1,"bundleHash":null,"device":"5e7415761e1c9515","findings":[],"hash":"823cd71c3b994906e8b39d5caea1c71d354c3247feadebf69479f941c76c1fc6","nodeId":"00744b5f-8a35-4ba3-a234-ab1f6237c2a9","outcome":"completed","summary":"Created [result.json](artifacts/goal/items/sp-response-3/result.json) and [sources.json](artifacts/goal/items/sp-response-3/sources.json). Local checks passed; result is 53,564 bytes.\n\nFinal scores: Starter **0.98217**, built-ins **1.0**, current **0.6185**, exploiter **0.058**. The model remains highly exploitable; upload is not recommended.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":9277824,"inputTokens":97884,"model":null,"outputTokens":58035,"runtime":"codex","turns":62,"wallClockMs":5880157}},{"artifacts":[{"bytes":38957,"hash":"bc6fc0805bec2bb996fcd957a6b3f53109f2ef1922ceb058506acd7eb07c0ef0","mediaType":"application/json","name":"result","path":"artifacts/goal/items/rule-constants/result.json"},{"bytes":951,"hash":"f1348d8c3b43708bf27fc14d888bc6a7a3a8166f243f061ae3b35250d7b1dc76","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/rule-constants/sources.json"}],"attempt":1,"bundleHash":null,"device":"4f8047a54bdc27c8","findings":[],"hash":"83d21b2e2c6d7f0ee235a31cb6ad1734e25a0ad925e7d66c5c4fd2728296c958","nodeId":"1405cd54-e37c-40e4-9787-7724da790894","outcome":"completed","summary":"Created [result.json](artifacts/goal/items/rule-constants/result.json) and [sources.json](artifacts/goal/items/rule-constants/sources.json). All validation checks passed.\n\nLocal scores: Starter **0.9963333333333333**, built-ins **1.0**, current **0.72**, exploiter **0.293**.\n\nThe new exploiter remains a weakness; its reconstruction parameters are included.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":12223104,"inputTokens":96666,"model":null,"outputTokens":119160,"runtime":"codex","turns":59,"wallClockMs":6152965}},{"artifacts":[{"bytes":56124,"hash":"f37981b9aba64c0ee3f8c540d68161bea78d3abf33f1be05a6d1adc9459ef4b8","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-gradient-2/result.json"},{"bytes":1618,"hash":"bab60681c308bc7b1bf968c81a3f8347c34c2ea0c0143336217c49b7373a0ffb","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-gradient-2/sources.json"}],"attempt":1,"bundleHash":null,"device":"5d0460745287d96a","findings":[],"hash":"84c9499ae7960c9dd26ca7aa70ed2a9de7bee0cd73dc06b57818d92cb302ac66","nodeId":"c6be5073-1be5-46ee-8762-668dbae7df6e","outcome":"completed","summary":"Created [result.json](artifacts/goal/items/sp-gradient-2/result.json) and [sources.json](artifacts/goal/items/sp-gradient-2/sources.json).\n\nScores: Starter **0.998**, built-ins **1.0**, current **0.713**, exploiter **0.43**. The fresh exploiter remains a weakness.\n\nJSON, size, hash, ONNX operators, and source quotes verified.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":13201408,"inputTokens":112266,"model":null,"outputTokens":107452,"runtime":"codex","turns":57,"wallClockMs":5415673}},{"artifacts":[{"bytes":28719,"hash":"0fbf0c13d950d06b74a0c37e8bff30cd012dabae154db6badaef4fca3641cd8c","mediaType":"application/json","name":"result","path":"artifacts/goal/items/rule-energy/result.json"},{"bytes":370,"hash":"9e8806b4efc1f57b78a41f11cd3209871d685c0fa750ee47ee4ba921db0fe752","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/rule-energy/sources.json"}],"attempt":1,"bundleHash":null,"device":"4e35c66a4f3087ab","findings":[],"hash":"88db760a7e201790f4bceee510fd4822c0aca9a86f24b158867102970e6a5c9e","nodeId":"f1f565c3-db23-45af-95bb-9094657ed1cd","outcome":"completed","summary":"I found nothing better than the current entry, so the result file submits \"IMD seat 798\" unchanged. Every speed change I tried scored lower than the model's own throttle.\n\n**Speed-and-turn work.** From the simulator's flight model:\n- Turn rate is 4 − 3.2·(v−20)/230 rad/s, so slower flight always turns tighter.\n- Turning bleeds 0.25·|turn|·speed, drag is 0.9·speed, and gravity is 130·sin(heading).\n- The top level speed at full throttle is 200 m/s.\n\nI wrapped the current model with a throttle controller that holds a target speed for each situation: closing, turning fight, and opponent nose-on behind. I tested it against Starter, Starter held at full throttle, Starter held at 0.3 throttle, and the current model, on 80–120 matches per variant:\n\n| Speeds (closing / turning / behind, m/s) | Score |\n|---|---|\n| Current model's own throttle | 0.875 |\n| 100 / 50 / 150 | 0.869 |\n| 120 / 60 / 60 | 0.853 |\n| 100 / 50 / 150, half-blended with the model's throttle | 0.842 |\n| 60 / 45 / 100 | 0.831 |\n| 150 / 60 / 120 | 0.809 |\n| 66 / 42 / 42 | 0.80 |\n| Slow, but 150 when the opponent is behind | 0.75 |\n\nThese small sample sizes mean differences under about 0.05 are noise, but nothing came out ahead.\n\n**The four records, all for the unchanged entry:**\n\n| Record | Matches | Score |\n|---|---|---|\n| Against Starter (also `current_vs_starter`) | 3,000 | 0.98833 (2,965 wins, 35 losses) |\n| Four built-ins | 800 | 1.0 |\n| Against itself, half from each side | 1,000 | 0.5 (404 wins, 404 losses, 192 draws) |\n| Exploiter stand-in | 500 | 0.838 |\n\n- **Starter check:** the previous seat measured 0.99333 on different matches.\n- **No trained exploiter:** this machine is limited to one CPU core, which gave about 4–6 matches a second, so I couldn't train a neural opponent in ten minutes. The stand-in is the current model forced to full throttle, and it won 81 of the 500 matches. The result file says so.\n- **Build workaround:** the machine has no C compiler. I used Zig's `zig cc` as the linker, pointed it at the system's C++ library, and took the OpenSSL headers from the Ubuntu archive.\n\nThe \"next\" note tells the next seat to leave the throttle alone and work on aim and on breaking mutual kills.\n\nThe model is 18,930 bytes and the whole result is 28,719 bytes. It uses only the current entry's operators. `sources.json` has two quotes, and I checked that both appear at their URLs. I wrote nothing else in scope; the untracked dotfiles in the workspace root were already there and I didn't touch them.\n\nFiles are in `artifacts/goal/items/rule-energy/`:\n- result.json\n- sources.json","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":1969047,"cost":{"source":"runtime","usdMicros":1386467},"inputTokens":81,"model":"claude-opus-5-5","outputTokens":22904,"runtime":"claude","turns":49,"wallClockMs":2710708}},{"artifacts":[{"bytes":33091,"hash":"fcfad29144f38b3d0072ed8ff8e9b530292f1873699e3889bcfcc623ab35b1ba","mediaType":"application/json","name":"result","path":"artifacts/goal/items/rule-losses/result.json"},{"bytes":1160,"hash":"2e4e3bfb66dcd9dfd5bdc9b86361b0791a736509eb96f327dd912105c685e928","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/rule-losses/sources.json"}],"attempt":2,"bundleHash":null,"device":"61b4050710026370","findings":[],"hash":"9da5c3be0ba39218fb248550b611176207a2b42e17fa28a05dcf217346904e7a","nodeId":"c2892870-9079-4835-ab70-cec132755e29","outcome":"completed","summary":"I've written the result and sources files. The final model is a retuned version of the current rule. It does better against Starter, the built-ins and the current entry, but the direct fixes for the two named problems both failed, and the retune made it weaker against neural opponents. Because the board's opponents are networks, I don't think it should be uploaded on these numbers alone.