{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:XGLKOTVMNGMSFILFVUVY57WZ2Y","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":"c4805dc70de8a97040209fe77858cf96d50cc54eab1bff04e31da85196167d37","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-05-24T19:43:15Z","title_canon_sha256":"967df33d6fc72d66dc2fa4226c4666fd7b51baf0f89bee780fd28f21f237499d"},"schema_version":"1.0","source":{"id":"2505.18847","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.18847","created_at":"2026-07-05T11:09:20Z"},{"alias_kind":"arxiv_version","alias_value":"2505.18847v1","created_at":"2026-07-05T11:09:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.18847","created_at":"2026-07-05T11:09:20Z"},{"alias_kind":"pith_short_12","alias_value":"XGLKOTVMNGMS","created_at":"2026-07-05T11:09:20Z"},{"alias_kind":"pith_short_16","alias_value":"XGLKOTVMNGMSFILF","created_at":"2026-07-05T11:09:20Z"},{"alias_kind":"pith_short_8","alias_value":"XGLKOTVM","created_at":"2026-07-05T11:09:20Z"}],"graph_snapshots":[{"event_id":"sha256:f12867a288721abe8f5196f113cfb4368f2d44f366ad7d8571ee8972622ca1fc","target":"graph","created_at":"2026-07-05T11:09:20Z","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":[],"endpoint":"/pith/2505.18847/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advances have increasingly applied large language models (LLMs) to electrocardiogram (ECG) interpretation, giving rise to Electrocardiogram-Language Models (ELMs). Conditioned on an ECG and a textual query, an ELM autoregressively generates a free-form textual response. Unlike traditional classification-based systems, ELMs emulate expert cardiac electrophysiologists by issuing diagnoses, analyzing waveform morphology, identifying contributing factors, and proposing patient-specific action plans. To realize this potential, researchers are curating instruction-tuning datasets that pair EC","authors_text":"Atharva Mhaskar, Chaojing Duan, Ding Zhao, Dylan Leong, Emerson Liu, Michael A. Rosenberg, William Han, Xiaoyu Song, Yihang Yao, Zhepeng Cen","cross_cats":["cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-05-24T19:43:15Z","title":"Signal, Image, or Symbolic: Exploring the Best Input Representation for Electrocardiogram-Language Models Through a Unified Framework"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.18847","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:eeeac391c8e44b5660e71c253315716ede6aa9d5c1c67ff7815bfb526dcfaac1","target":"record","created_at":"2026-07-05T11:09:20Z","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":"c4805dc70de8a97040209fe77858cf96d50cc54eab1bff04e31da85196167d37","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-05-24T19:43:15Z","title_canon_sha256":"967df33d6fc72d66dc2fa4226c4666fd7b51baf0f89bee780fd28f21f237499d"},"schema_version":"1.0","source":{"id":"2505.18847","kind":"arxiv","version":1}},"canonical_sha256":"b996a74eac699922a165ad2b8efed9d61f1d40ebc70e5a8671eb0c8ef48c9200","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b996a74eac699922a165ad2b8efed9d61f1d40ebc70e5a8671eb0c8ef48c9200","first_computed_at":"2026-07-05T11:09:20.528716Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:09:20.528716Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Usqg1ZfAn6NQ7t9QK29wJcWT5a3U0137UKl5vBHho17hM69cDFXGbuyk5hsLZMqhpUFXP9jlPXQ54HWG9TtlAg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:09:20.529194Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.18847","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eeeac391c8e44b5660e71c253315716ede6aa9d5c1c67ff7815bfb526dcfaac1","sha256:f12867a288721abe8f5196f113cfb4368f2d44f366ad7d8571ee8972622ca1fc"],"state_sha256":"255c2169e20360846e010148174c522f64831373728c882d2ab2300eec902977"}