{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:CXYWJANAKT2YMFP4QN66B6CAB5","short_pith_number":"pith:CXYWJANA","schema_version":"1.0","canonical_sha256":"15f16481a054f58615fc837de0f8400f454f12a5d6af3aaf3e6a14468ffacda4","source":{"kind":"arxiv","id":"2505.13102","version":4},"attestation_state":"computed","paper":{"title":"Lightweight and Interpretable Transformer via Mixed Graph Algorithm Unrolling for Traffic Forecast","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","eess.SP"],"primary_cat":"cs.LG","authors_text":"Gene Cheung, H. Vicky Zhao, Ji Qi, Mingxiao Liu, Tam Thuc Do, Yuzhe Li, Zhuoshi Pan","submitted_at":"2025-05-19T13:32:34Z","abstract_excerpt":"Unlike conventional \"black-box\" transformers with classical self-attention mechanism, we build a lightweight and interpretable transformer-like neural net by unrolling a mixed-graph-based optimization algorithm to forecast traffic with spatial and temporal dimensions. We construct two graphs: an undirected graph $\\mathcal{G}^u$ capturing spatial correlations across geography, and a directed graph $\\mathcal{G}^d$ capturing sequential relationships over time. We predict future samples of signal $\\mathbf{x}$, assuming it is \"smooth\" with respect to both $\\mathcal{G}^u$ and $\\mathcal{G}^d$, where "},"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":"2505.13102","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-05-19T13:32:34Z","cross_cats_sorted":["cs.AI","eess.SP"],"title_canon_sha256":"d7387979d83051fbf3db756e5b718120a1bb818309d11b03d37380685cce9737","abstract_canon_sha256":"caf882573af3970ff48dd74aae0dab0c8e729b3311ac5b2a02e103fe71e96199"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-12T01:08:13.975396Z","signature_b64":"UyjdUwsCcLLkem9UzX72WjBvZ/i7hu+hbO7AZHjr+Y1WDvCLlrNoaxi2NFqyCFFy4jclXbWfWjZKLcz12KUFAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"15f16481a054f58615fc837de0f8400f454f12a5d6af3aaf3e6a14468ffacda4","last_reissued_at":"2026-06-12T01:08:13.974187Z","signature_status":"signed_v1","first_computed_at":"2026-06-12T01:08:13.974187Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Lightweight and Interpretable Transformer via Mixed Graph Algorithm Unrolling for Traffic Forecast","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","eess.SP"],"primary_cat":"cs.LG","authors_text":"Gene Cheung, H. Vicky Zhao, Ji Qi, Mingxiao Liu, Tam Thuc Do, Yuzhe Li, Zhuoshi Pan","submitted_at":"2025-05-19T13:32:34Z","abstract_excerpt":"Unlike conventional \"black-box\" transformers with classical self-attention mechanism, we build a lightweight and interpretable transformer-like neural net by unrolling a mixed-graph-based optimization algorithm to forecast traffic with spatial and temporal dimensions. We construct two graphs: an undirected graph $\\mathcal{G}^u$ capturing spatial correlations across geography, and a directed graph $\\mathcal{G}^d$ capturing sequential relationships over time. We predict future samples of signal $\\mathbf{x}$, assuming it is \"smooth\" with respect to both $\\mathcal{G}^u$ and $\\mathcal{G}^d$, where "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.13102","kind":"arxiv","version":4},"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/2505.13102/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":"2505.13102","created_at":"2026-06-12T01:08:13.974358+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.13102v4","created_at":"2026-06-12T01:08:13.974358+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.13102","created_at":"2026-06-12T01:08:13.974358+00:00"},{"alias_kind":"pith_short_12","alias_value":"CXYWJANAKT2Y","created_at":"2026-06-12T01:08:13.974358+00:00"},{"alias_kind":"pith_short_16","alias_value":"CXYWJANAKT2YMFP4","created_at":"2026-06-12T01:08:13.974358+00:00"},{"alias_kind":"pith_short_8","alias_value":"CXYWJANA","created_at":"2026-06-12T01:08:13.974358+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/CXYWJANAKT2YMFP4QN66B6CAB5","json":"https://pith.science/pith/CXYWJANAKT2YMFP4QN66B6CAB5.json","graph_json":"https://pith.science/api/pith-number/CXYWJANAKT2YMFP4QN66B6CAB5/graph.json","events_json":"https://pith.science/api/pith-number/CXYWJANAKT2YMFP4QN66B6CAB5/events.json","paper":"https://pith.science/paper/CXYWJANA"},"agent_actions":{"view_html":"https://pith.science/pith/CXYWJANAKT2YMFP4QN66B6CAB5","download_json":"https://pith.science/pith/CXYWJANAKT2YMFP4QN66B6CAB5.json","view_paper":"https://pith.science/paper/CXYWJANA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.13102&json=true","fetch_graph":"https://pith.science/api/pith-number/CXYWJANAKT2YMFP4QN66B6CAB5/graph.json","fetch_events":"https://pith.science/api/pith-number/CXYWJANAKT2YMFP4QN66B6CAB5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CXYWJANAKT2YMFP4QN66B6CAB5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CXYWJANAKT2YMFP4QN66B6CAB5/action/storage_attestation","attest_author":"https://pith.science/pith/CXYWJANAKT2YMFP4QN66B6CAB5/action/author_attestation","sign_citation":"https://pith.science/pith/CXYWJANAKT2YMFP4QN66B6CAB5/action/citation_signature","submit_replication":"https://pith.science/pith/CXYWJANAKT2YMFP4QN66B6CAB5/action/replication_record"}},"created_at":"2026-06-12T01:08:13.974358+00:00","updated_at":"2026-06-12T01:08:13.974358+00:00"}