\n\n**Harness check:** the current model scores 0.991 against Starter over 3,000 random-spawn matches (`current_vs_starter`); its seat measured 0.99333. Starter is rebuilt in Rust and the rule graph is translated to Rust; both match ONNX Runtime on 3,738 observations (to within 3e-6, with no shoot-decision mismatches). The shipped model was checked the same way.\n\n**What the losses share, and what didn't fix them:**\n- **Losses to Starter:** 24 of the 27 losses came right after the rule stalled, almost all in the last exchange with 1 HP each. Its stall guard can never trigger (it checks for a speed below the sim's 20 m/s minimum). But a working guard scored worse in every setting, at best 0.985 against Starter, because the rule stalls in 44% of its matches and mostly wins those.\n- **Mutual kills:** in matches against itself, about 22–24% end with both planes dying on the same tick. Both guns reload in step and hit at the same moment, so shooting earlier made no difference. Both changes were dropped as noise.\n\n**What worked:** three rounds of retuning the rule's 34 constants against the current entry, exploiters trained against each round's best version, and Starter. On fresh seeds:\n\n| Record | Matches | Score |\n|---|---|---|\n| Starter | 3,000 | 0.99867 |\n| Built-ins | 800 | 0.999375 |\n| Current entry | 1,000 | 0.5645 (521 wins, 392 losses, 87 draws) |\n| Exploiter | 500 | 0.212 |\n\nThe exploiter record comes from evolving the current rule's constants against my final model for 120 s. Mutual kills against the current entry fell to 8.7%, from 22–24% when the current rule plays itself.\n\n**Problems you should know about:**\n- **Self-retunes:** the current entry is badly exposed to retuned copies of itself. One I trained in 90 s held it to 0.163. My model is also beaten by such a copy (the 0.212 above).\n- **Neural regression:** a 60-second neural exploiter brings my model down to about 0.76–0.78. Two similar exploiters (50 s each) trained against the current rule left it at 0.965 or better. A fourth round meant to fix this was rejected: it fell against the current entry (0.29 and 0.37).\n\nThe `next` field recommends putting neural and self-play opponents in the tuning pool from the first round.\n\nThe six source quotes match the repository files and the entry JSON word for word, and the model uses only operators the current entry uses. The working files stay under `.cache/`, as the brief asked.\n\nFiles are in `artifacts/goal/items/rule-losses`:\n- result.json\n- sources.json","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":2530956,"cost":{"source":"runtime","usdMicros":4166278},"inputTokens":44,"model":"claude-opus-5-5","outputTokens":109096,"runtime":"claude","turns":36,"wallClockMs":2551284}},{"artifacts":[],"attempt":1,"bundleHash":null,"device":"9ea7ad07a55cf76f","findings":[],"hash":"a8ee9193738c7ae8143926e6cb20a322ded72c67d7d408052adaf28a20188089","nodeId":"f7d31210-b90e-48b3-bfa5-3f3f79ca01f9","outcome":"completed","summary":"No substantiated defects found. Figures and rankings match both cited summaries; model execution was not independently verified.\n\nSaved `{\"findings\":[]}` to [.imd-findings.json](/home/imd/.identitymd/work/d94acc27-198d-4d50-b5c6-5cdd0575cf7a/f7d31210-b90e-48b3-bfa5-3f3f79ca01f9/.imd-findings.json).","treeHash":null,"usage":{"cachedInputTokens":447744,"cost":{"source":"estimated","usdMicros":1545014},"inputTokens":67192,"model":"gpt-6-astra","outputTokens":8507,"runtime":"codex","turns":4,"wallClockMs":302801}},{"artifacts":[{"bytes":26596,"hash":"f2a9d74beb9515b380c0ec438c6d13266d9c4545a10dd8ad31e4487f0355b889","mediaType":"application/json","name":"result","path":"artifacts/goal/items/size-and-ops/result.json"},{"bytes":342,"hash":"dec0ade2eac57b84f1fc9028750cba95d23ad51a1e6b209999549cdd80d14d47","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/size-and-ops/sources.json"}],"attempt":1,"bundleHash":null,"device":"4d6b0e34f0d3e310","findings":[],"hash":"b0c6aeb7ebc225e8c11655076e655b4ddafe8ec8655a8a205d539ff6b4ce3c2f","nodeId":"27a9eb4f-ea4c-474c-b10f-3861cf21ec82","outcome":"completed","summary":"Completed the deliverables:\n\n- [result.json]( /home/seat/.identitymd/work/d94acc27-198d-4d50-b5c6-5cdd0575cf7a/27a9eb4f-ea4c-474c-b10f-3861cf21ec82/artifacts/goal/items/size-and-ops/result.json )\n- [sources.json]( /home/seat/.identitymd/work/d94acc27-198d-4d50-b5c6-5cdd0575cf7a/27a9eb4f-ea4c-474c-b10f-3861cf21ec82/artifacts/goal/items/size-and-ops/sources.json )\n\nThe result is valid JSON, 26,596 bytes, and preserves the current model unchanged. Runtime measurements are marked blocked due to the container’s linker crash.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":948608,"inputTokens":57104,"model":null,"outputTokens":10367,"runtime":"codex","turns":5,"wallClockMs":282503}},{"artifacts":[{"bytes":49266,"hash":"dbd09aa47a4f92ea5806e279ef31596f3e9f1d56ce6a7a6729a5eda89c193b9f","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-response-1/result.json"},{"bytes":344,"hash":"c5debaf00545c798746f5c29fa91b09914edeb1ec4b9f43bc3931edaee13e381","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-response-1/sources.json"}],"attempt":1,"bundleHash":null,"device":"5ded77c3c883b7b0","findings":[],"hash":"b24dc16f3de91a9b227971f6727950a169cf305e6e8362254ae86dc8903a95ff","nodeId":"1f82f955-012b-4255-a7a3-04e7213b44c9","outcome":"completed","summary":"Both output files are written and parse, but the chain's final model is weak and shouldn't be uploaded.\n\n**Harness check:** the current entry scored **0.991** against Starter over 3,000 random-spawn matches (2,973 kills, 27 losses). Its seat measured 0.99333, so the harness agrees closely.\n\n**Final model's four records:**\n\n| Opponent | Matches | Score |\n|---|---|---|\n| Starter | 3,000 | 0.594 |\n| Four built-ins | 800 | 0.119 |\n| Current entry | 1,000 | 0.0 (lost all 1,000) |\n| Exploiter | 500 | 0.01 |\n\n**What happened:**\n- I first copied the current hand-built rule into a small network (224-32-32-3, 8,355 parameters). The best copy beat Starter 0.975 but scored only 0.08 against the rule itself.\n- **Link 1:** 7 minutes of evolution-strategy training against the rule found nothing. It went from 0.08 to a best of 0.15 on matches it didn't train on, with no upward trend.\n- **Links 2 and 3:** each easily beat the link before it (0.92 and 0.905). They did it by exploiting each other, not by flying better.\n- **Final model:** trained against the rule, all three links and Starter, it reached 0.544 on held-out matches. It lost the rule's skill along the way.\n\nThe exploiter shows the drift: its starting copy already beat the final model 0.98 before any training.\n\n**Deviations from the brief:**\n- Total training was about 40 minutes, not the 90 asked for, because building the setup took time.\n- The exploiter trained for 4 minutes instead of 10. I note this in the result's \"how\" field.\n\n**Setup notes:**\n- This machine has no C compiler or linker, so I used zig (installed from pip) as the linker.\n- The simulator is the repository's own engine running in a loop I wrote.\n- ONNX opponents run through the onnxruntime 1.31.1 library from pip, so the OpenSSL step in the old setup wasn't needed.\n- The exported model uses only Gemm and Relu, both Starter operators. It is 33,747 bytes and gives the same results as the training copy.\n- All working files are in `.cache/`. The untracked dotfiles in the repository root (`.bashrc`, `.gitmodules` and others) are empty placeholders from the sandbox, dated before this run; I didn't create them.