{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:GIFZ2KDSX5GE6MB3IVBJ7N3KQ5","short_pith_number":"pith:GIFZ2KDS","canonical_record":{"source":{"id":"2605.17276","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-17T05:53:35Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"4aab53c1a31b3388bc57ef9e078669dbef64b3417f46af917b767f957385a59e","abstract_canon_sha256":"850356e11ed7e9380641ff4793c2a737ab9c3e244769766cd36d3bc49c6780ff"},"schema_version":"1.0"},"canonical_sha256":"320b9d2872bf4c4f303b45429fb76a874c8864c77187b91fbecedeb619aa20f4","source":{"kind":"arxiv","id":"2605.17276","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.17276","created_at":"2026-05-20T00:03:49Z"},{"alias_kind":"arxiv_version","alias_value":"2605.17276v1","created_at":"2026-05-20T00:03:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17276","created_at":"2026-05-20T00:03:49Z"},{"alias_kind":"pith_short_12","alias_value":"GIFZ2KDSX5GE","created_at":"2026-05-20T00:03:49Z"},{"alias_kind":"pith_short_16","alias_value":"GIFZ2KDSX5GE6MB3","created_at":"2026-05-20T00:03:49Z"},{"alias_kind":"pith_short_8","alias_value":"GIFZ2KDS","created_at":"2026-05-20T00:03:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:GIFZ2KDSX5GE6MB3IVBJ7N3KQ5","target":"record","payload":{"canonical_record":{"source":{"id":"2605.17276","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-17T05:53:35Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"4aab53c1a31b3388bc57ef9e078669dbef64b3417f46af917b767f957385a59e","abstract_canon_sha256":"850356e11ed7e9380641ff4793c2a737ab9c3e244769766cd36d3bc49c6780ff"},"schema_version":"1.0"},"canonical_sha256":"320b9d2872bf4c4f303b45429fb76a874c8864c77187b91fbecedeb619aa20f4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:03:49.310576Z","signature_b64":"1ONGcUpbcL8rijcnrzhQTBgDlOu7an7VmX+zKzFnXuFwBRoZUQLRcraoLIg7plBdP0TvsO23aR4gNILk0VbKCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"320b9d2872bf4c4f303b45429fb76a874c8864c77187b91fbecedeb619aa20f4","last_reissued_at":"2026-05-20T00:03:49.309749Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:03:49.309749Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.17276","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:03:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2G5Kjhwy38suGpLf0L11N2SA7lzN155I2POPSyuFSaOqCMtMrVvKHBqL9hHHhdGDzhyRBum9tDR0wCVprTuDBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T20:08:17.146194Z"},"content_sha256":"6d1f0d18e28fa0a8718805514f35f2f50eab348af17ac0bc0d17a37432809849","schema_version":"1.0","event_id":"sha256:6d1f0d18e28fa0a8718805514f35f2f50eab348af17ac0bc0d17a37432809849"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:GIFZ2KDSX5GE6MB3IVBJ7N3KQ5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"How Do Electrocardiogram Models Scale?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Ant\\^onio H. Ribeiro, Fabio Bonassi, Jiawei Li, Johan Sundstr\\\"om, Ming Jin, Stefan Gustafsson, Thomas B. Sch\\\"on","submitted_at":"2026-05-17T05:53:35Z","abstract_excerpt":"While scaling laws have established a fundamental framework for foundation models in natural language processing, their applicability to electrocardiogram (ECG) models remains poorly characterized. Indeed, recent studies do not always yield consistent downstream gains as one increases the model size or pre-training dataset size of ECG models, leaving the exact roles of architectural inductive biases, pre-training paradigms, and expected improvements with size largely unanswered. In this work, we systematically investigate neural and loss-to-loss scaling laws within the ECG domain. By pre-train"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.17276","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.17276/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"claim_evidence","ran_at":"2026-05-19T22:01:57.831434Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T21:33:23.775091Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"7fe51379bf255288dd30e0c1bfbb817ce7493faac57f652ce478d53e25b7b60e"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:03:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yZ8a0WxuaDkhRxSLwRVkZ3YFoJwcj5wXK/zD7EKE8l9/Horj531A0rR1ZE8fAM1gPtsuTJFbQPswfKa8pMUQCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T20:08:17.146636Z"},"content_sha256":"ef0b37a65bf1fae37661554d264eed5439f5ea6519bbbf914ece0830176028c2","schema_version":"1.0","event_id":"sha256:ef0b37a65bf1fae37661554d264eed5439f5ea6519bbbf914ece0830176028c2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GIFZ2KDSX5GE6MB3IVBJ7N3KQ5/bundle.json","state_