{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:WIMLQNM5EMJZOAVRCZAZWQHC26","short_pith_number":"pith:WIMLQNM5","schema_version":"1.0","canonical_sha256":"b218b8359d23139702b116419b40e2d794278deae347972f63d85907b1290f66","source":{"kind":"arxiv","id":"2601.13632","version":2},"attestation_state":"computed","paper":{"title":"Resilient Routing: Risk-Aware Dynamic Routing in Smart Logistics via Spatiotemporal Graph Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Sichen Zhao, Xianling Zeng, Yalun Qi, Zhiming Xue, Zihan Yu","submitted_at":"2026-01-20T06:06:35Z","abstract_excerpt":"With the rapid development of the e-commerce industry, the logistics network is experiencing unprecedented pressure. The traditional static routing strategy most time cannot tolerate the traffic congestion and fluctuating retail demand. In this paper, we propose a Risk-Aware Dynamic Routing(RADR) framework which integrates Spatiotemporal Graph Neural Networks (ST-GNN) with combinatorial optimization. We first construct a logistics topology graph by using the discrete GPS data using spatial clustering methods. Subsequently, a hybrid deep learning model combining Graph Convolutional Network (GCN"},"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":"2601.13632","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-01-20T06:06:35Z","cross_cats_sorted":[],"title_canon_sha256":"ce87a2d24bcf3c5594862f5385f92525f065ad84537f2d605fad50c8fa6d1e96","abstract_canon_sha256":"566ae0235f82b226b144ccc159f8aa9bec8367c822c9c8749cc819c10864fc61"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-26T01:15:17.046676Z","signature_b64":"2FxUXRqaJJyJed7GjFqND282EXo7klUIZB917l/j9V/eVyrbzmA/EP/rJR/wOFxIa0M0xpds1VmtmYzLZybwBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b218b8359d23139702b116419b40e2d794278deae347972f63d85907b1290f66","last_reissued_at":"2026-06-26T01:15:17.046183Z","signature_status":"signed_v1","first_computed_at":"2026-06-26T01:15:17.046183Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Resilient Routing: Risk-Aware Dynamic Routing in Smart Logistics via Spatiotemporal Graph Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Sichen Zhao, Xianling Zeng, Yalun Qi, Zhiming Xue, Zihan Yu","submitted_at":"2026-01-20T06:06:35Z","abstract_excerpt":"With the rapid development of the e-commerce industry, the logistics network is experiencing unprecedented pressure. The traditional static routing strategy most time cannot tolerate the traffic congestion and fluctuating retail demand. In this paper, we propose a Risk-Aware Dynamic Routing(RADR) framework which integrates Spatiotemporal Graph Neural Networks (ST-GNN) with combinatorial optimization. We first construct a logistics topology graph by using the discrete GPS data using spatial clustering methods. Subsequently, a hybrid deep learning model combining Graph Convolutional Network (GCN"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.13632","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/2601.13632/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":"2601.13632","created_at":"2026-06-26T01:15:17.046241+00:00"},{"alias_kind":"arxiv_version","alias_value":"2601.13632v2","created_at":"2026-06-26T01:15:17.046241+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.13632","created_at":"2026-06-26T01:15:17.046241+00:00"},{"alias_kind":"pith_short_12","alias_value":"WIMLQNM5EMJZ","created_at":"2026-06-26T01:15:17.046241+00:00"},{"alias_kind":"pith_short_16","alias_value":"WIMLQNM5EMJZOAVR","created_at":"2026-06-26T01:15:17.046241+00:00"},{"alias_kind":"pith_short_8","alias_value":"WIMLQNM5","created_at":"2026-06-26T01:15:17.046241+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":7,"sample":[{"citing_arxiv_id":"2605.23071","citing_title":"The Efficiency Frontier: A Unified Framework for Cost-Performance Optimization in LLM Context Management","ref_index":36,"is_internal_anchor":true},{"citing_arxiv_id":"2605.23071","citing_title":"The Efficiency Frontier: A Unified Framework for Cost-Performance Optimization in LLM Context Management","ref_index":36,"is_internal_anchor":true},{"citing_arxiv_id":"2603.26815","citing_title":"Sustainable Hybrid Document-Routed Retrieval for Financial RAG: Resolving the Robustness-Precision Trade-off","ref_index":18,"is_internal_anchor":true},{"citing_arxiv_id":"2605.04479","citing_title":"ESG as Priced Crash Insurance: State-Dependent Tail Risk and Deconfounding Evidence","ref_index":13,"is_internal_anchor":true},{"citing_arxiv_id":"2605.00351","citing_title":"Hypergraph and Latent ODE Learning for Multimodal Root Cause Localization in Microservices","ref_index":4,"is_internal_anchor":true},{"citing_arxiv_id":"2604.05254","citing_title":"EAGLE: Edge-Aware Graph Learning for Proactive Delivery Delay Prediction in Smart Logistics Networks","ref_index":3,"is_internal_anchor":true},{"citing_arxiv_id":"2604.05465","citing_title":"Adaptive Serverless Resource Management via Slot-Survival Prediction and Event-Driven Lifecycle Control","ref_index":6,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WIMLQNM5EMJZOAVRCZAZWQHC26","json":"https://pith.science/pith/WIMLQNM5EMJZOAVRCZAZWQHC26.json","graph_json":"https://pith.science/api/pith-number/WIMLQNM5EMJZOAVRCZAZWQHC26/graph.json","events_json":"https://pith.science/api/pith-number/WIMLQNM5EMJZOAVRCZAZWQHC26/events.json","paper":"https://pith.science/paper/WIMLQNM5"},"agent_actions":{"view_html":"https://pith.science/pith/WIMLQNM5EMJZOAVRCZAZWQHC26","download_json":"https://pith.science/pith/WIMLQNM5EMJZOAVRCZAZWQHC26.json","view_paper":"https://pith.science/paper/WIMLQNM5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2601.13632&json=true","fetch_graph":"https://pith.science/api/pith-number/WIMLQNM5EMJZOAVRCZAZWQHC26/graph.json","fetch_events":"https://pith.science/api/pith-number/WIMLQNM5EMJZOAVRCZAZWQHC26/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WIMLQNM5EMJZOAVRCZAZWQHC26/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WIMLQNM5EMJZOAVRCZAZWQHC26/action/storage_attestation","attest_author":"https://pith.science/pith/WIMLQNM5EMJZOAVRCZAZWQHC26/action/author_attestation","sign_citation":"https://pith.science/pith/WIMLQNM5EMJZOAVRCZAZWQHC26/action/citation_signature","submit_replication":"https://pith.science/pith/WIMLQNM5EMJZOAVRCZAZWQHC26/action/replication_record"}},"created_at":"2026-06-26T01:15:17.046241+00:00","updated_at":"2026-06-26T01:15:17.046241+00:00"}