\n\nThe result's \"next\" field tells the next seat to keep the rule in every link's opponent pool and require each link to stay at 0.95 or better against Starter and the built-ins. It also suggests far more compute for the first link, and porting the rule to Rust for training, since it only ran at about 77 matches a second through ONNX here.\n\n`sources.json` cites the current entry's JSON and the repository's `constants.rs`; I checked both quotes against the live pages.\n\nFiles are in `artifacts/goal/items/sp-response-1/`:\n- result.json (49,266 bytes)\n- sources.json","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":1682759,"cost":{"source":"runtime","usdMicros":1566697},"inputTokens":65,"model":"claude-opus-5-5","outputTokens":34282,"runtime":"claude","turns":42,"wallClockMs":2522412}},{"artifacts":[{"bytes":53774,"hash":"0efe4742924f32c4cee62f2a234fce619d2b978dc7143bed3c636e6225412749","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sparring-head/result.json"},{"bytes":601,"hash":"c1bdf88cee0ad18f9fa67e13ea43b466119231424d9ad5cd7f0556f28945a433","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sparring-head/sources.json"}],"attempt":1,"bundleHash":null,"device":"4be5e039e1c77ce3","findings":[],"hash":"b653ddb99cf2defcd22a54d15261c67c7d28e61acf23e30a02724fbf50966776","nodeId":"e6fdbb9e-9b8b-46f6-bbc6-1387c44e8325","outcome":"completed","summary":"Both files are written and checked: `result.json` is 53,774 bytes and `sources.json` is 601. The entry is an evolved opponent that beats the current model 0.828, but it isn't a stronger entry overall, and I don't recommend uploading it. It lost 72 of 3,000 to Starter where the current model lost 26 on the same seeds, and third place allows no loss in 60.\n\nTotal time was about 45 minutes. My `current_vs_starter` check gave 0.9895, then 0.993 and 0.9913 on other seed sets, which matches the seat's 0.9933 within noise.\n\n| Record (my model's side) | Matches | Score |\n|---|---|---|\n| Starter | 3,000 | 0.9745 |\n| Four built-ins | 800 | 0.99875 |\n| Current model | 1,000 | 0.828 (810 wins, 154 losses, 36 draws) |\n| 10-minute exploiter | 500 | 0.269 |\n\n**What the entry is:** the current rule's graph plus a small evolved residual network, 35,530 bytes. It passes the repo's validator and uses no new ONNX operators.\n\n**Departure from the brief:** evolving Starter's head, as asked, never produced a winner:\n- **Result:** it scored 0.478 against the current model, with 954 draws in 1,000, by staying about 430 m away, beyond bullet range.\n- **Compute:** that run had only one of the four cores for about 18 minutes.\n- **Why the brief's 27% didn't repeat:** that figure was measured against an earlier rule, n7, not the current entry, which was hardened afterwards.\n- **What I did instead:** I started the residual from zero on top of the rule, so training began at an even match. That reached about 0.8 within minutes.\n- **Not purely against the current model:** the last 520 s of evolution also included Starter and the evader, and `result.json` says so.\n\n**How it wins:** it fires when the rule would fire; only the flying changes. It uses more throttle early (0.65 vs 0.35 in the first 3 seconds), closes fast, then slows inside about 90 m. That keeps it out of the head-on exchange, and the rule's hit rate against it drops from 82% to 69%.\n\n**Worth reusing:** the method is the useful part. A 16 KB residual took a hardened model from 0.5 to about 0.8 in minutes. The exploiter it produced could be a sparring partner in later rounds, though it isn't saved in the result files; `next` in `result.json` sets out how to rebuild it.\n\n**Stray folder to delete:** a failed `cd` put my clone in the workspace root, and I moved it into `.cache/`. An empty `repo/` folder remains in the root, holding only a `.git` with `config` and `hooks` that the sandbox won't let me delete. `git status` no longer lists it, and an earlier dry run on `repo/` staged nothing, so it shouldn't enter the submission. Please delete it if the workspace gets reused.\n\nFiles are in `artifacts/goal/items/sparring-head/`:\n- `result.json`\n- `sources.json`","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":3160205,"cost":{"source":"runtime","usdMicros":3053193},"inputTokens":60,"model":"claude-opus-5-5","outputTokens":59339,"runtime":"claude","turns":41,"wallClockMs":2704299}},{"artifacts":[{"bytes":33165,"hash":"a1d3b27115c57cdcaf3fa10562e219441d09c5bbbaed62e270b02f57b5a110de","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-league-2/result.json"},{"bytes":642,"hash":"84490d50a07bce1d4793813ade0fe04822f0d2eb23f740b5ed5c8efd3bc3bda1","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-league-2/sources.json"}],"attempt":1,"bundleHash":null,"device":"540f6378761dba26","findings":[],"hash":"b9be30b7dc2de81d8a2ece52869cf918e2dd8f40445cf26f442dbff1a350abdd","nodeId":"d219427f-7fe3-4bfd-adaf-1060d737dbd7","outcome":"completed","summary":"Created and validated [result.json](artifacts/goal/items/sp-league-2/result.json) and [sources.json](artifacts/goal/items/sp-league-2/sources.json).\n\nScores: Starter **1.0**, built-ins **0.99125**, current entry **0.668**, exploiter **0.129**.\n\n**Not recommended for upload yet:** the current entry scored **0.81** against that same exploiter. Reproduction parameters are included.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":10302080,"inputTokens":112275,"model":null,"outputTokens":100436,"runtime":"codex","turns":38,"wallClockMs":5618386}},{"artifacts":[{"bytes":41891,"hash":"184768c1178b835b7b424d649758a0fcaa7c53bd63a03eaed5c4c1f9d5a30c48","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-starter-3/result.json"},{"bytes":676,"hash":"418646e9165a60c8ddb8c317e5b8c7494c508362d88ee46b966a19a250fb9f35","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-starter-3/sources.json"}],"attempt":1,"bundleHash":null,"device":"505a745a77612e11","findings":[],"hash":"bdbdd6c2bd045138823747e1b9adec1d412445d6ea2a3e16ee6e5667f90171f5","nodeId":"91b13151-0d93-406a-bffc-2940d1599a5c","outcome":"completed","summary":"I trained a self-play network from Starter (run 3, seed 3) and wrote the result. **It is much weaker than the current entry and should not be uploaded:** the current rule beats it in 993 of 1,000 matches, and an opponent trained against it for 10 minutes beats it in 496 of 500.\n\n**Step 1, harness check.** The current model scores **0.9926666666666667** against Starter over 3,000 random-spawn matches (2,978 wins, 22 losses). Its seat measured 0.99333, so the harness agrees.\n\n**Final model.** It is a small network (two hidden layers of 48) stored at half precision: 27,117 bytes, using only the Cast, Gemm and Relu operators. Its four records, all on seeds it never trained on:\n\n| Opponent | Matches | Score |\n|---|---|---|\n| Starter | 3,000 | 0.9570833333333333 |\n| Four built-ins | 800 | 0.924375 (weakest: dogfighter 0.79) |\n| Current model, half from each side | 1,000 | 0.0065 (6 wins, 993 losses, 1 draw) |\n| Exploiter trained for 9.5 minutes | 500 | 0.008 |\n\nThe exploiter itself scores only 0.018 against Starter, so it is a narrow specialist. Even so, the final model is easy to beat.\n\n**How it went:**\n- **Imitating Starter:** the copied network scored only 0.20 against Starter.\n- **First self-play phase:** within 8.5 minutes it reached 0.979 against Starter, but still scored 0 against the current rule.\n- **Training against the rule alone:** this was the only thing that dented it (0.17), but it fell to 0.78 against Starter and 0.12 against the built-ins.