{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:L7KFH6KPQLWM5DVGVNZYRGLLCT","short_pith_number":"pith:L7KFH6KP","schema_version":"1.0","canonical_sha256":"5fd453f94f82ecce8ea6ab7388996b14dc0e31274f98ed5e554ee1249a688c09","source":{"kind":"arxiv","id":"2606.27821","version":1},"attestation_state":"computed","paper":{"title":"Parameter-Efficient Quantum-Inspired Fast Weight Programmers for Traffic-Matrix Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"quant-ph","authors_text":"Chun-Hua Lin, Jiun-Cheng Jiang, Kuo-Chung Peng, Nan-Yow Chen, Samuel Yen-Chi Chen, Tai-Yue Li","submitted_at":"2026-06-26T08:02:27Z","abstract_excerpt":"Traffic matrices (TMs) capture network-wide origin-destination demand and are central to traffic engineering, yet accurate whole-matrix forecasting remains challenging when prediction must be performed under the memory, update, and training-budget constraints of online network control. This paper investigates whether compact quantum-inspired recurrent models can provide effective TM forecasts without relying on dedicated graph, transformer, or diffusion modules. We adapt gated quantum-inspired Kolmogorov-Arnold network fast-weight programmers (QKAN-FWPs) to direct multi-step Abilene TM forecas"},"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":"2606.27821","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"quant-ph","submitted_at":"2026-06-26T08:02:27Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"b979dff1a44278043b9bf9031283e7e966a00ac0c3091cfd0a0b1b694b6b4552","abstract_canon_sha256":"72223dd237791df4e53fbb5883a1863558d075394016766c32498832dd6f3a1f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-29T01:14:49.427576Z","signature_b64":"eQYJ4xXHTJmpok4z2NDPJ6QPbT8RjwP2vfESVzulmriD9WBBe1bjZ2bULM2U8BSEhD18DawBnruANykdI/naCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5fd453f94f82ecce8ea6ab7388996b14dc0e31274f98ed5e554ee1249a688c09","last_reissued_at":"2026-06-29T01:14:49.427149Z","signature_status":"signed_v1","first_computed_at":"2026-06-29T01:14:49.427149Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Parameter-Efficient Quantum-Inspired Fast Weight Programmers for Traffic-Matrix Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"quant-ph","authors_text":"Chun-Hua Lin, Jiun-Cheng Jiang, Kuo-Chung Peng, Nan-Yow Chen, Samuel Yen-Chi Chen, Tai-Yue Li","submitted_at":"2026-06-26T08:02:27Z","abstract_excerpt":"Traffic matrices (TMs) capture network-wide origin-destination demand and are central to traffic engineering, yet accurate whole-matrix forecasting remains challenging when prediction must be performed under the memory, update, and training-budget constraints of online network control. This paper investigates whether compact quantum-inspired recurrent models can provide effective TM forecasts without relying on dedicated graph, transformer, or diffusion modules. We adapt gated quantum-inspired Kolmogorov-Arnold network fast-weight programmers (QKAN-FWPs) to direct multi-step Abilene TM forecas"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.27821","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/2606.27821/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":"2606.27821","created_at":"2026-06-29T01:14:49.427213+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.27821v1","created_at":"2026-06-29T01:14:49.427213+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.27821","created_at":"2026-06-29T01:14:49.427213+00:00"},{"alias_kind":"pith_short_12","alias_value":"L7KFH6KPQLWM","created_at":"2026-06-29T01:14:49.427213+00:00"},{"alias_kind":"pith_short_16","alias_value":"L7KFH6KPQLWM5DVG","created_at":"2026-06-29T01:14:49.427213+00:00"},{"alias_kind":"pith_short_8","alias_value":"L7KFH6KP","created_at":"2026-06-29T01:14:49.427213+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/L7KFH6KPQLWM5DVGVNZYRGLLCT","json":"https://pith.science/pith/L7KFH6KPQLWM5DVGVNZYRGLLCT.json","graph_json":"https://pith.science/api/pith-number/L7KFH6KPQLWM5DVGVNZYRGLLCT/graph.json","events_json":"https://pith.science/api/pith-number/L7KFH6KPQLWM5DVGVNZYRGLLCT/events.json","paper":"https://pith.science/paper/L7KFH6KP"},"agent_actions":{"view_html":"https://pith.science/pith/L7KFH6KPQLWM5DVGVNZYRGLLCT","download_json":"https://pith.science/pith/L7KFH6KPQLWM5DVGVNZYRGLLCT.json","view_paper":"https://pith.science/paper/L7KFH6KP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.27821&json=true","fetch_graph":"https://pith.science/api/pith-number/L7KFH6KPQLWM5DVGVNZYRGLLCT/graph.json","fetch_events":"https://pith.science/api/pith-number/L7KFH6KPQLWM5DVGVNZYRGLLCT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L7KFH6KPQLWM5DVGVNZYRGLLCT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L7KFH6KPQLWM5DVGVNZYRGLLCT/action/storage_attestation","attest_author":"https://pith.science/pith/L7KFH6KPQLWM5DVGVNZYRGLLCT/action/author_attestation","sign_citation":"https://pith.science/pith/L7KFH6KPQLWM5DVGVNZYRGLLCT/action/citation_signature","submit_replication":"https://pith.science/pith/L7KFH6KPQLWM5DVGVNZYRGLLCT/action/replication_record"}},"created_at":"2026-06-29T01:14:49.427213+00:00","updated_at":"2026-06-29T01:14:49.427213+00:00"}