{"_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_2b29f5c3cecf8253bd6e2905e7b12b09b5bd39be486f328b6eadcb25cf4a463c","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_2b29f5c3cecf8253bd6e2905e7b12b09b5bd39be486f328b6eadcb25cf4a463c","axiom_type":"AX.OBS","body":{"axiom_subtype":"news.vendor_press.v1","category":"news","fingerprint":"de1588af3d0a4caee0daa34ca4e0d76b19978106526f3747af13176b322ab902","published":"Wed, 13 May 2026 00:00:00 -0400","receipt_hash":"de1588af3d0a4caee0daa34ca4e0d76b19978106526f3747af13176b322ab902","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":"de1588af3d0a4caee0daa34ca4e0d76b19978106526f3747af13176b322ab902","observed_at":"2026-05-13T04:43:23.245186Z","parent_run_hash":"2a6c2eea51fc0fdbbd1acb92c878f75767034fb102189f6a665cf0ad0e16536e","published":"Wed, 13 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:2605.10840v2 Announce Type: cross \nAbstract: We present Clin-JEPA, a multi-phase co-training framework for joint-embedding predictive (JEPA) pretraining on EHR patient trajectories. JEPA architectures have enabled latent-space planning in robotics and high-quality representation learning in vision, but extending the paradigm to EHR data -- to obtain a single backbone that simultaneously forecasts patient trajectories and serves diverse downstream risk-prediction tasks without per-task fine-tuning -- remains an open challenge. Existing JEPA frameworks either discard the predictor after pretraining (I-JEPA, V-JEPA) or train it on a frozen pretrained encoder (V-JEPA 2-AC), leaving the encoder unaware of the rollout signal that the retained predictor must use at inference; co-training the encoder and predictor under a shared JEPA prediction objective would supply this grounding, but na\\\"ive co-training is unstable, with representation collapse and online/target drift causing autoregr","title":"Clin-JEPA: A Multi-Phase Co-Training Framework for Joint-Embedding Predictive Pretraining on EHR Patient Trajectories","url":"https://arxiv.org/abs/2605.10840","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.10840v2 Announce Type: cross \nAbstract: We present Clin-JEPA, a multi-phase co-training framework for joint-embedding predictive (JEPA) pretraining on EHR patient trajectories. JEPA architectures have enabled latent-space planning in robotics and high-quality representation learning in vision, but extending the paradigm to EHR data -- to obtain a single backbone that simultaneously forecasts patient trajectories and serves diverse downstream risk-prediction tasks without per-task fine-tuning -- remains an open challenge. Existing JEPA frameworks either discard the predictor after pretraining (I-JEPA, V-JEPA) or train it on a frozen pretrained encoder (V-JEPA 2-AC), leaving the encoder unaware of the rollout signal that the retained predictor must use at inference; co-training the encoder and predictor under a shared JEPA prediction objective would supply this grounding, but na\\\"ive co-training is unstable, with representation collapse and online/target drift causing autoregr","title":"Clin-JEPA: A Multi-Phase Co-Training Framework for Joint-Embedding Predictive Pretraining on EHR Patient Trajectories","vendor":"arxiv_cs_ai"},"confidence":{"method":"deterministic"},"decision":"POSITIVE","issued_at":"2026-05-13T04:43:23Z","notes":"Spider vendor_press (news) news.vendor_press.v1","object":{"captured_by":"crovia.spider.vendor_press","primary_source_url":"https://arxiv.org/abs/2605.10840"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:423de34e8c3d496eb6ede5e4b1abfc1e179895413978c07fd8ac4abc7e0d1366f2035090915252c001abeb991d62356c006b046fddbe3ba6d489149dddb8280f","signer":"crovia.substrate","subject":{"observed_at":"2026-05-13T04:43:23Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2605.10840"},"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":"03edc220187438bcac8e0010a4775dd4eab8ac9054d392dde702736986496293","leaf_index":130841,"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":"7e4e6c64313fb98433cc49daf2ffd20d9b6ed438b8d76df69bd7646a9e227f55","side":"left"},{"sibling":"1d4ea7cc54b06c71de5d6aa16eaed18d82e724ad025975c4314d97f8a0f49afc","side":"right"},{"sibling":"013244374ad4ad1c7827ac9384a11249fb15ec7e68f9c44881d9f079d0bab014","side":"right"},{"sibling":"a16c1f7aa28a1f9f3ebb1639cfbfe86d53b7a4961ca29f46e8ee7de43395403f","side":"left"},{"sibling":"b39fc48325ec2629348b731570580eaa2165db885737369da3aafe7640d600b4","side":"left"},{"sibling":"6fb621d7885f617d4c32e8192907383f1535fee31ff131144336ec2721a782b9","side":"right"},{"sibling":"8c3518f7d38b276e607f8cbcb7f75921989c30b7fe9b7b7e1b44124372142599","side":"right"},{"sibling":"85f99166b87e29998300b931a3a01c0d1d77304f8efad796211b0ec7150ce694","side":"right"},{"sibling":"15cb4b41b9655a3093d130158e8145d2864f09dffa42ed180b96bc1d7e0f2f1b","side":"left"},{"sibling":"2170332c55df32c3e98553424d3e6242cef02b9714e5a34c7878ee7365425826","side":"left"},{"sibling":"ae6b2233fdba10d8b237d055d6febc6665ca818dcd31382efc2ab8723586948f","side":"left"},{"sibling":"d32d0a951c1d7e98a8e1951a587d83c97962daeeaa455643bc1d5d53744dc21e","side":"left"},{"sibling":"ffc4d51379293bc3e1910c7d612f409dc610fd9acf8241793fb89f82e1bad4ef","side":"left"},{"sibling":"62ac6554017807bd83187f5a3e5f4f72d6c482616429c2780e9fff1f4845fa04","side":"left"},{"sibling":"3a5e69cf0803f4c91f3895ed7c9a95748fef240bec4422e167c05300f79f06c0","side":"left"},{"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_2b29f5c3cecf8253bd6e2905e7b12b09b5bd39be486f328b6eadcb25cf4a463c"}}