\n- **Balanced league afterwards:** it recovered Starter and the built-ins, but the gain against the rule disappeared.\n- **Picking the final model:** results were circular (one candidate beat a second 0.73, the second edged a third, and the third beat the first 0.55), so I picked the model with the best average over held-out opponents.\n- **Shrinking to half precision:** lost nothing (0.954 before, 0.957 after, same seeds). The exported file gives exactly the same outputs as my simulator's copy of the network.\n\n**Setup notes:**\n- This machine has no C compiler, so the build the current entry's \"setup\" describes could not be done as written. I used zig (installed with pip) as the linker instead.\n- Starter and the current rule run natively in Rust instead of through ONNX Runtime. The rule was converted from its ONNX graph and gives identical outputs; Starter is within 2.4e-6. This ran about 3,000 matches a second.\n- `entry_sha256` is the hash of the base64 text, the same convention as the current entry's file.\n\n**For the next seat (\"next\" in the result):** start from a network trained to copy the current rule, not Starter. Add each exploiter to the league as soon as it is found, and pick the final model by a round robin on fresh seeds.\n\nThe sources file quotes the repository's RULES.md and the current entry; I checked each quote against a fresh download.\n\nFiles are in `artifacts/goal/items/sp-starter-3/`:\n- `result.json` (41,891 bytes)\n- `sources.json` (4 quotes)","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":2482894,"cost":{"source":"runtime","usdMicros":2211725},"inputTokens":75,"model":"claude-opus-5-5","outputTokens":48759,"runtime":"claude","turns":46,"wallClockMs":3634017}},{"artifacts":[{"bytes":51338,"hash":"096a822c3fa30b8ffe5e8bc6cb12ceb6aa90ede0971ce35e5c36027ac61b6ada","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-distill-1/result.json"},{"bytes":658,"hash":"65b0a0073d35bde65e505675f86ec196fc57092c5d09eb7384b811cc370621fa","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-distill-1/sources.json"}],"attempt":1,"bundleHash":null,"device":"5231314e8cca884c","findings":[],"hash":"be615a7622dbfdc4544708993fd77b5a7ccddf72e87df31434dfd1c560066f03","nodeId":"868336e0-ae01-4cfc-997b-7d780fe12e44","outcome":"completed","summary":"Saved [result.json](artifacts/goal/items/sp-distill-1/result.json) and [sources.json](artifacts/goal/items/sp-distill-1/sources.json).\n\nFinal scores: Starter **0.99375**, built-ins **0.99875**, current **0.8195**, exploiter **0.254**.\n\nValidation passed. The model remains exploitable; top-three performance is unverified.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":20902272,"inputTokens":149386,"model":null,"outputTokens":142858,"runtime":"codex","turns":37,"wallClockMs":5647178}},{"artifacts":[{"bytes":56486,"hash":"7f74c609733fa9e42a26058e14d688897b604b8e05aa321a7e2b2bf50ad354dd","mediaType":"application/json","name":"result","path":"artifacts/goal/items/rule-defence/result.json"},{"bytes":1204,"hash":"f8046640ccec30797aa445c59e8db29647bc1347783b84bf5223bba3e715b4c4","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/rule-defence/sources.json"}],"attempt":1,"bundleHash":null,"device":"4eb5b0156b09157c","findings":[],"hash":"bf4cff430dab7a4a839960b344c250ad274ce98c6fa3b2579478350d5db306cb","nodeId":"7dd5972c-6f08-471a-8800-1c0b3028cf7b","outcome":"completed","summary":"Created [result.json](artifacts/goal/items/rule-defence/result.json) and [sources.json](artifacts/goal/items/rule-defence/sources.json). JSON, ONNX, size, hash, operators, and source quotes passed checks.\n\nScores: Starter **0.9975**, built-ins **1.0**, current **0.7775**, exploiter **0.273**.\n\nThe exploiter remains a significant weakness; direct upload is not recommended.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":14903424,"inputTokens":115373,"model":null,"outputTokens":126383,"runtime":"codex","turns":38,"wallClockMs":5478869}},{"artifacts":[{"bytes":41652,"hash":"1e8d13c6f388bbe765748eef56f431a255a2c9b2f2a156a7934b956af94ec97b","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-distill-3/result.json"},{"bytes":759,"hash":"b16f1ffbf92971cfa7d569813f3477cabf62497c57ca35184f36b60927c59372","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-distill-3/sources.json"}],"attempt":1,"bundleHash":null,"device":"523ef565dd740e25","findings":[],"hash":"c5b8841c5c3d6d0c65637b3027fecb1cc37dcdf0097e68090db89d14939c7547","nodeId":"5e44943c-b624-44b3-a09d-15e2e2d7479e","outcome":"completed","summary":"Done. The deliverable is written and validated, but the headline finding is negative: the distilled network did not reach the current rule's strength, so it must not replace the board entry.\n\n**What was built**\n\n- The simulator was built under .cache with rustup Rust plus a gcc-14, binutils and libssl-dev sysroot extracted from Ubuntu debs, since this machine had no C compiler. The repo's own validator passes the final model (Cast, Gemm, Relu; 13,299 parameters; 27,120 bytes).\n- A Rust harness over SimState runs random-spawn matches on 6 threads, records teacher decisions, labels student states for DAgger, and runs evolution strategies on MLP weights.\n- The model is a 224-48-48-3 ReLU MLP with fp16 weights, trained by behaviour cloning of the current ONNX rule on 322k decisions, then two DAgger rounds with 470k more labels. Seed 3 and middle-of-the-road choices throughout.\n\n**Records, all on fresh seeds**\n\n| Record | Matches | Score |\n|---|---|---|\n| Harness check, current rule vs Starter | 3000 | 0.9927 (its seat: 0.9933) |\n| Final vs Starter | 3000 | 0.9512 |\n| Final vs four built-ins | 800 | 0.886 |\n| Final vs current rule | 1000 | 0.1665 (98 wins, 765 losses, 137 draws) |\n| Final vs 7-minute ES exploiter | 500 | 0.068 |\n\n**What failed and why.** The clone agrees with the rule on 98.5 percent of shoot decisions, but the rare wrong shots compound into lost fights against the rule itself. A 10-minute evolution-strategies run against a mixed pool was rejected: it pushed the built-in score to 1.0 but dropped the score against the rule to about 0.05, because easy opponents dominated the reward. The exploiter run shows the same ES recipe finds a strong counter to the clone within minutes.\n\n**Files written.** result.json (41,652 bytes) and sources.json under artifacts/goal/items/sp-distill-3. The \"next\" field tells the following seat to use a current-frame-only input with a wider shoot head, treat yaw as three-way classification, run many more DAgger rounds against the rule, and only then fine-tune with the rule as at least half the pool. All working files stayed in .cache.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":2912359,"cost":{"source":"runtime","usdMicros":5551749},"inputTokens":3829,"model":"claude-fable-5-1","outputTokens":50537,"runtime":"claude","turns":56,"wallClockMs":2301968}},{"artifacts":[{"bytes":30466,"hash":"fe71a20a0bdc34a328682940dfcf77798b8c806c8b237ebb60e3526375b77ec5","mediaType":"application/json","name":"result","path":"artifacts/goal/items/rule-unreadable/result.json"},{"bytes":210,"hash":"1ec3515c9ec1b5de6a8f875a9b1ff7d66be320d3ee244ca5566fb3bdf519f1f4","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/rule-unreadable/sources.json"}],"attempt":1,"bundleHash":null,"device":"4faf975f1178e1f8","findings":[],"hash":"d5c76559f4d107fce222d28143056b53c7ca504853d9d385f13b01df90075cab","nodeId":"6a41f5f9-4509-45c1-be1d-1a93b7590ac5","outcome":"completed","summary":"I wrote both files, but the styled model is weaker than the current entry head to head, so I don't recommend uploading it. Both files parse as JSON. `result.json` is 30,466 bytes, the model is 19,997 bytes, and the repo validator passes it.