{"_canonicalization":{"envelope_id":"axm_ + sha256(envelope minus {signature, axiom_id, anchors})","envelope_signature":"ed25519(envelope minus {signature, axiom_id})","json":"sort_keys=True, separators=(',',':'), ensure_ascii=False, allow_nan=False, utf-8","leaf_hash":"sha256(0x00 || canonical_json(envelope_full))","seal_signature":"ed25519(seal minus {signature, sig_algorithm})"},"axiom_id":"axm_892e6cab1373a28be3052313ac4750015541bb6e4b8379ba5fa2661c154b78b5","bitcoin_anchor":{"bitcoin_attestations":["bitcoin_block_949451"],"calendar_attestations":["https://finney.calendar.eternitywall.com","https://btc.calendar.catallaxy.com","https://alice.btc.calendar.opentimestamps.org","https://bob.btc.calendar.opentimestamps.org"],"ots_url":"/registry/data/substrate/anchors/77fc9c28fae777b81da5b495b3115474df6592dfac590333213d3bdf8b94a9b3.ots","stamped_at":"2026-05-15T03:00:03Z","status":"bitcoin"},"envelope":{"anchors":[{"chain":"crovia.axiom_graph","height":0,"merkle_proof":"spider_vendor_press_v1","root_at_anchor":"spider_vendor_press_v1"}],"axiom_id":"axm_892e6cab1373a28be3052313ac4750015541bb6e4b8379ba5fa2661c154b78b5","axiom_type":"AX.OBS","body":{"axiom_subtype":"news.vendor_press.v1","category":"news","fingerprint":"cfec5ed94abc5af555a526b57d8125cc58da9158a76827b07209f106db4418b3","published":"Wed, 04 Jul 2018 07:00:00 GMT","receipt_hash":"cfec5ed94abc5af555a526b57d8125cc58da9158a76827b07209f106db4418b3","schema":"spider.news.vendor_press.v1","spider":"vendor_press","spider_record":{"axiom_subtype":"news.vendor_press.v1","category":"news","decision_hint":"POSITIVE","envelope_target":"AX.OBS","fingerprint":"cfec5ed94abc5af555a526b57d8125cc58da9158a76827b07209f106db4418b3","observed_at":"2026-05-03T15:11:27.656745Z","parent_run_hash":null,"published":"Wed, 04 Jul 2018 07:00:00 GMT","runtime_version":"0.1.0","schema":"spider.news.vendor_press.v1","source_status":200,"source_url":"https://openai.com/blog/rss.xml","spider":"vendor_press","summary_excerpt":"We’ve trained an agent to achieve a high score of 74,500 on Montezuma’s Revenge from a single human demonstration, better than any previously published result. Our algorithm is simple: the agent plays a sequence of games starting from carefully chosen states from the demonstration, and learns from them by optimizing the game score using PPO, the same reinforcement learning algorithm that underpins OpenAI Five.","title":"Learning Montezuma’s Revenge from a single demonstration","url":"https://openai.com/index/learning-montezumas-revenge-from-a-single-demonstration","vendor":"openai"},"summary":"We’ve trained an agent to achieve a high score of 74,500 on Montezuma’s Revenge from a single human demonstration, better than any previously published result. Our algorithm is simple: the agent plays a sequence of games starting from carefully chosen states from the demonstration, and learns from them by optimizing the game score using PPO, the same reinforcement learning algorithm that underpins OpenAI Five.","title":"Learning Montezuma’s Revenge from a single demonstration","vendor":"openai"},"confidence":{"method":"deterministic"},"decision":"POSITIVE","issued_at":"2026-05-03T15:11:27Z","notes":"Spider vendor_press (news) news.vendor_press.v1","object":{"captured_by":"crovia.spider.vendor_press","primary_source_url":"https://openai.com/index/learning-montezumas-revenge-from-a-single-demonstration"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:5391e6c42de6d5673e8c614ac7033c01290d3e82ff46ce528d12c2c83a044a6eba07f73a278c4ef3e7daef328954d6a83804841636c69e4ee27337d7289c4801","signer":"crovia.substrate","subject":{"observed_at":"2026-05-03T15:11:27Z","source_collector":"spider:vendor_press","target_id":"https://openai.com/index/learning-montezumas-revenge-from-a-single-demonstration"},"tsa":{"authority":"crovia.substrate.bootstrap","rfc3161_token":"{\"kind\":\"crovia.bootstrap.tsa\",\"source_jsonl\":\"/opt/crovia/spider/data/news/vendor_press_v1.jsonl\",\"source_seal_merkle_root\":\"spider_vendor_press_v1\",\"upgrade_path\":\"Sessione H \\u2014 OpenTimestamps weekly anchor\"}"},"zk_mode":"clear","zk_proof":null},"ledger":{"leaf_hash":"45451a34396fd9520a3bc4a7118033200109cd073193d4618373c294a55a3173","leaf_index":111349,"ledger_path":"/opt/crovia/substrate/axiom_ledger.jsonl"},"merkle_proof":{"hash_alg":"sha256","leaf_prefix":"0x00","node_prefix":"0x01","odd_leaf_rule":"duplicate_last","path":[{"sibling":"54b13b3b83c36069895de0f57dca0ed9c03bd2ba98b4381b9b0ff5ba5ac2e29d","side":"left"},{"sibling":"63d6a5b9dcd74b0b465bb18a46734ac36b437e3d06d9651e0f6302a85557c57a","side":"right"},{"sibling":"e5c099d257223964f318d386373bb8bd70a82652379e072b0cab4f8862711831","side":"left"},{"sibling":"16b73bfa3e2969ebe04077725056b705034977b6850f4c1e4ba50a0a1ad03440","side":"right"},{"sibling":"ab163e40bd8e556db302865a194fcf0b81d9c23151ea6aeed8c19fe338099921","side":"left"},{"sibling":"3c190d3ed38ea4b7ff0e10f26f11be6479e9458dc5360ff1be05af76c22282e3","side":"left"},{"sibling":"85b04019be154438348fa87830598ca6136f0ba202258678458f2ca98847c975","side":"left"},{"sibling":"c3b6bc2a92a929153395bd488f116be1795aa53c22c134d42c45d915a516f551","side":"left"},{"sibling":"22664c4c4d36711e4d6d3caff9b8d78ad50645790ab4228b865f88ccc73b9804","side":"right"},{"sibling":"1e7f05cd06e20834fa02b399bd87f68b112d4974bb4065ba7a9f0541a6410670","side":"left"},{"sibling":"7322dcde9f6dec40cc973ca12dd1ebd16fe5aecf078317f5e57c51a436730eb5","side":"right"},{"sibling":"a6d6e5bc888edc394757fbba02b675bac6ad0831e36a6f5ebbaa4e5548f25603","side":"right"},{"sibling":"76d7c4385d71ae8104107105be66eddb792e01ed6e493db5a9b4eec5441abb76","side":"left"},{"sibling":"b03f005860bf95148ea89c703a32ca19ab7a2ca71b58bb628ff94a9edb8703b0","side":"left"},{"sibling":"21d1e30b556f553dc939fddc23e6367f0a7755ebe3dd489dc0001cef017beb7f","side":"right"},{"sibling":"f2817ab288b5324fe49770372c7a10f33f7cd11005f8d4c0a730316f5229dc98","side":"left"},{"sibling":"725fac972e772ca0dc598810ea1abc70df472f72d2d6ab8a0baee2b80e5d2f4c","side":"left"},{"sibling":"98fc57dfef8873b512edc8340f7181df57302bb96777625e072235c62d7c5895","side":"right"}]},"schema":"crovia.axiom_proof.v1","seal":{"first_collector_run_id":"","first_receipt_hash":"","jsonl_path":"/opt/crovia/substrate/axiom_ledger.jsonl","key_id":"430895f101d38164","last_collector_run_id":"","last_receipt_hash":"","leaf_count":134292,"merkle_root":"77fc9c28fae777b81da5b495b3115474df6592dfac590333213d3bdf8b94a9b3","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","run_id":"hourly_json_retrofit_20260515T023701Z","schema":"crovia.seal.v1","seal_family_version":"crovia-seal-family/1","seal_kind":"substrate_batch","sealed_at":"2026-05-15T02:37:25Z","sig_algorithm":"ed25519","signature":"68107a834b00b24f5d4501e5ec727445311f132a486567ecc4c72a4e6dff24c8c21f2de3105293353ba5fdbe370d032819af6aa70f694e2e39b6af6737507009","signer_version":"1.1.0"},"trust_root":{"key_id":"430895f101d38164","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","signature_algorithm":"ed25519","url":"/registry/canon/TRUST_ROOT.md"},"verifier":{"spec":"/registry/canon/AXIOM_RECEIPT_v1.md","url":"/v/axm_892e6cab1373a28be3052313ac4750015541bb6e4b8379ba5fa2661c154b78b5"}}