{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:KYNSSRNROGVBKVWKFNHCU6A5QI","short_pith_number":"pith:KYNSSRNR","schema_version":"1.0","canonical_sha256":"561b2945b171aa1556ca2b4e2a781d8202c34d8d34d59e650cc8a987942811cf","source":{"kind":"arxiv","id":"2607.00860","version":1},"attestation_state":"computed","paper":{"title":"Meta-Transfer Learning for mmWave Beam Alignment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.SY","eess.SY"],"primary_cat":"eess.SP","authors_text":"Ahmet Nuri Cevik, Sinem Coleri","submitted_at":"2026-07-01T12:24:48Z","abstract_excerpt":"Millimeter-wave (mmWave) beam alignment plays a critical role in next-generation wireless systems, yet its efficient implementation remains challenging. Meta-learning and transfer learning have been explored to enable deep learning-based beam prediction models to rapidly adapt to unseen environments; however, existing meta-learning approaches adapt the entire network and are trained from random initialization, leading to a large number of updated parameters and a high meta-training cost, while transfer learning approaches restrict adaptation to part of the network but do not exploit episodic m"},"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":"2607.00860","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2026-07-01T12:24:48Z","cross_cats_sorted":["cs.AI","cs.SY","eess.SY"],"title_canon_sha256":"d1188ae65f1060cc04244a14e80ba0789354f4b7ed7a668cdc700ac1240a95a9","abstract_canon_sha256":"7bb694f68808a92df18555c78de32609123b9ef052cfb6326254f9680bd9ed87"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-02T01:18:21.657539Z","signature_b64":"2IgWi2pxl08usTQOF18EI5yLBZYVq26Y0XTkVZuZB0T/Zy+LI+h7Mf3UiyATB7e9hThyE+qGIAvVSOhZFoqYCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"561b2945b171aa1556ca2b4e2a781d8202c34d8d34d59e650cc8a987942811cf","last_reissued_at":"2026-07-02T01:18:21.657183Z","signature_status":"signed_v1","first_computed_at":"2026-07-02T01:18:21.657183Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Meta-Transfer Learning for mmWave Beam Alignment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.SY","eess.SY"],"primary_cat":"eess.SP","authors_text":"Ahmet Nuri Cevik, Sinem Coleri","submitted_at":"2026-07-01T12:24:48Z","abstract_excerpt":"Millimeter-wave (mmWave) beam alignment plays a critical role in next-generation wireless systems, yet its efficient implementation remains challenging. Meta-learning and transfer learning have been explored to enable deep learning-based beam prediction models to rapidly adapt to unseen environments; however, existing meta-learning approaches adapt the entire network and are trained from random initialization, leading to a large number of updated parameters and a high meta-training cost, while transfer learning approaches restrict adaptation to part of the network but do not exploit episodic m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.00860","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/2607.00860/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":"2607.00860","created_at":"2026-07-02T01:18:21.657245+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.00860v1","created_at":"2026-07-02T01:18:21.657245+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.00860","created_at":"2026-07-02T01:18:21.657245+00:00"},{"alias_kind":"pith_short_12","alias_value":"KYNSSRNROGVB","created_at":"2026-07-02T01:18:21.657245+00:00"},{"alias_kind":"pith_short_16","alias_value":"KYNSSRNROGVBKVWK","created_at":"2026-07-02T01:18:21.657245+00:00"},{"alias_kind":"pith_short_8","alias_value":"KYNSSRNR","created_at":"2026-07-02T01:18:21.657245+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/KYNSSRNROGVBKVWKFNHCU6A5QI","json":"https://pith.science/pith/KYNSSRNROGVBKVWKFNHCU6A5QI.json","graph_json":"https://pith.science/api/pith-number/KYNSSRNROGVBKVWKFNHCU6A5QI/graph.json","events_json":"https://pith.science/api/pith-number/KYNSSRNROGVBKVWKFNHCU6A5QI/events.json","paper":"https://pith.science/paper/KYNSSRNR"},"agent_actions":{"view_html":"https://pith.science/pith/KYNSSRNROGVBKVWKFNHCU6A5QI","download_json":"https://pith.science/pith/KYNSSRNROGVBKVWKFNHCU6A5QI.json","view_paper":"https://pith.science/paper/KYNSSRNR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.00860&json=true","fetch_graph":"https://pith.science/api/pith-number/KYNSSRNROGVBKVWKFNHCU6A5QI/graph.json","fetch_events":"https://pith.science/api/pith-number/KYNSSRNROGVBKVWKFNHCU6A5QI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KYNSSRNROGVBKVWKFNHCU6A5QI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KYNSSRNROGVBKVWKFNHCU6A5QI/action/storage_attestation","attest_author":"https://pith.science/pith/KYNSSRNROGVBKVWKFNHCU6A5QI/action/author_attestation","sign_citation":"https://pith.science/pith/KYNSSRNROGVBKVWKFNHCU6A5QI/action/citation_signature","submit_replication":"https://pith.science/pith/KYNSSRNROGVBKVWKFNHCU6A5QI/action/replication_record"}},"created_at":"2026-07-02T01:18:21.657245+00:00","updated_at":"2026-07-02T01:18:21.657245+00:00"}