\n\nI took the current entry's rule graph unchanged and added a style mixer on top. A chaotic hash of the opponent's relative position and speed, plus own altitude and time left, picks a style at each 12 Hz decision. 80% of decisions use the base rule. 10% use a \"turning fight\" (yaw ×0.93) and 10% a \"patient defence\" (throttle ×0.95). It uses only operators the current entry already has.\n\n| Record | Result |\n|---|---|\n| Current entry vs Starter, 3,000 matches (`current_vs_starter`) | 0.9895 (the previous seat measured 0.9933) |\n| Styled model vs Starter, 3,000 matches | 0.9867 |\n| Styled model vs the four built-ins, 800 matches | 1.0 |\n| Styled model vs current entry, 1,000 matches, half from each side | 0.4555 (369 wins, 458 losses, 173 draws) |\n| Current entry vs itself, 1,000 matches | 0.500 |\n| Styled model vs my exploiter, 500 matches | 0.998 |\n\n- **Cost of styles:** mixing styles cost about 4.5 points head to head. Cruder variants did worse; one that added throttle noise scored 0.30 against the current entry.\n- **Exploiter result:** the question you asked, whether styles make the rule harder to exploit, is not answered. The exploiter learned nothing against either model: it won 1 of 500 against the styled model and 2 of 500 against the current entry.\n- **Why the exploiter was weak:** the rule graph runs at about 4 to 15 matches a second here, against the 770 the previous seat saw. The exploiter got 25 generations in 420 seconds, not the ten minutes asked for, because the tool caps a command at 10 minutes.\n- **Setup:** the machine had no C compiler. I unpacked gcc, binutils, libc and OpenSSL headers under `.cache/` and built the simulator there.\n- **Next step, as written in `result.json`:** build a native or batched copy of the rule so an exploiter gets thousands of matches a second, then compare the generations an exploiter needs to reach 20% against the plain and the mixed rule. Build the styles inside the rule, not by scaling its outputs.\n\n`sources.json` lists one source, the current entry's public JSON file.\n\nFiles are in `artifacts/goal/items/rule-unreadable/`:\n- `result.json`\n- `sources.json`","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":2006617,"cost":{"source":"runtime","usdMicros":811983},"inputTokens":83,"model":"claude-sonnet-5-5","outputTokens":34475,"runtime":"claude","turns":46,"wallClockMs":5013084}},{"artifacts":[{"bytes":57588,"hash":"c305e434bc80da7a5b9cfc31e14030eaf9dc9067c747ed331d2a8bab478c6c7a","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-distill-5/result.json"},{"bytes":15,"hash":"73608e3346e331760ac29f17144976922e84678fed8a2eb7cc66f6ef0e89a33c","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-distill-5/sources.json"}],"attempt":1,"bundleHash":null,"device":"52c98c0dc01791cd","findings":[],"hash":"d68b9df505d71848b9d128f2b87efd08aa0a17a245108ac369cab5add50df428","nodeId":"1aa43a6e-afd9-4d18-acf6-210be209c3b0","outcome":"completed","summary":"I wrote `result.json` (57,588 bytes) and `sources.json` (empty, since I relied on no outside source). My network is not stronger than the current entry, so the operator should not upload it.\n\nIt is a plain 224-68-64-3 network with 19,911 parameters, 40,310 bytes, using only the Cast, Gemm and Relu operators. It passes the repo validator. All matches used random spawns and alternating sides, run through the repo's `run_match`.\n\n| Check | Matches | Score |\n|---|---|---|\n| Current model vs Starter (my harness) | 3,000 | 0.9913 (seat measured 0.9933) |\n| My model vs Starter | 3,000 | 0.9808 |\n| My model vs the four built-ins (chaser 0.965, dogfighter 1.0, ace 1.0, brawler 0.995) | 800 | 0.99 |\n| My model vs the current model | 1,000 | 0.419 (338 kills, 162 draws, 500 losses) |\n| My model vs my ten-minute exploiter | 500 | 0.079 (32 kills, 15 draws, 453 losses) |\n\n- **Cloning:** Copying the current rule did not work well. The copy scored about 0.93 against Starter and 0.08 against the current model. The yaw error stuck at about 0.18, and three DAgger rounds did not help.\n- **Evolution-strategy tuning:** Only evolution-strategy tuning against a pool of Starter, the built-ins and the current model lifted it to about 0.99 against Starter.\n- **Exploiter:** It started from my model's own weights and trained against the final model for 560 seconds. It beats the model badly, so the model is easy to exploit.\n\nThe result's `next` field suggests more capacity or precomputed geometry for the distillation, longer evolution-strategy runs with earlier clones in the pool, and not uploading this model.\n\nThe machine had no C compiler, so I unpacked gcc and OpenSSL headers into `.cache/` to build the simulator. Everything I made is under `.cache/`; the only files delivered are the two named outputs.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":2357755,"cost":{"source":"runtime","usdMicros":925904},"inputTokens":89,"model":"claude-sonnet-5-5","outputTokens":38256,"runtime":"claude","turns":50,"wallClockMs":4776830}},{"artifacts":[{"bytes":27352,"hash":"fac1751cd097737c20393fbb9d6a5d2d08e00829153d98a838684f2c10d11306","mediaType":"text/markdown","name":"merge","path":"artifacts/goal/merges/01/result.md"}],"attempt":1,"bundleHash":null,"device":"6ce0cec991d77172","findings":[],"hash":"d6c21b44e6ef07af798bf5e269318d9085622489fcba588337eabacb12e66c85","nodeId":"2453ccf7-92a8-4a3b-a038-eee9f4ca72de","outcome":"completed","summary":"Created [result.md](/home/worker/.identitymd-5/work/d94acc27-198d-4d50-b5c6-5cdd0575cf7a/2453ccf7-92a8-4a3b-a038-eee9f4ca72de/artifacts/goal/merges/01/result.md) with all 25 rows, cited findings, and limitations.\n\nChecked exact records, sizes, operators, and model exclusion. **27,352 bytes**, below the limit. Left untracked; no other files written.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":748928,"cost":{"source":"estimated","usdMicros":428892},"inputTokens":79763,"model":"gpt-6.1-sol","outputTokens":11958,"runtime":"codex","turns":5,"wallClockMs":334553}},{"artifacts":[],"attempt":1,"bundleHash":null,"device":"51908b9b0306f441","findings":[],"hash":"d741a778b70ed107a6f1a6be33257e90a0a41b583d06f4a0bfdd7e3322864d6e","nodeId":"b67f583f-487d-4d5c-90a6-d431076bc079","outcome":"failed","summary":"Selected model is at capacity. Please try a different model.","treeHash":null,"usage":{"cachedInputTokens":8991360,"inputTokens":347351,"model":null,"outputTokens":74824,"runtime":"codex","turns":33,"wallClockMs":3673255}},{"artifacts":[],"attempt":1,"bundleHash":null,"device":"5d667e4b0751bcb5","findings":[],"hash":"da70491e3b4cb6156f52ca1903adcddb62d51b4a6393b2b68fbf961a2073895c","nodeId":"24c172dc-6b74-4a82-b7f8-719aa820c196","outcome":"failed","summary":"Failed to authenticate: this login reached its time limit and cannot be renewed. Run `claude auth login` to sign in again, or use `claude setup-token` and set CLAUDE_CODE_OAUTH_TOKEN for unattended jobs","treeHash":null,"usage":{"cachedInputTokens":0,"inputTokens":0,"model":null,"outputTokens":0,"runtime":"claude","turns":1,"wallClockMs":1546}},{"artifacts":[{"bytes":61329,"hash":"927c5679885b8849448520dfbea601fb2ea82764781cb1982b5954d5e52c4c17","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-response-5/result.json"},{"bytes":657,"hash":"be57b2f4dc11229df9baec74f1e0b5a253f6bcc3664febfb7d7392f8d16dd14f","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-response-5/sources.json"}],"attempt":1,"bundleHash":null,"device":"5f419f6fdfb42f37","findings":[],"hash":"dae92171f3cf1fee3df930966e6ab73d973be2ad135ddcced8e9d2d5121a48e8","nodeId":"e1f32640-0c43-4a95-a604-3a4f8023183b","outcome":"completed","summary":"Created [result.json](artifacts/goal/items/sp-response-5/result.json) and [sources.json](artifacts/goal/items/sp-response-5/sources.json).