{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:YH7UGQ6XQ6HCV3I7MG76WTTP3R","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":"894258c264bbb2344ea2885e312a54729918cdecb631762e8904438ff496aa1e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2026-03-05T07:02:17Z","title_canon_sha256":"f42dc724ce0570971e582e14bc9a0c024350ae0065cff0d388be3edd4f7f93cc"},"schema_version":"1.0","source":{"id":"2603.04873","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2603.04873","created_at":"2026-06-29T00:14:02Z"},{"alias_kind":"arxiv_version","alias_value":"2603.04873v3","created_at":"2026-06-29T00:14:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2603.04873","created_at":"2026-06-29T00:14:02Z"},{"alias_kind":"pith_short_12","alias_value":"YH7UGQ6XQ6HC","created_at":"2026-06-29T00:14:02Z"},{"alias_kind":"pith_short_16","alias_value":"YH7UGQ6XQ6HCV3I7","created_at":"2026-06-29T00:14:02Z"},{"alias_kind":"pith_short_8","alias_value":"YH7UGQ6X","created_at":"2026-06-29T00:14:02Z"}],"graph_snapshots":[{"event_id":"sha256:2a6b3217e91cab4ced156b61016a2da03e9757d4de339c183f931fb7ef14dfe2","target":"graph","created_at":"2026-06-29T00:14:02Z","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/2603.04873/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accurate time series forecasting underpins decision-making in many domains, yetconventional ML development often faces data scarcity, distribution shift, anddiminishing returns from manual iteration. We propose Self-Evolving Agent forTime Series Algorithms (SEATS), a framework that autonomously generates, val-idates, and optimizes forecasting algorithm code through an iterative self-evolutionloop. Our design combines three mechanisms: (1) Metric-Advantage MCTS(MA-MCTS), which replaces fixed rewards with a statistically normalized advan-tage score for search guidance, (2) code review with runni","authors_text":"Longkun Xu, Qiantu Tuo, Rui Li, Xiaochun Zhang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2026-03-05T07:02:17Z","title":"SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2603.04873","kind":"arxiv","version":3},"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:b69d9a2c28ad28d405edc4dfe2ad6fa0cefcaf7ae045932069e847c254a09be4","target":"record","created_at":"2026-06-29T00:14:02Z","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":"894258c264bbb2344ea2885e312a54729918cdecb631762e8904438ff496aa1e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2026-03-05T07:02:17Z","title_canon_sha256":"f42dc724ce0570971e582e14bc9a0c024350ae0065cff0d388be3edd4f7f93cc"},"schema_version":"1.0","source":{"id":"2603.04873","kind":"arxiv","version":3}},"canonical_sha256":"c1ff4343d7878e2aed1f61bfeb4e6fdc7a620a273942d995b878fd738f8921a9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c1ff4343d7878e2aed1f61bfeb4e6fdc7a620a273942d995b878fd738f8921a9","first_computed_at":"2026-06-29T00:14:02.562380Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-29T00:14:02.562380Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Mg5hAvVbjkqr+MYo43NTq8OHRKoYklFM9USghiIprJf8Afw9yhZvBFfbo2+9429Nv3T+DY2ktL10L67glp+FAQ==","signature_status":"signed_v1","signed_at":"2026-06-29T00:14:02.563045Z","signed_message":"canonical_sha256_bytes"},"source_id":"2603.04873","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b69d9a2c28ad28d405edc4dfe2ad6fa0cefcaf7ae045932069e847c254a09be4","sha256:2a6b3217e91cab4ced156b61016a2da03e9757d4de339c183f931fb7ef14dfe2"],"state_sha256":"82d6e467e7939625e0a1ed39f0862e1f81719daaa777f360144c2fd81afda47b"}