{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:K2IGNKA5REHDLMSQSXJKU3EDXB","short_pith_number":"pith:K2IGNKA5","canonical_record":{"source":{"id":"2505.19620","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-26T07:37:39Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"64cb55b024ef36639df040e0e1b666af0988e017ba22c56c689d86a6c760d2b7","abstract_canon_sha256":"94d0961fffba6146a0a6dd1e1eec2f82cd9865012d74a7018b5100ae04d4696e"},"schema_version":"1.0"},"canonical_sha256":"569066a81d890e35b25095d2aa6c83b8534f5665c487d240ce211a16bf9e6acc","source":{"kind":"arxiv","id":"2505.19620","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.19620","created_at":"2026-07-05T11:09:33Z"},{"alias_kind":"arxiv_version","alias_value":"2505.19620v1","created_at":"2026-07-05T11:09:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.19620","created_at":"2026-07-05T11:09:33Z"},{"alias_kind":"pith_short_12","alias_value":"K2IGNKA5REHD","created_at":"2026-07-05T11:09:33Z"},{"alias_kind":"pith_short_16","alias_value":"K2IGNKA5REHDLMSQ","created_at":"2026-07-05T11:09:33Z"},{"alias_kind":"pith_short_8","alias_value":"K2IGNKA5","created_at":"2026-07-05T11:09:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:K2IGNKA5REHDLMSQSXJKU3EDXB","target":"record","payload":{"canonical_record":{"source":{"id":"2505.19620","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-26T07:37:39Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"64cb55b024ef36639df040e0e1b666af0988e017ba22c56c689d86a6c760d2b7","abstract_canon_sha256":"94d0961fffba6146a0a6dd1e1eec2f82cd9865012d74a7018b5100ae04d4696e"},"schema_version":"1.0"},"canonical_sha256":"569066a81d890e35b25095d2aa6c83b8534f5665c487d240ce211a16bf9e6acc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:09:33.616525Z","signature_b64":"fXcfPcbGVNRI133zskfAvJkXJ22mjVPf9MgwUS2vM0iX8P4dcjcCwj3HPzs66ejPU4aJUUpjaYHONrhsmzupDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"569066a81d890e35b25095d2aa6c83b8534f5665c487d240ce211a16bf9e6acc","last_reissued_at":"2026-07-05T11:09:33.616073Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:09:33.616073Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.19620","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:09:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wN3qWXtyb4bNwndyZ02ZIaqcXWI3RfLiB+EaOjAQ31jepoGdsMi2tsAAggUniJDpy4uH9D/x0lSmNiCH+EAQDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T22:05:02.119098Z"},"content_sha256":"183a24d8393b0e1d9bd50ee1201321b7cbe817651d63ecd0cca1db06d8ad2f3c","schema_version":"1.0","event_id":"sha256:183a24d8393b0e1d9bd50ee1201321b7cbe817651d63ecd0cca1db06d8ad2f3c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:K2IGNKA5REHDLMSQSXJKU3EDXB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Decoupling Spatio-Temporal Prediction: When Lightweight Large Models Meet Adaptive Hypergraphs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Duxin Chen, Jiawen Chen, Qi Shao, Wenwu Yu","submitted_at":"2025-05-26T07:37:39Z","abstract_excerpt":"Spatio-temporal prediction is a pivotal task with broad applications in traffic management, climate monitoring, energy scheduling, etc. However, existing methodologies often struggle to balance model expressiveness and computational efficiency, especially when scaling to large real-world datasets. To tackle these challenges, we propose STH-SepNet (Spatio-Temporal Hypergraph Separation Networks), a novel framework that decouples temporal and spatial modeling to enhance both efficiency and precision. Therein, the temporal dimension is modeled using lightweight large language models, which effect"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.19620","kind":"arxiv","version":1},"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/2505.19620/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":"2026-07-05T11:09:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WoOJtg6wtSBsS1Sp9U7gmVwipBLnnU3OAHaJrr9azHvxaaIRMVZttYvis7/3islrnGDELQrMKIX2rNOIqkR8Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T22:05:02.119606Z"},"content_sha256":"9696351af539ecda1ff240f14598c950ecce6b56f05d47077dd030fa485d0424","schema_version":"1.0","event_id":"sha256:9696351af539ecda1ff240f14598c950ecce6b56f05d47077dd030fa485d0424"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/K2IGNKA5REHDLMSQSXJKU3EDXB/bundle.json","state_url":"https