\n\nScores: Starter **0.999**, built-ins **0.99875**, current **0.6625**, exploiter **0.147**.\n\nValidation passed. The candidate remains highly exploitable and needs further training before upload.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":20050304,"inputTokens":124047,"model":null,"outputTokens":162447,"runtime":"codex","turns":38,"wallClockMs":6696780}},{"artifacts":[{"bytes":2240,"hash":"23e51bf7893266445a6a2cf2cb4e9e966fafbf114f4fa8da29c1f22add771d02","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-response-4/result.json"},{"bytes":412,"hash":"b78120bf5ff9be716e30ddda9f77da6700e3f10c56fb041a74d4e7c1b0165f22","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-response-4/sources.json"}],"attempt":1,"bundleHash":null,"device":"5f24516e20c96aec","findings":[],"hash":"dc3f54fa6fad62ef2122c61b7509154780f7cb3949b86b9660f79a403e5d28b6","nodeId":"59917dc5-a5f5-4909-bff4-e612dfc36aea","outcome":"completed","summary":"Created and validated `result.json` and `sources.json`.\n\nBlocked: simulator compilation failed because Apple’s `xcrun` developer tool is missing. No training or matches ran; scores are null. Temporary files were removed, leaving only the two required outputs.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":300672,"inputTokens":36278,"model":null,"outputTokens":2456,"runtime":"codex","turns":4,"wallClockMs":88236}},{"artifacts":[{"bytes":29584,"hash":"02bbccd31804be999b71902786d70da0dff57e15bee06f8dd27c575303116fc4","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-league-5/result.json"},{"bytes":210,"hash":"1ec3515c9ec1b5de6a8f875a9b1ff7d66be320d3ee244ca5566fb3bdf519f1f4","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-league-5/sources.json"}],"attempt":1,"bundleHash":null,"device":"559cfaaab2c0d013","findings":[],"hash":"dce2e432243d665647efa3892373f0149f39dca2d599764c47cf5c11ca230f94","nodeId":"087634ce-2de9-45a6-a38c-8f4a570582af","outcome":"completed","summary":"I wrote `result.json` and `sources.json`. The submitted model beats the current entry head to head, but a network trained against it beats it. I never ran the exported ONNX file in ONNX Runtime.\n\n**What the model is.** It is the current entry's own ONNX graph with the 65 scalar constants retuned. The 18 operators and the wiring are unchanged. The file is 18,930 bytes and passes `onnx.checker`. I ran the graph through my own Rust re-implementation, and that is what produced the numbers below.\n\n**Records (random spawns, sides alternating):**\n\n| Opponent | Matches | Score |\n|---|---|---|\n| Current entry vs Starter (harness check) | 3,000 | 0.9895 (the seat measured 0.9933) |\n| Final model vs Starter | 3,000 | 0.99517 |\n| Final model vs the four built-ins | 800 | 1.0 |\n| Final model vs current entry | 1,000 | 0.7065 (639 wins, 226 losses, 135 draws) |\n| Final model vs an exploiter trained against it | 500 | 0.4005 |\n\n**Weakness.** The exploiter was a head-only network trained for 480 seconds (8 minutes, not 10, to fit the tool limit). The same exploiter leaves the current entry at 0.879. A 240-second exploiter trained against the current entry leaves it at 0.908 and the final model at 0.969. The exploiters had different budgets and targets, so this is not a like-for-like comparison. Since the board keeps the latest upload, the operator should weigh that before uploading.\n\n**How the league ran.** I ran three rounds. Each round trained an exploiter of the champion, a generalist network against the pool, and a retuned rule champion. The pool grew from 6 to 14 members. A new champion was accepted only if its worst case on held-out seeds did not get worse. Round 0 was a one-second accidental run of the round command. It passed the same gate, and I disclosed it in the result.\n\n**Gaps:**\n- This is fewer rounds than the method wants, in under 90 minutes of work.\n- Ten of the 14 pool members are network heads or rule champions I trained in this run, so a larger and more varied pool would be a stronger test.\n- The only source listed is the entry JSON, for the Starter-endpoint sentence. I quoted nothing from the Paradigm pages.\n- I wrote the result file with a script, not by hand. Its size is 29,584 bytes, under the 64,000 limit.\n\nFiles are in `artifacts/goal/items/sp-league-5/`:\n- `result.json`\n- `sources.json`","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":1718625,"cost":{"source":"runtime","usdMicros":811719},"inputTokens":65,"model":"claude-sonnet-5-5","outputTokens":36925,"runtime":"claude","turns":35,"wallClockMs":2374484}},{"artifacts":[{"bytes":35295,"hash":"fddf1ec52ebfa4b09a7603f65157a7bec258e186d8e06379c37ec6897d74c9ab","mediaType":"application/json","name":"result","path":"artifacts/goal/items/rule-mutual/result.json"},{"bytes":921,"hash":"a33fb8596e193fcc3940c05bbb24e2a3afd72a59eac97c37c325ffbe2c1f6886","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/rule-mutual/sources.json"}],"attempt":1,"bundleHash":null,"device":"4f64b8f2c68244fd","findings":[],"hash":"e278e99fe39c7c60a6ef9301736d0f26a3c76ba049d8c2471185c595ba536693","nodeId":"f7d56743-9635-48d0-8133-d850809dd22a","outcome":"completed","summary":"Created and validated [result.json](artifacts/goal/items/rule-mutual/result.json) and [sources.json](artifacts/goal/items/rule-mutual/sources.json).\n\nMirror mutual kills fell from 194 to 46 per 1,000 matches. The model scored only 0.06 against its strongest exploiter, so robustness remains unresolved. Nothing was uploaded.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":21718912,"inputTokens":148564,"model":null,"outputTokens":109733,"runtime":"codex","turns":45,"wallClockMs":5394200}},{"artifacts":[{"bytes":13001,"hash":"6a92030d0ddbea89bd3e2fd97af90db1a6a162452fb708bffac85d188ed46c41","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sparring-small/result.json"},{"bytes":1258,"hash":"c398000b778f9b5dc470c968c2297c41febb98ff1f2f3a334a12cf6ab9294a83","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sparring-small/sources.json"}],"attempt":1,"bundleHash":null,"device":"4d218e2d11a36af4","findings":[],"hash":"e7acac15f149d74d1ccc5f688135e1caabe5d7e29a6158b66de0c9f029f1d0b6","nodeId":"255387ad-efa9-4804-9f80-3217aad6018f","outcome":"completed","summary":"Created [result.json](artifacts/goal/items/sparring-small/result.json) and [sources.json](artifacts/goal/items/sparring-small/sources.json).\n\nModel scores 0.60375 against current, but 0 against its exploiter. Retain the current upload; use this model as a sparring opponent. All checks passed.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":11287680,"inputTokens":101495,"model":null,"outputTokens":77467,"runtime":"codex","turns":30,"wallClockMs":2285615}},{"artifacts":[{"bytes":62019,"hash":"f8647c4ae1208afee35946d40a7a45722732d1f624bac4e2222f4c1a53dba9ed","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-evolution-5/result.json"},{"bytes":1233,"hash":"a8ec2f7007c5727e145fdeb593c55b7393bb5cc7e5778d49b3366bf165119aaf","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-evolution-5/sources.json"}],"attempt":1,"bundleHash":null,"device":"5c0fdae3b22cd896","findings":[],"hash":"f33ec8738c406a2c9b51beb551b852fe7090b30a319d84d8cb62dbcb8e0628c6","nodeId":"9a407b50-8d99-47a5-a53c-2266c09683d6","outcome":"completed","summary":"Created and validated [result.json](artifacts/goal/items/sp-evolution-5/result.json) and [sources.json](artifacts/goal/items/sp-evolution-5/sources.json).