{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:2WHHFKQORAF5NXKUDMFN5XYCG7","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":"c1d7729f0f988c636402a500558f9bd53ff5d1bc20e07adffce36027ef459688","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-09-25T14:02:26Z","title_canon_sha256":"278479586ae777b2370584a407e6b6f485fd3849064f73b63fa24f153ef25533"},"schema_version":"1.0","source":{"id":"2510.03244","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2510.03244","created_at":"2026-06-09T02:07:10Z"},{"alias_kind":"arxiv_version","alias_value":"2510.03244v2","created_at":"2026-06-09T02:07:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.03244","created_at":"2026-06-09T02:07:10Z"},{"alias_kind":"pith_short_12","alias_value":"2WHHFKQORAF5","created_at":"2026-06-09T02:07:10Z"},{"alias_kind":"pith_short_16","alias_value":"2WHHFKQORAF5NXKU","created_at":"2026-06-09T02:07:10Z"},{"alias_kind":"pith_short_8","alias_value":"2WHHFKQO","created_at":"2026-06-09T02:07:10Z"}],"graph_snapshots":[{"event_id":"sha256:301aba56fa459c1a73ac0ee268f443ae72519c597cb159736b1ac7fab718d1fe","target":"graph","created_at":"2026-06-09T02:07:10Z","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/2510.03244/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large time series foundation models often adopt channel-independent architectures to handle varying data dimensions, but this design ignores crucial cross-channel dependencies. Meanwhile, existing cross-modal methods predominantly rely on textual modalities, leaving the spatial pattern recognition capabilities of vision models underexplored for time series analysis. To address these limitations, we propose VFEM, a cross-modal forecasting model that leverages pre-trained large vision models (LVMs) to capture complex cross-variable patterns. VFEM transforms multivariate time series into visual r","authors_text":"Danny Dongning Sun, Fei Ma, Hang Yu, Hongkang Zhang, Jian Xu, Shao-Lun Huang, Tongtong Feng, Xiao-Ping Zhang, Yanlong Wang, Zijian Zhang","cross_cats":["cs.AI","cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-09-25T14:02:26Z","title":"VFEM: Visual Feature Empowered Multivariate Time Series Forecasting with Cross-Modal Fusion"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.03244","kind":"arxiv","version":2},"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":"sha256:7fe60bff220f69c204b4d8bf158051fa93dd3102f468313091435958abd59803","target":"record","created_at":"2026-06-09T02:07:10Z","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":"c1d7729f0f988c636402a500558f9bd53ff5d1bc20e07adffce36027ef459688","cross_cats_sorted":["cs.AI","cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-09-25T14:02:26Z","title_canon_sha256":"278479586ae777b2370584a407e6b6f485fd3849064f73b63fa24f153ef25533"},"schema_version":"1.0","source":{"id":"2510.03244","kind":"arxiv","version":2}},"canonical_sha256":"d58e72aa0e880bd6dd541b0adedf0237c0e9cbcf86e3af5f256c696911f11ccc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d58e72aa0e880bd6dd541b0adedf0237c0e9cbcf86e3af5f256c696911f11ccc","first_computed_at":"2026-06-09T02:07:10.431965Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-09T02:07:10.431965Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jm2U0fBCbDBvKasT1oHnSjbq3BcrpbSBmTjQbrT6PimXqLO018lfb+4BkWfAasRhMfFfp7m3I4vIAp4RrQM6Bw==","signature_status":"signed_v1","signed_at":"2026-06-09T02:07:10.432841Z","signed_message":"canonical_sha256_bytes"},"source_id":"2510.03244","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7fe60bff220f69c204b4d8bf158051fa93dd3102f468313091435958abd59803","sha256:301aba56fa459c1a73ac0ee268f443ae72519c597cb159736b1ac7fab718d1fe"],"state_sha256":"b3eb64b54e01420699a8dff8ace485e5262a29e9e240a20b39cdd6647021452c"}