{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:IQHACK4YXM3EUNVVHKVKKFY7R2","short_pith_number":"pith:IQHACK4Y","schema_version":"1.0","canonical_sha256":"440e012b98bb364a36b53aaaa5171f8ebf88bee822c6ac0b1ebba11122e9db43","source":{"kind":"arxiv","id":"2511.23276","version":2},"attestation_state":"computed","paper":{"title":"Auditable Context-Aware HFMD Forecasting with Structured LLM Agents","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MA"],"primary_cat":"cs.LG","authors_text":"Chen Xiong, Dongmei Yu, Gong Yunhan, Ji Jiansong, Joongwon Chae, Lian Zhang, Peiwu Qin, Runming Wang","submitted_at":"2025-11-28T15:29:26Z","abstract_excerpt":"Effective HFMD surveillance requires forecasts capturing both time-series patterns and contextual drivers such as school calendars, weather, and policy or surveillance reports. In clinical settings, forecasts must be trusted and actionable; thus, beyond point accuracy, decision-makers require concise, auditable explanations of why risk is expected to rise or fall. Classical models (e.g., ARIMA and Prophet) and foundation models (e.g., Chronos, Moirai, and TimesFM) treat external covariates as numerical inputs, lacking semantic reasoning to reflect epidemiological mechanisms or resolve conflict"},"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":"2511.23276","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-11-28T15:29:26Z","cross_cats_sorted":["cs.MA"],"title_canon_sha256":"f7c5a095a3f878424ab00bde2b90d4fb3f4daa4354bb75bea55d2321b8d6cd59","abstract_canon_sha256":"e85c555d856132f7211195a0db1f403910afbc088940bc6d3e12cf2fb0d1bc73"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-15T00:21:14.537004Z","signature_b64":"oJANAzjNTYw00gLzSe4cOvzsPABxOGKvKjtwhuLXHIKCRKGeASvhiWU5ve6Lro+imNtuLk+y3BlI0+Jd5T3sAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"440e012b98bb364a36b53aaaa5171f8ebf88bee822c6ac0b1ebba11122e9db43","last_reissued_at":"2026-07-15T00:21:14.535976Z","signature_status":"signed_v1","first_computed_at":"2026-07-15T00:21:14.535976Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Auditable Context-Aware HFMD Forecasting with Structured LLM Agents","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.MA"],"primary_cat":"cs.LG","authors_text":"Chen Xiong, Dongmei Yu, Gong Yunhan, Ji Jiansong, Joongwon Chae, Lian Zhang, Peiwu Qin, Runming Wang","submitted_at":"2025-11-28T15:29:26Z","abstract_excerpt":"Effective HFMD surveillance requires forecasts capturing both time-series patterns and contextual drivers such as school calendars, weather, and policy or surveillance reports. In clinical settings, forecasts must be trusted and actionable; thus, beyond point accuracy, decision-makers require concise, auditable explanations of why risk is expected to rise or fall. Classical models (e.g., ARIMA and Prophet) and foundation models (e.g., Chronos, Moirai, and TimesFM) treat external covariates as numerical inputs, lacking semantic reasoning to reflect epidemiological mechanisms or resolve conflict"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.23276","kind":"arxiv","version":2},"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/2511.23276/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":"2511.23276","created_at":"2026-07-15T00:21:14.536488+00:00"},{"alias_kind":"arxiv_version","alias_value":"2511.23276v2","created_at":"2026-07-15T00:21:14.536488+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.23276","created_at":"2026-07-15T00:21:14.536488+00:00"},{"alias_kind":"pith_short_12","alias_value":"IQHACK4YXM3E","created_at":"2026-07-15T00:21:14.536488+00:00"},{"alias_kind":"pith_short_16","alias_value":"IQHACK4YXM3EUNVV","created_at":"2026-07-15T00:21:14.536488+00:00"},{"alias_kind":"pith_short_8","alias_value":"IQHACK4Y","created_at":"2026-07-15T00:21:14.536488+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IQHACK4YXM3EUNVVHKVKKFY7R2","json":"https://pith.science/pith/IQHACK4YXM3EUNVVHKVKKFY7R2.json","graph_json":"https://pith.science/api/pith-number/IQHACK4YXM3EUNVVHKVKKFY7R2/graph.json","events_json":"https://pith.science/api/pith-number/IQHACK4YXM3EUNVVHKVKKFY7R2/events.json","paper":"https://pith.science/paper/IQHACK4Y"},"agent_actions":{"view_html":"https://pith.science/pith/IQHACK4YXM3EUNVVHKVKKFY7R2","download_json":"https://pith.science/pith/IQHACK4YXM3EUNVVHKVKKFY7R2.json","view_paper":"https://pith.science/paper/IQHACK4Y","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2511.23276&json=true","fetch_graph":"https://pith.science/api/pith-number/IQHACK4YXM3EUNVVHKVKKFY7R2/graph.json","fetch_events":"https://pith.science/api/pith-number/IQHACK4YXM3EUNVVHKVKKFY7R2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IQHACK4YXM3EUNVVHKVKKFY7R2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IQHACK4YXM3EUNVVHKVKKFY7R2/action/storage_attestation","attest_author":"https://pith.science/pith/IQHACK4YXM3EUNVVHKVKKFY7R2/action/author_attestation","sign_citation":"https://pith.science/pith/IQHACK4YXM3EUNVVHKVKKFY7R2/action/citation_signature","submit_replication":"https://pith.science/pith/IQHACK4YXM3EUNVVHKVKKFY7R2/action/replication_record"}},"created_at":"2026-07-15T00:21:14.536488+00:00","updated_at":"2026-07-15T00:21:14.536488+00:00"}