\n\nThe result is 62,019 bytes. The candidate won 0 of 1,000 matches against the current entry; keep the live entry.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":20025728,"cost":{"source":"estimated","usdMicros":5176684},"inputTokens":117464,"model":"gpt-6.1-sol","outputTokens":93661,"runtime":"codex","turns":66,"wallClockMs":5653236}},{"artifacts":[{"bytes":41948,"hash":"be48d5353b1ead6ddbfb4f53a67706a03037630f5f965dc31ab178b4685ea272","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-curriculum-1/result.json"},{"bytes":365,"hash":"0dd483d5d11efccbe219939c404353f3feb62af28389dafb9520da61b17b3530","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-curriculum-1/sources.json"}],"attempt":1,"bundleHash":null,"device":"5620e37a43b11630","findings":[],"hash":"f6237b94d96575a5e7f6eed4dcf6268e1672143584cbe7ba48e02511e3f7ca61","nodeId":"4c741e0e-7b30-4fd0-abd6-c796f09c5220","outcome":"completed","summary":"I wrote the result and sources files, but the model I trained is much weaker than the current entry and should not be uploaded. Training stopped at the first curriculum stage (the four built-in opponents) and never reached the 95% gate needed to move on.\n\n**What limited it.** I built the simulator and harness in `.cache/`, but the sandbox gave me one effective CPU core even though the machine reports 16. That meant about 650 matches a second against the built-ins, about 60 against Starter and about 8 against the current rule. Once the model learned to survive, matches ran to the 90-second timeout and each training generation slowed to about 10 seconds. The machine also had no C compiler and no OpenSSL headers, so I used Zig from pip as the compiler and unpacked the OpenSSL headers from the Ubuntu archive.\n\n**Harness check.** The current entry scored 0.993 against Starter over 3,000 matches (2,979 kills, 21 losses). Its seat reported 0.9933, so the harness agrees.\n\n**Final model.** A small neural network (224 → 32 → 3, 29,421 bytes, operators Gemm and Relu, both used by Starter). It was trained from random weights by an evolution strategy, seeded with 1, against the built-ins only.\n\n| Record | Matches | Score |\n|---|---|---|\n| Starter | 3,000 | 0.238 |\n| Built-ins | 800 | 0.6934 (mostly wins on hit points, not kills) |\n| Current entry | 1,000 | 0.001 (1 win, 999 losses) |\n| Exploiter | 500 | 0.5 (all draws) |\n\nThe exploiter is weak, and the result says so. I trained it for 200 seconds, not the ten minutes the brief asks for, because of the time left. The current rule is the stronger opponent in practice.\n\nThe result's \"next\" field tells the next seat to get more than one core or move the opponents into Rust. It also suggests starting from Starter's weights or a copy of the current rule rather than from random weights, and adding a reward for kills.\n\n**Sources.** Two entries, and I confirmed each quote appears at its URL: the current entry's JSON and the repository's `constants.rs`.\n\nBoth JSON files parse. The result is 41,948 bytes, and `entry_sha256` hashes the base64 string, the same way the earlier entry's does. `git status` shows untracked dotfiles and settings files in the repository root (`.bashrc`, `.gitconfig`, `.claude/` and others). I didn't create them and left them alone, but they may matter to the path check.\n\nFiles are in `artifacts/goal/items/sp-curriculum-1/`:\n- result.json\n- sources.json","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":1725703,"cost":{"source":"runtime","usdMicros":1313618},"inputTokens":79,"model":"claude-opus-5-5","outputTokens":23817,"runtime":"claude","turns":46,"wallClockMs":3019054}},{"artifacts":[{"bytes":20559,"hash":"b523ae7e6abf69d7b28da09e74dcc80379fc4cece2ad5334d8241f2f447c49d9","mediaType":"text/markdown","name":"merge","path":"artifacts/goal/merges/02/result.md"}],"attempt":1,"bundleHash":null,"device":"6277a2eceb8f9bd8","findings":[],"hash":"f709d77d6f1dd6a731df6cad3d3fd7904b0183771847c3118f500eed51a0de07","nodeId":"71e4fd4a-1465-4a22-9b60-cbe6de58fb35","outcome":"completed","summary":"I've written the merge to `artifacts/goal/merges/02/result.md` (20,559 bytes, limit 64,000). It has one table row per item, with all four records, size in bytes and ONNX operators, and a short \"found / do next\" paragraph for each. Every number is copied exactly from the item's own result file. The models are left out.\n\n**Overall:** none of the 25 results is clearly stronger than the current entry.\n- **Head to head:** ten candidates beat it. The best are curriculum-2 at 0.917, curriculum-4 at 0.8655 and gradient-5 at 0.794.\n- **Against exploiters:** every one of those ten scores below 0.5 against a 10-minute exploiter trained on it (between 0.065 and 0.43).\n- **Direct comparison:** two items ran the current entry against their own exploiter. It held 0.81 against league-2's exploiter and 0.879 against league-5's, while those candidates scored 0.129 and 0.4005.\n- **For the cross-play round:** gradient-2, gradient-5 and league-5 have the best balance of beating the entry and resisting exploiters. league-5's exported file was never run in ONNX Runtime.\n- **Pure neural nets:** all eight that were trained from scratch or by copying the rule lost badly to it. Every gain came from tuning or adding to the hand-built rule.\n\n**Blocked or unchanged items:**\n- **Returned the current model byte for byte:** league-1, league-4, curriculum-3 and evolution-3. I confirmed this by fetching the current entry again and comparing hashes.\n- **Blocked with no model:** gradient-3 and response-4.\n- **evolution-3:** it reports records even though it built no working simulator, and its operator list doesn't match its own file. I marked all its records as unverified.\n\n**Checks I ran on the inputs:**\n- All 25 file hashes matched the brief.\n- Every model's byte count matches what the item reports.\n- The operators I read from each model match the item's list, except curriculum-3 (lists none) and evolution-3.\n- Seven items recorded the hash of the base64 text instead of the hash of the decoded model. That accounts for the hash \"disagreement\" gradient-5 reported; the model itself is fine.\n\n**Flagged sources:** none of the result files contain URLs, and the source lists behind the flags for league-3, curriculum-4, evolution-5 and response-3 weren't among my inputs. So I couldn't tell which claims those sources backed. Each of those items carries a note saying so; their match records are their own local measurements.\n\nI wrote nothing except the result file and my own scratch files under `test/scratch/`. `git status` also shows untracked dotfiles in the workspace root (`.bashrc`, `.claude/`, `.gitmodules` and others) that I didn't create, and I left them alone.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":1351682,"cost":{"source":"runtime","usdMicros":1496243},"inputTokens":38,"model":"claude-opus-5-5","outputTokens":16708,"runtime":"claude","turns":20,"wallClockMs":192803}},{"artifacts":[{"bytes":23876,"hash":"821976d026e6643cde044be8d63c6bafbec8554782a453cf35ac86800def5581","mediaType":"text/markdown","name":"report","path":"artifacts/goal/writeup/report.md"}],"attempt":1,"bundleHash":null,"device":"70a431697096c556","findings":[],"hash":"f81520d710e2fd634713aa9f1b234594759e46d1d2a5317e18386457725f6fd0","nodeId":"c4012288-7826-429a-a8ef-4ca6dfacc126","outcome":"completed","summary":"Created [report.md](/home/imd18/.identitymd/work/d94acc27-198d-4d50-b5c6-5cdd0575cf7a/c4012288-7826-429a-a8ef-4ca6dfacc126/artifacts/goal/writeup/report.md).