://pith.science/pith/K2IGNKA5REHDLMSQSXJKU3EDXB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/K2IGNKA5REHDLMSQSXJKU3EDXB/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":"2026-08-07T22:05:02Z","links":{"resolver":"https://pith.science/pith/K2IGNKA5REHDLMSQSXJKU3EDXB","bundle":"https://pith.science/pith/K2IGNKA5REHDLMSQSXJKU3EDXB/bundle.json","state":"https://pith.science/pith/K2IGNKA5REHDLMSQSXJKU3EDXB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/K2IGNKA5REHDLMSQSXJKU3EDXB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:K2IGNKA5REHDLMSQSXJKU3EDXB","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":"94d0961fffba6146a0a6dd1e1eec2f82cd9865012d74a7018b5100ae04d4696e","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-26T07:37:39Z","title_canon_sha256":"64cb55b024ef36639df040e0e1b666af0988e017ba22c56c689d86a6c760d2b7"},"schema_version":"1.0","source":{"id":"2505.19620","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.19620","created_at":"2026-07-05T11:09:33Z"},{"alias_kind":"arxiv_version","alias_value":"2505.19620v1","created_at":"2026-07-05T11:09:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.19620","created_at":"2026-07-05T11:09:33Z"},{"alias_kind":"pith_short_12","alias_value":"K2IGNKA5REHD","created_at":"2026-07-05T11:09:33Z"},{"alias_kind":"pith_short_16","alias_value":"K2IGNKA5REHDLMSQ","created_at":"2026-07-05T11:09:33Z"},{"alias_kind":"pith_short_8","alias_value":"K2IGNKA5","created_at":"2026-07-05T11:09:33Z"}],"graph_snapshots":[{"event_id":"sha256:9696351af539ecda1ff240f14598c950ecce6b56f05d47077dd030fa485d0424","target":"graph","created_at":"2026-07-05T11:09:33Z","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/2505.19620/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Spatio-temporal prediction is a pivotal task with broad applications in traffic management, climate monitoring, energy scheduling, etc. However, existing methodologies often struggle to balance model expressiveness and computational efficiency, especially when scaling to large real-world datasets. To tackle these challenges, we propose STH-SepNet (Spatio-Temporal Hypergraph Separation Networks), a novel framework that decouples temporal and spatial modeling to enhance both efficiency and precision. Therein, the temporal dimension is modeled using lightweight large language models, which effect","authors_text":"Duxin Chen, Jiawen Chen, Qi Shao, Wenwu Yu","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-26T07:37:39Z","title":"Decoupling Spatio-Temporal Prediction: When Lightweight Large Models Meet Adaptive Hypergraphs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.19620","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":"sha256:183a24d8393b0e1d9bd50ee1201321b7cbe817651d63ecd0cca1db06d8ad2f3c","target":"record","created_at":"2026-07-05T11:09:33Z","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":"94d0961fffba6146a0a6dd1e1eec2f82cd9865012d74a7018b5100ae04d4696e","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-26T07:37:39Z","title_canon_sha256":"64cb55b024ef36639df040e0e1b666af0988e017ba22c56c689d86a6c760d2b7"},"schema_version":"1.0","source":{"id":"2505.19620","kind":"arxiv","version":1}},"canonical_sha256":"569066a81d890e35b25095d2aa6c83b8534f5665c487d240ce211a16bf9e6acc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"569066a81d890e35b25095d2aa6c83b8534f5665c487d240ce211a16bf9e6acc","first_computed_at":"2026-07-05T11:09:33.616073Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:09:33.616073Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fXcfPcbGVNRI133zskfAvJkXJ22mjVPf9MgwUS2vM0iX8P4dcjcCwj3HPzs66ejPU4aJUUpjaYHONrhsmzupDw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:09:33.616525Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.19620","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:183a24d8393b0e1d9bd50ee1201321b7cbe817651d63ecd0cca1db06d8ad2f3c","sha256:9696351af539ecda1ff240f14598c950ecce6b56f05d47077dd030fa485d0424"],"state_sha256":"5d8dc5f5c6e4aa901d3fcdabbc440d92688bea015d804bf9744d816d118a1044"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gPYB+vgha/PKzaTkCE5U6W9kW4wsl/gJlO1hMkXH+FLyQ4WUwFepj1zTRslgPDKmn1hZ5VFKdk9c1fqyzrgMDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T22:05:02.123768Z","bundle_sha256":"6d48440053813d59dda2bad8a502f38bc9a6b47120b708d90ffc02ec73701aaa"}}