{"_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_b4d6e36b8acafbc8169b7ba4029b448d679f0dfaee882aaa1961005a35237f04","bitcoin_anchor":{"bitcoin_attestations":[],"calendar_attestations":[],"ots_url":"","stamped_at":"","status":"pending_next_stamp"},"envelope":{"anchors":[{"chain":"crovia.axiom_graph","height":0,"merkle_proof":"spider_vendor_press_v1","root_at_anchor":"spider_vendor_press_v1"}],"axiom_id":"axm_b4d6e36b8acafbc8169b7ba4029b448d679f0dfaee882aaa1961005a35237f04","axiom_type":"AX.OBS","body":{"axiom_subtype":"news.vendor_press.v1","category":"news","fingerprint":"11b754ad58a2562378d92ede1bec94548c00c9536123cba57269ee248d518765","published":"Thu, 28 May 2026 00:00:00 -0400","receipt_hash":"11b754ad58a2562378d92ede1bec94548c00c9536123cba57269ee248d518765","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":"11b754ad58a2562378d92ede1bec94548c00c9536123cba57269ee248d518765","observed_at":"2026-05-28T04:43:38.862500Z","parent_run_hash":"58f8b4a134069e0a15ea3949252489597eb86dd27c9ca3fb15c6fb838ce49ef3","published":"Thu, 28 May 2026 00:00:00 -0400","runtime_version":"0.1.0","schema":"spider.news.vendor_press.v1","source_status":200,"source_url":"https://export.arxiv.org/rss/cs.AI","spider":"vendor_press","summary_excerpt":"arXiv:2605.27873v1 Announce Type: new \nAbstract: AI models underpin data-centric applications from image and text processing to scientific discovery in biology, physics, and chemistry. Yet developing them remains heavily manual, requiring practitioners to design architectures, build training pipelines, and iteratively refine solutions, making it challenging for natural scientists without specialized AI engineering expertise to build the high-performing models their research demands. To reduce this burden and broaden access to AI for scientific discovery, agents that automatically build AI models have been proposed. However, the performance of these agents is largely limited by the parametric knowledge of their underlying large language models, which is static, often outdated, and sparse on practical AI model engineering know-how. To address this limitation, we introduce AIBuildAI-2, a knowledge-enhanced agent with an external, evolving knowledge system for automatically building AI mod","title":"AIBuildAI-2: A Knowledge-Enhanced Agent for Automatically Building AI Models","url":"https://arxiv.org/abs/2605.27873","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.27873v1 Announce Type: new \nAbstract: AI models underpin data-centric applications from image and text processing to scientific discovery in biology, physics, and chemistry. Yet developing them remains heavily manual, requiring practitioners to design architectures, build training pipelines, and iteratively refine solutions, making it challenging for natural scientists without specialized AI engineering expertise to build the high-performing models their research demands. To reduce this burden and broaden access to AI for scientific discovery, agents that automatically build AI models have been proposed. However, the performance of these agents is largely limited by the parametric knowledge of their underlying large language models, which is static, often outdated, and sparse on practical AI model engineering know-how. To address this limitation, we introduce AIBuildAI-2, a knowledge-enhanced agent with an external, evolving knowledge system for automatically building AI mod","title":"AIBuildAI-2: A Knowledge-Enhanced Agent for Automatically Building AI Models","vendor":"arxiv_cs_ai"},"confidence":{"method":"deterministic"},"decision":"POSITIVE","issued_at":"2026-05-28T04:43:38Z","notes":"Spider vendor_press (news) news.vendor_press.v1","object":{"captured_by":"crovia.spider.vendor_press","primary_source_url":"https://arxiv.org/abs/2605.27873"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:c269c9d0b21d6f2d628eb06b4311a164ee69a421f3ad38eb7906d9f37152b3d1f7602ba0ceb45d9f11e0c5e595dc18584de47c9652661e7f9df48c1726554209","signer":"crovia.substrate","subject":{"observed_at":"2026-05-28T04:43:38Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2605.27873"},"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":"7e18a80d3598806c8db634a29eb8ad5cff7ea9e4f6d826345953369c504cce1d","leaf_index":155608,"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":"42c66d6fb390ef210f7362104f36e7abfe9e653552ab866a2133f518e8b31f15","side":"right"},{"sibling":"89d251dde8247722d18a24fb4fb9c0fbc5158cc37985b38a9da82495ed130cfb","side":"right"},{"sibling":"212fa5afd846952dab79e4416a08c9821754eb0d4f9bbe07b895f186c8008d49","side":"right"},{"sibling":"8e1da781a41050afc2962abd670bf79a21c12bdad3ec09051399428ae8b2fed8","side":"left"},{"sibling":"c8c21e6ef77478e42625acc377a4062ee6ad593745301a1f6d90936e6d94e475","side":"left"},{"sibling":"9271164ea0f2c427fba10eb695cb98be464e920bb5d4d839cf341ff315753f23","side":"right"},{"sibling":"7252c2f7874601f6b2d750e8a0aa6cc72c6924d6614fe237cd5c97d2026c5c17","side":"left"},{"sibling":"70e73f197ecea5d12ff2f9cf2ca33087110a782ea956c539b3058422ada4b2bd","side":"left"},{"sibling":"8c9c7b64ca041ef0029c35e402972d0bebed706fe997b9c587da6f5c811dcce1","side":"left"},{"sibling":"aa7a574eaea239ab8851da225206477d62a6ea5a15b65834f22d1c10288348e7","side":"left"},{"sibling":"7fcba2ee8232873e5d864902f28968d2be38867751c238f238f629eddb98b226","side":"left"},{"sibling":"816f233274bb10f5a122aac086a0c8c697b78fec67a4af55190bb596b7506fab","side":"left"},{"sibling":"d415e6939aee710631f5062799379b547d2c3e3d9a68f263bbb5a693285ab2ca","side":"left"},{"sibling":"d422d38e0ffe6e849b6ce3259d90c9d36497b4d7391a003e15591668c79028a7","side":"right"},{"sibling":"35ca36cee447f0ef7064a25d55f59357c66901e31427729c4c1d8b14aa8adb6c","side":"left"},{"sibling":"7b6f0bea4291a5e63574dfca9aa0f9756f450c9478c3d07474d39a7ababb51f9","side":"right"},{"sibling":"1d39fe14b21e2ebbfb87e882423b24ee9469eae1e4c77af5b799ac4db9537467","side":"right"},{"sibling":"d841ad93efda0869e5eb97678f348f03f5caab4353e05ff4bf18f47fb945b822","side":"left"}]},"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":156177,"merkle_root":"c5705a0243d16afd8b1ebfd731b7aa304079c442c2a7906493c5bbed374c69ec","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","run_id":"hourly_json_retrofit_20260528T053701Z","schema":"crovia.seal.v1","seal_family_version":"crovia-seal-family/1","seal_kind":"substrate_batch","sealed_at":"2026-05-28T05:37:36Z","sig_algorithm":"ed25519","signature":"f087e13febc8bb6a2e0812610de64cebc65be92915518d9c4b230799c3b161839b04c4eb1b02741f938f35545a76ab76555b04c782bdc2f9a44852d171d65909","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_b4d6e36b8acafbc8169b7ba4029b448d679f0dfaee882aaa1961005a35237f04"}}