{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:4IRDWZD7TOJ5FHZSOXRCP7QKE7","short_pith_number":"pith:4IRDWZD7","schema_version":"1.0","canonical_sha256":"e2223b647f9b93d29f3275e227fe0a27ce9c5a194708220b215ccc444ed2cc38","source":{"kind":"arxiv","id":"2504.11441","version":1},"attestation_state":"computed","paper":{"title":"TADACap: Time-series Adaptive Domain-Aware Captioning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.CV","authors_text":"Elizabeth Fons, Manuela Veloso, Rachneet Kaur, Soham Palande, Svitlana Vyetrenko, Tucker Balch, Zhen Zeng","submitted_at":"2025-04-15T17:54:59Z","abstract_excerpt":"While image captioning has gained significant attention, the potential of captioning time-series images, prevalent in areas like finance and healthcare, remains largely untapped. Existing time-series captioning methods typically offer generic, domain-agnostic descriptions of time-series shapes and struggle to adapt to new domains without substantial retraining. To address these limitations, we introduce TADACap, a retrieval-based framework to generate domain-aware captions for time-series images, capable of adapting to new domains without retraining. Building on TADACap, we propose a novel ret"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2504.11441","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-04-15T17:54:59Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"943bafa1a82f9d9579d1659cede4c13f97a3719da6afc19a3a89351aa1f2218b","abstract_canon_sha256":"a94fe9ae155f6aec0310275ff0a424039866edf32bf82651be087ce858073680"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:49:33.957591Z","signature_b64":"Jj2blxpbFEDjzif1k8tgvneWgficdpxKGHYS3hHshb3O8q4SBZOJABM6qFBnYs/aIF96UBOB2WgjSVgj+uQeCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e2223b647f9b93d29f3275e227fe0a27ce9c5a194708220b215ccc444ed2cc38","last_reissued_at":"2026-07-05T10:49:33.957112Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:49:33.957112Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TADACap: Time-series Adaptive Domain-Aware Captioning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.CV","authors_text":"Elizabeth Fons, Manuela Veloso, Rachneet Kaur, Soham Palande, Svitlana Vyetrenko, Tucker Balch, Zhen Zeng","submitted_at":"2025-04-15T17:54:59Z","abstract_excerpt":"While image captioning has gained significant attention, the potential of captioning time-series images, prevalent in areas like finance and healthcare, remains largely untapped. Existing time-series captioning methods typically offer generic, domain-agnostic descriptions of time-series shapes and struggle to adapt to new domains without substantial retraining. To address these limitations, we introduce TADACap, a retrieval-based framework to generate domain-aware captions for time-series images, capable of adapting to new domains without retraining. Building on TADACap, we propose a novel ret"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.11441","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/2504.11441/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2504.11441","created_at":"2026-07-05T10:49:33.957169+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.11441v1","created_at":"2026-07-05T10:49:33.957169+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.11441","created_at":"2026-07-05T10:49:33.957169+00:00"},{"alias_kind":"pith_short_12","alias_value":"4IRDWZD7TOJ5","created_at":"2026-07-05T10:49:33.957169+00:00"},{"alias_kind":"pith_short_16","alias_value":"4IRDWZD7TOJ5FHZS","created_at":"2026-07-05T10:49:33.957169+00:00"},{"alias_kind":"pith_short_8","alias_value":"4IRDWZD7","created_at":"2026-07-05T10:49:33.957169+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.26216","citing_title":"CyberChainBench: Can AI Agents Secure Smart Contracts Against Real-World On-Chain Vulnerabilities?","ref_index":30,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4IRDWZD7TOJ5FHZSOXRCP7QKE7","json":"https://pith.science/pith/4IRDWZD7TOJ5FHZSOXRCP7QKE7.json","graph_json":"https://pith.science/api/pith-number/4IRDWZD7TOJ5FHZSOXRCP7QKE7/graph.json","events_json":"https://pith.science/api/pith-number/4IRDWZD7TOJ5FHZSOXRCP7QKE7/events.json","paper":"https://pith.science/paper/4IRDWZD7"},"agent_actions":{"view_html":"https://pith.science/pith/4IRDWZD7TOJ5FHZSOXRCP7QKE7","download_json":"https://pith.science/pith/4IRDWZD7TOJ5FHZSOXRCP7QKE7.json","view_paper":"https://pith.science/paper/4IRDWZD7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.11441&json=true","fetch_graph":"https://pith.science/api/pith-number/4IRDWZD7TOJ5FHZSOXRCP7QKE7/graph.json","fetch_events":"https://pith.science/api/pith-number/4IRDWZD7TOJ5FHZSOXRCP7QKE7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4IRDWZD7TOJ5FHZSOXRCP7QKE7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4IRDWZD7TOJ5FHZSOXRCP7QKE7/action/storage_attestation","attest_author":"https://pith.science/pith/4IRDWZD7TOJ5FHZSOXRCP7QKE7/action/author_attestation","sign_citation":"https://pith.science/pith/4IRDWZD7TOJ5FHZSOXRCP7QKE7/action/citation_signature","submit_replication":"https://pith.science/pith/4IRDWZD7TOJ5FHZSOXRCP7QKE7/action/replication_record"}},"created_at":"2026-07-05T10:49:33.957169+00:00","updated_at":"2026-07-05T10:49:33.957169+00:00"}