url":"https://pith.science/pith/GIFZ2KDSX5GE6MB3IVBJ7N3KQ5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GIFZ2KDSX5GE6MB3IVBJ7N3KQ5/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-22T20:08:17Z","links":{"resolver":"https://pith.science/pith/GIFZ2KDSX5GE6MB3IVBJ7N3KQ5","bundle":"https://pith.science/pith/GIFZ2KDSX5GE6MB3IVBJ7N3KQ5/bundle.json","state":"https://pith.science/pith/GIFZ2KDSX5GE6MB3IVBJ7N3KQ5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GIFZ2KDSX5GE6MB3IVBJ7N3KQ5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:GIFZ2KDSX5GE6MB3IVBJ7N3KQ5","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"850356e11ed7e9380641ff4793c2a737ab9c3e244769766cd36d3bc49c6780ff","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-17T05:53:35Z","title_canon_sha256":"4aab53c1a31b3388bc57ef9e078669dbef64b3417f46af917b767f957385a59e"},"schema_version":"1.0","source":{"id":"2605.17276","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.17276","created_at":"2026-05-20T00:03:49Z"},{"alias_kind":"arxiv_version","alias_value":"2605.17276v1","created_at":"2026-05-20T00:03:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17276","created_at":"2026-05-20T00:03:49Z"},{"alias_kind":"pith_short_12","alias_value":"GIFZ2KDSX5GE","created_at":"2026-05-20T00:03:49Z"},{"alias_kind":"pith_short_16","alias_value":"GIFZ2KDSX5GE6MB3","created_at":"2026-05-20T00:03:49Z"},{"alias_kind":"pith_short_8","alias_value":"GIFZ2KDS","created_at":"2026-05-20T00:03:49Z"}],"graph_snapshots":[{"event_id":"sha256:ef0b37a65bf1fae37661554d264eed5439f5ea6519bbbf914ece0830176028c2","target":"graph","created_at":"2026-05-20T00:03:49Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T22:01:57.831434Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T21:33:23.775091Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.17276/integrity.json","findings":[],"snapshot_sha256":"7fe51379bf255288dd30e0c1bfbb817ce7493faac57f652ce478d53e25b7b60e","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While scaling laws have established a fundamental framework for foundation models in natural language processing, their applicability to electrocardiogram (ECG) models remains poorly characterized. Indeed, recent studies do not always yield consistent downstream gains as one increases the model size or pre-training dataset size of ECG models, leaving the exact roles of architectural inductive biases, pre-training paradigms, and expected improvements with size largely unanswered. In this work, we systematically investigate neural and loss-to-loss scaling laws within the ECG domain. By pre-train","authors_text":"Ant\\^onio H. Ribeiro, Fabio Bonassi, Jiawei Li, Johan Sundstr\\\"om, Ming Jin, Stefan Gustafsson, Thomas B. Sch\\\"on","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-17T05:53:35Z","title":"How Do Electrocardiogram Models Scale?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.17276","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:6d1f0d18e28fa0a8718805514f35f2f50eab348af17ac0bc0d17a37432809849","target":"record","created_at":"2026-05-20T00:03:49Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"850356e11ed7e9380641ff4793c2a737ab9c3e244769766cd36d3bc49c6780ff","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-17T05:53:35Z","title_canon_sha256":"4aab53c1a31b3388bc57ef9e078669dbef64b3417f46af917b767f957385a59e"},"schema_version":"1.0","source":{"id":"2605.17276","kind":"arxiv","version":1}},"canonical_sha256":"320b9d2872bf4c4f303b45429fb76a874c8864c77187b91fbecedeb619aa20f4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"320b9d2872bf4c4f303b45429fb76a874c8864c77187b91fbecedeb619aa20f4","first_computed_at":"2026-05-20T00:03:49.309749Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:03:49.309749Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1ONGcUpbcL8rijcnrzhQTBgDlOu7an7VmX+zKzFnXuFwBRoZUQLRcraoLIg7plBdP0TvsO23aR4gNILk0VbKCg==","signature_status":"signed_v1","signed_at":"2026-05-20T00:03:49.310576Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.17276","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6d1f0d18e28fa0a8718805514f35f2f50eab348af17ac0bc0d17a37432809849","sha256:ef0b37a65bf1fae37661554d264eed5439f5ea6519bbbf914ece0830176028c2"],"state_sha256":"58c795ee2d9b98f4dbd2001cf70bc99f97a7891db384d178d6b879ecd21da8f4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cGlqqPjzgIL+WkTA+xQHkPgdEkvg3l9/XOjfLZCSQDmYEDrXRS4u5o+EXKME6sL02sLPIvnOHjTxwFx8+qKGBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-22T20:08:17.148734Z","bundle_sha256":"ed85990bf12de6a5e493cf5f88bdb4a27e9fb9e87882f07cfc78402ba6ae6fd5"}}