{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:JLHQ2N26K4MN5XTMB65AWIMKUF","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":"43bb4c7f34db02a842f5b5819ae30f6d4d6425a1448511f5ae8b0914f6315177","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-05T14:01:58Z","title_canon_sha256":"a4fc910af63375b932a3b3f8f86c7ef3f38c271fb656385493a632fd37865201"},"schema_version":"1.0","source":{"id":"2606.07291","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.07291","created_at":"2026-06-08T01:05:17Z"},{"alias_kind":"arxiv_version","alias_value":"2606.07291v1","created_at":"2026-06-08T01:05:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.07291","created_at":"2026-06-08T01:05:17Z"},{"alias_kind":"pith_short_12","alias_value":"JLHQ2N26K4MN","created_at":"2026-06-08T01:05:17Z"},{"alias_kind":"pith_short_16","alias_value":"JLHQ2N26K4MN5XTM","created_at":"2026-06-08T01:05:17Z"},{"alias_kind":"pith_short_8","alias_value":"JLHQ2N26","created_at":"2026-06-08T01:05:17Z"}],"graph_snapshots":[{"event_id":"sha256:74cce71058e6b2b8123bb7d43c5dbde3e94fb2ed031afee5281ea42167e4a661","target":"graph","created_at":"2026-06-08T01:05:17Z","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/2606.07291/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multivariate time-series forecasting requires models to reason over temporal dynamics, cross-variable dependencies, and historical input-output correspondences. Recent Prior-Data Fitted Networks (PFNs) suggest that synthetic tasks can be useful for learning transferable inference behavior. However, directly transferring this paradigm to time-series forecasting remains difficult, since temporal order, dynamic lags, and recurring historical patterns are not naturally captured by ordinary tabular priors. Motivated by this observation, we propose Trio, a sample-aware time-series forecasting archit","authors_text":"Chunlei Peng, Decheng Liu, Dongjing Wang, Hengwei He, Hongda Li, Tao Chen, Wenyue Ding, Xin Zhang, Yexu Zhou, Zheng Chen, Zhewei Chen, Zhi Gong","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-05T14:01:58Z","title":"Trio: Learning Time-Series Forecasting with Temporal-Spatial-Sample Attention and Structural Causal Priors"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.07291","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":"sha256:33deb923188f390839a527ca78935a27561a00081119262af3e2ca77767ad925","target":"record","created_at":"2026-06-08T01:05:17Z","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":"43bb4c7f34db02a842f5b5819ae30f6d4d6425a1448511f5ae8b0914f6315177","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-05T14:01:58Z","title_canon_sha256":"a4fc910af63375b932a3b3f8f86c7ef3f38c271fb656385493a632fd37865201"},"schema_version":"1.0","source":{"id":"2606.07291","kind":"arxiv","version":1}},"canonical_sha256":"4acf0d375e5718dede6c0fba0b218aa143e8a3446df9ab737d7316745ed44098","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4acf0d375e5718dede6c0fba0b218aa143e8a3446df9ab737d7316745ed44098","first_computed_at":"2026-06-08T01:05:17.879448Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-08T01:05:17.879448Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TK8ULlEeHqGu4lJmtvQD6P7vqAqC9xwI9EOwtDdH1LQyPBka9kXloRN+ttNHrLQp9SK0moLF/mtiWX6d85/MDQ==","signature_status":"signed_v1","signed_at":"2026-06-08T01:05:17.880237Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.07291","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:33deb923188f390839a527ca78935a27561a00081119262af3e2ca77767ad925","sha256:74cce71058e6b2b8123bb7d43c5dbde3e94fb2ed031afee5281ea42167e4a661"],"state_sha256":"52ebc8611d7eadb5f3941feda5f1d64ec3aae9a2f9610428a800d2cc77ce4a3b"}