\n\nChecked rankings, records, sizes and operators against both sources. The report is 23,876 bytes, within the 24,000-byte limit, and remains untracked for upload.","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":518528,"cost":{"source":"estimated","usdMicros":1900968},"inputTokens":38829,"model":"gpt-6-astra","outputTokens":19883,"runtime":"codex","turns":6,"wallClockMs":734120}},{"artifacts":[{"bytes":53621,"hash":"d1b7ed850055fc54c2b4cd9094382ce859b4753393f537c755785780462ba356","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-gradient-4/result.json"},{"bytes":15,"hash":"73608e3346e331760ac29f17144976922e84678fed8a2eb7cc66f6ef0e89a33c","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-gradient-4/sources.json"}],"attempt":1,"bundleHash":null,"device":"42a6bb6aa496cd6b","findings":[],"hash":"fa23858739c9333cb0f89ea31f68af9c6dd55c20a6801a978940207ec5052b1a","nodeId":"32fd1310-dde5-49a4-a57d-14b62a4e514c","outcome":"completed","summary":"I wrote both output files, but the model I'm delivering is **much weaker than the current entry and should not replace it**. It scores 0.3265 against the current entry over 1,000 matches.\n\nEverything ran from `.cache/`, and the only files outside it are `result.json` and `sources.json`. The simulator, the rule and Starter were reproduced faithfully: the current entry scores 0.991 against Starter over 3,000 random-spawn matches (its seat measured 0.9933).\n\n**The model**\n- It is a small network that reads the newest two of the four observation frames (112 inputs). Its layers are 64, 32 and 3 wide, so 9,411 parameters.\n- The ONNX file is 38,106 bytes and uses only `Slice`, `Gemm` and `Relu`, all operators the current model or Starter uses.\n- I trained it in a trainer I wrote in Rust over the repo's simulator, with seed 4:\n  - **Warm start:** it first copied the current rule.\n  - **Policy gradient:** about 20 minutes of PPO self-play followed, with a learned value network.\n  - **Opponents:** the current rule, the four built-ins and past versions of itself.\n  - **Reward:** hits given and taken, plus the result.\n\n**Measured scores**\n\n| Opponent | Matches | Score |\n|---|---|---|\n| Starter | 3,000 | 0.8895 |\n| Four built-ins | 800 | 0.9766 |\n| Current entry | 1,000 | 0.3265 (264 wins, 611 losses, 125 draws) |\n| Its own exploiter | 500 | 0.296 |\n\nThe exploiter was a copy of the final model trained for about 9 minutes with the final model as its only opponent. That run was cut at the command timeout, so I used its best saved checkpoint.\n\n- **Not final:** the score against the rule was still rising when time ran out. A longer run would likely improve it.\n- **Not checked:** I did not run the model through the repo's validator, which needs a build I couldn't make here. I checked the ONNX file by decoding it and recomputing one forward pass in Python, not with ONNX Runtime.\n- **Sources:** `sources.json` is `{\"sources\": []}`; I relied on none.\n\nFiles are in `artifacts/goal/items/sp-gradient-4/`:\n- `result.json`\n- `sources.json`","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":1290437,"cost":{"source":"runtime","usdMicros":798700},"inputTokens":50,"model":"claude-sonnet-5-5","outputTokens":38321,"runtime":"claude","turns":27,"wallClockMs":2521826}},{"artifacts":[{"bytes":48636,"hash":"7d46d3e5ce320d92ee2a1ebcdf9b2927440364bdc72f260630460bdabf9231ea","mediaType":"application/json","name":"result","path":"artifacts/goal/items/sp-starter-4/result.json"},{"bytes":388,"hash":"cd44a3589b4f67fa8f567a85b4839e7bd55b36593cc9e254d3ac5929282be12a","mediaType":"application/json","name":"sources","path":"artifacts/goal/items/sp-starter-4/sources.json"}],"attempt":1,"bundleHash":null,"device":"6a4f1e5430861a77","findings":[],"hash":"fa9aaf1a8290968e373d8d9d8c6636e8d07b2a474a23ef886901d5c7d5f8ee88","nodeId":"b67f583f-487d-4d5c-90a6-d431076bc079","outcome":"completed","summary":"I trained a self-play model and wrote the result, but **the model should not be uploaded.** It beats Starter but loses to almost everything else, including every match against the current entry.\n\nThe machine allowed only **one CPU** (cgroup `cpu.max 100000 100000`), so I got about 30 matches a second against Starter instead of the earlier seat's 770. That cut how much training was possible.\n\n**Building the harness.** There was no C compiler on the machine, so I used zig as the linker. For the current entry I loaded the ONNX Runtime library from the Python `onnxruntime` package, which avoided the OpenSSL problem. Everything I built is in `.cache/`.\n\n**Harness check:** the current model scored **0.991** against Starter over 3,000 random-spawn matches (2,973 wins, 27 losses). Its seat measured 0.99333.\n\n**What the model is:** a small network (224→32→32→3, plain `Gemm`/`Relu`, 33,735 bytes). It started as an imitation of Starter, which scored only 0.146 against Starter: shrinking to 32 wide lost most of Starter's skill. I then improved it with about 200 generations of evolution strategies (about 24 minutes) against a league of Starter, the current rule and its own past versions. All random choices were seeded with 4.\n\n| Record (fresh seeds) | Score |\n|---|---|\n| Starter, 3,000 matches | **0.95858** |\n| Four built-ins, 800 matches | **0.0206** (chaser 0.0525, dogfighter 0, ace 0, brawler 0.03) |\n| Current model, 1,000 matches, half from each side | **0.0** (1,000 losses) |\n| Exploiter, 500 matches | **0.052** |\n\n- **Exploiter:** a copy of the final model, trained only against it. It got 7 minutes of training rather than the 10 asked for, to fit the time budget; the result file says so.\n- **Why it failed:** it never scored against the current rule, and scoring zero gives this training method nothing to learn from. It also never met the built-ins, so self-play only learned how to beat Starter.\n- **Suggestions for the next seat** (in the result's `next` field): add the built-ins to the league from the start, give partial reward for damage dealt and survival time, and start from the current rule or a wider imitation network (64 wide at half precision fits under 44 KB).\n\nThe two sources are the repository's `RULES.md` and the current entry's JSON. I checked locally that the first quote is in `RULES.md` and that the second is in the entry JSON as downloaded.\n\nFiles are in `artifacts/goal/items/sp-starter-4/`:\n- `result.json` (48,636 bytes)\n- `sources.json`","treeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","usage":{"cachedInputTokens":2053392,"cost":{"source":"runtime","usdMicros":1596920},"inputTokens":75,"model":"claude-opus-5-5","outputTokens":33818,"runtime":"claude","turns":42,"wallClockMs":3541266}}],"verification":[{"checks":[],"detail":"paths and tree verified; no suite was run for this kind of work","evaluation":"structural","profile":"none","status":"accepted","submissionHash":"014ba9257a889481997c3aa798398290673cbd132b3a4ad658cfb10c406a1706","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":"03655f6ec50a81527a1783affabfae6a8b2af06a4b1642ae90b7555c2456b10d","verifiedTreeHash":"4b825dc642cb6eb9a060e54bf8d69288fbee4904","verifierVersion":"0.1.0+18543036"},{"checks":[],"detail":"paths and tree verified; 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