{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:WQVKFTPBQL5AG22TTMN3EFMWLA","short_pith_number":"pith:WQVKFTPB","schema_version":"1.0","canonical_sha256":"b42aa2cde182fa036b539b1bb215965828672361d83a03a2106df5dd5efdc52f","source":{"kind":"arxiv","id":"2507.19513","version":1},"attestation_state":"computed","paper":{"title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Andreas Fischer, Andreas Kassler, Khalid Ali, Zineddine Bettouche","submitted_at":"2025-07-17T22:48:46Z","abstract_excerpt":"Accurate spatiotemporal traffic forecasting is vital for intelligent resource management in 5G and beyond. However, conventional AI approaches often fail to capture the intricate spatial and temporal patterns that exist, due to e.g., the mobility of users. We introduce a lightweight, dual-path Spatiotemporal Network that leverages a Scalar LSTM (sLSTM) for efficient temporal modeling and a three-layer Conv3D module for spatial feature extraction. A fusion layer integrates both streams into a cohesive representation, enabling robust forecasting. Our design improves gradient stability and conver"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2507.19513","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-07-17T22:48:46Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"cb878266400ac40636ca5369b74714ed342eb9ff1dc31c915ef424be322fbc91","abstract_canon_sha256":"39dce80e1fd0cbaab78d179ab939062d2bb61af07d6b8d683380b08b5edec077"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:43:35.306446Z","signature_b64":"GntxhmGwUlAlk/O9rkCl9mqMNeanm4bd9GkWhUASScfg7WtCRaVSfQJZutor3Z7+q7pf+ssbby4jC9h3y7PzDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b42aa2cde182fa036b539b1bb215965828672361d83a03a2106df5dd5efdc52f","last_reissued_at":"2026-07-05T11:43:35.305867Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:43:35.305867Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Andreas Fischer, Andreas Kassler, Khalid Ali, Zineddine Bettouche","submitted_at":"2025-07-17T22:48:46Z","abstract_excerpt":"Accurate spatiotemporal traffic forecasting is vital for intelligent resource management in 5G and beyond. However, conventional AI approaches often fail to capture the intricate spatial and temporal patterns that exist, due to e.g., the mobility of users. We introduce a lightweight, dual-path Spatiotemporal Network that leverages a Scalar LSTM (sLSTM) for efficient temporal modeling and a three-layer Conv3D module for spatial feature extraction. A fusion layer integrates both streams into a cohesive representation, enabling robust forecasting. Our design improves gradient stability and conver"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.19513","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/2507.19513/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2507.19513","created_at":"2026-07-05T11:43:35.305935+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.19513v1","created_at":"2026-07-05T11:43:35.305935+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.19513","created_at":"2026-07-05T11:43:35.305935+00:00"},{"alias_kind":"pith_short_12","alias_value":"WQVKFTPBQL5A","created_at":"2026-07-05T11:43:35.305935+00:00"},{"alias_kind":"pith_short_16","alias_value":"WQVKFTPBQL5AG22T","created_at":"2026-07-05T11:43:35.305935+00:00"},{"alias_kind":"pith_short_8","alias_value":"WQVKFTPB","created_at":"2026-07-05T11:43:35.305935+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WQVKFTPBQL5AG22TTMN3EFMWLA","json":"https://pith.science/pith/WQVKFTPBQL5AG22TTMN3EFMWLA.json","graph_json":"https://pith.science/api/pith-number/WQVKFTPBQL5AG22TTMN3EFMWLA/graph.json","events_json":"https://pith.science/api/pith-number/WQVKFTPBQL5AG22TTMN3EFMWLA/events.json","paper":"https://pith.science/paper/WQVKFTPB"},"agent_actions":{"view_html":"https://pith.science/pith/WQVKFTPBQL5AG22TTMN3EFMWLA","download_json":"https://pith.science/pith/WQVKFTPBQL5AG22TTMN3EFMWLA.json","view_paper":"https://pith.science/paper/WQVKFTPB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.19513&json=true","fetch_graph":"https://pith.science/api/pith-number/WQVKFTPBQL5AG22TTMN3EFMWLA/graph.json","fetch_events":"https://pith.science/api/pith-number/WQVKFTPBQL5AG22TTMN3EFMWLA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WQVKFTPBQL5AG22TTMN3EFMWLA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WQVKFTPBQL5AG22TTMN3EFMWLA/action/storage_attestation","attest_author":"https://pith.science/pith/WQVKFTPBQL5AG22TTMN3EFMWLA/action/author_attestation","sign_citation":"https://pith.science/pith/WQVKFTPBQL5AG22TTMN3EFMWLA/action/citation_signature","submit_replication":"https://pith.science/pith/WQVKFTPBQL5AG22TTMN3EFMWLA/action/replication_record"}},"created_at":"2026-07-05T11:43:35.305935+00:00","updated_at":"2026-07-05T11:43:35.305935+00:00"}