{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:2WHHFKQORAF5NXKUDMFN5XYCG7","short_pith_number":"pith:2WHHFKQO","canonical_record":{"source":{"id":"2510.03244","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-09-25T14:02:26Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"278479586ae777b2370584a407e6b6f485fd3849064f73b63fa24f153ef25533","abstract_canon_sha256":"c1d7729f0f988c636402a500558f9bd53ff5d1bc20e07adffce36027ef459688"},"schema_version":"1.0"},"canonical_sha256":"d58e72aa0e880bd6dd541b0adedf0237c0e9cbcf86e3af5f256c696911f11ccc","source":{"kind":"arxiv","id":"2510.03244","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"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:2WHHFKQORAF5NXKUDMFN5XYCG7","target":"record","payload":{"canonical_record":{"source":{"id":"2510.03244","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-09-25T14:02:26Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"278479586ae777b2370584a407e6b6f485fd3849064f73b63fa24f153ef25533","abstract_canon_sha256":"c1d7729f0f988c636402a500558f9bd53ff5d1bc20e07adffce36027ef459688"},"schema_version":"1.0"},"canonical_sha256":"d58e72aa0e880bd6dd541b0adedf0237c0e9cbcf86e3af5f256c696911f11ccc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T02:07:10.432841Z","signature_b64":"jm2U0fBCbDBvKasT1oHnSjbq3BcrpbSBmTjQbrT6PimXqLO018lfb+4BkWfAasRhMfFfp7m3I4vIAp4RrQM6Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d58e72aa0e880bd6dd541b0adedf0237c0e9cbcf86e3af5f256c696911f11ccc","last_reissued_at":"2026-06-09T02:07:10.431965Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T02:07:10.431965Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2510.03244","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-09T02:07:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yTFXBK5KPk9f/Zk/2pP86HnAKGtB0DubqKKQKk8boQ65+gbvy/5L/iCo2PEmGRlfxtsuR4asOuROJVbWo2XwCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T16:33:21.251651Z"},"content_sha256":"7fe60bff220f69c204b4d8bf158051fa93dd3102f468313091435958abd59803","schema_version":"1.0","event_id":"sha256:7fe60bff220f69c204b4d8bf158051fa93dd3102f468313091435958abd59803"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:2WHHFKQORAF5NXKUDMFN5XYCG7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"VFEM: Visual Feature Empowered Multivariate Time Series Forecasting with Cross-Modal Fusion","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","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","submitted_at":"2025-09-25T14:02:26Z","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"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.03244","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/2510.03244/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-09T02:07:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2X+ZO2TXizq6PWvIX/UiNZtyfEg+XJnl1gQKu12wDI+aOJf9jEmQOl2dlfIR6o9tjhgbb76B70rYovr69K58CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T16:33:21.252473Z"},"content_sha256":"301aba56fa459c1a73ac0ee268f443ae72519c597cb159736b1ac7fab718d1fe","schema_version":"1.0","event_id":"sha256:301aba56fa459c1a73ac0ee268f443ae72519c597cb159736b1ac7fab718d1fe"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2WHHFKQORAF5NXKUDMFN5XYCG7/bundle.json","state_url":"https://pith.science/pith/2WHHFKQORAF5NXKUDMFN5XYCG7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2WHHFKQORAF5NXKUDMFN5XYCG7/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-11T16:33:21Z","links":{"resolver":"https://pith.science/pith/2WHHFKQORAF5NXKUDMFN5XYCG7","bundle":"https://pith.science/pith/2WHHFKQORAF5NXKUDMFN5XYCG7/bundle.json","state":"https://pith.science/pith/2WHHFKQORAF5NXKUDMFN5XYCG7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2WHHFKQORAF5NXKUDMFN5XYCG7/bundle.json"},"state":{"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"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HZ+S8r3V15zz1hw2b00MGiJoxUUWzZWP/a8y1IeNCjLz1NgjPyAI9l7hcQrmTNeMB0Y8TbsbFVIreHHd8nI2Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T16:33:21.257372Z","bundle_sha256":"a098d3713b04617f1de497191f5e102f18a52054afa8926b77a131353dede4e8"}}