{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:HUUEB7MYUZTMXD6VYZQRKHP2OZ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"3878b6f56ea40c90e225abf42cd749a64d8c55fa1343ec8f52148704ae0da618","cross_cats_sorted":["cs.AI","math.IT"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IT","submitted_at":"2023-11-27T15:56:58Z","title_canon_sha256":"81106a282a26e169e19cddd0c2494616c1746605739631d6be92a92550561551"},"schema_version":"1.0","source":{"id":"2311.15950","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.15950","created_at":"2026-07-05T07:17:11Z"},{"alias_kind":"arxiv_version","alias_value":"2311.15950v1","created_at":"2026-07-05T07:17:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.15950","created_at":"2026-07-05T07:17:11Z"},{"alias_kind":"pith_short_12","alias_value":"HUUEB7MYUZTM","created_at":"2026-07-05T07:17:11Z"},{"alias_kind":"pith_short_16","alias_value":"HUUEB7MYUZTMXD6V","created_at":"2026-07-05T07:17:11Z"},{"alias_kind":"pith_short_8","alias_value":"HUUEB7MY","created_at":"2026-07-05T07:17:11Z"}],"graph_snapshots":[{"event_id":"sha256:828aff0984c5ec8c672994e7ad942f226d0b1b037baa0d4424c818afb70bf27d","target":"graph","created_at":"2026-07-05T07:17:11Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2311.15950/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep learning has revolutionized the design of the channel state information (CSI) feedback module in wireless communications. However, designing the optimal neural network (NN) architecture for CSI feedback can be a laborious and time-consuming process. Manual design can be prohibitively expensive for customizing NNs to different scenarios. This paper proposes using neural architecture search (NAS) to automate the generation of scenario-customized CSI feedback NN architectures, thereby maximizing the potential of deep learning in exclusive environments. By employing automated machine learning","authors_text":"Chao-Kai Wen, Jiajia Guo, Shi Jin, Xiangyi Li","cross_cats":["cs.AI","math.IT"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IT","submitted_at":"2023-11-27T15:56:58Z","title":"Auto-CsiNet: Scenario-customized Automatic Neural Network Architecture Generation for Massive MIMO CSI Feedback"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.15950","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:4ba8de9689a89302cfedfa444864d3dbc80114ca98b65fe94d97c75cbd847f10","target":"record","created_at":"2026-07-05T07:17:11Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"3878b6f56ea40c90e225abf42cd749a64d8c55fa1343ec8f52148704ae0da618","cross_cats_sorted":["cs.AI","math.IT"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IT","submitted_at":"2023-11-27T15:56:58Z","title_canon_sha256":"81106a282a26e169e19cddd0c2494616c1746605739631d6be92a92550561551"},"schema_version":"1.0","source":{"id":"2311.15950","kind":"arxiv","version":1}},"canonical_sha256":"3d2840fd98a666cb8fd5c661151dfa767d499a1d6244ed0dc2cc18c920120124","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3d2840fd98a666cb8fd5c661151dfa767d499a1d6244ed0dc2cc18c920120124","first_computed_at":"2026-07-05T07:17:11.581067Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:17:11.581067Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Pli4eD7R1Lvk7/fAuFgH68/lH/H4y9l5bZbsEFn35QyA/X9DksejAbcniv5N1yig6d672rlR1Fmhl1toO9qRDg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:17:11.581482Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.15950","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4ba8de9689a89302cfedfa444864d3dbc80114ca98b65fe94d97c75cbd847f10","sha256:828aff0984c5ec8c672994e7ad942f226d0b1b037baa0d4424c818afb70bf27d"],"state_sha256":"8fb7d17ce0a8e7a563b5f37d4644d8c333adb317f77d485ce8e9ffb9b52eb7cb"}