{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:EM2AABGJYOOKAY7ZJKK3CSMF35","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":"9d2906158ca08b9ca607d1c2a54a31fd16bb8c3f918b8efa2438a0b008fb3500","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-07-12T09:30:58Z","title_canon_sha256":"2e4ad0773e2ecef95bfcb39fe6c6ce14e11b35bb10c578c52c5950358e9d5fdc"},"schema_version":"1.0","source":{"id":"2207.05420","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.05420","created_at":"2026-07-05T04:56:18Z"},{"alias_kind":"arxiv_version","alias_value":"2207.05420v2","created_at":"2026-07-05T04:56:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.05420","created_at":"2026-07-05T04:56:18Z"},{"alias_kind":"pith_short_12","alias_value":"EM2AABGJYOOK","created_at":"2026-07-05T04:56:18Z"},{"alias_kind":"pith_short_16","alias_value":"EM2AABGJYOOKAY7Z","created_at":"2026-07-05T04:56:18Z"},{"alias_kind":"pith_short_8","alias_value":"EM2AABGJ","created_at":"2026-07-05T04:56:18Z"}],"graph_snapshots":[{"event_id":"sha256:8f04c893347b7ef085cd1473df0427d965394c71c70d378b7371a552b421bdfa","target":"graph","created_at":"2026-07-05T04:56:18Z","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/2207.05420/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recently, transformer and multi-layer perceptron (MLP) architectures have achieved impressive results on various vision tasks. However, how to effectively combine those operators to form high-performance hybrid visual architectures still remains a challenge. In this work, we study the learnable combination of convolution, transformer, and MLP by proposing a novel unified architecture search approach. Our approach contains two key designs to achieve the search for high-performance networks. First, we model the very different searchable operators in a unified form, and thus enable the operators ","authors_text":"Guanglu Song, Hongsheng Li, Jihao Liu, Xin Huang, Yu Liu","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-07-12T09:30:58Z","title":"UniNet: Unified Architecture Search with Convolution, Transformer, and MLP"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.05420","kind":"arxiv","version":2},"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:b9891d4b139b02c10cc23e21cfda7578efca92175a2e47e882d71e9fb5807851","target":"record","created_at":"2026-07-05T04:56:18Z","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":"9d2906158ca08b9ca607d1c2a54a31fd16bb8c3f918b8efa2438a0b008fb3500","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-07-12T09:30:58Z","title_canon_sha256":"2e4ad0773e2ecef95bfcb39fe6c6ce14e11b35bb10c578c52c5950358e9d5fdc"},"schema_version":"1.0","source":{"id":"2207.05420","kind":"arxiv","version":2}},"canonical_sha256":"23340004c9c39ca063f94a95b14985df6717f214dc126468bcc1a7f24b4aafa8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"23340004c9c39ca063f94a95b14985df6717f214dc126468bcc1a7f24b4aafa8","first_computed_at":"2026-07-05T04:56:18.756722Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:56:18.756722Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KrL/MaOqKV6nTtQ0CkXpkPvbykFrvTOL3XwqGpXZY0Xf9Ai9bblu+n2g4bsQVMa8MBqLnuICL9SitLyPtoDwCA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:56:18.757103Z","signed_message":"canonical_sha256_bytes"},"source_id":"2207.05420","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b9891d4b139b02c10cc23e21cfda7578efca92175a2e47e882d71e9fb5807851","sha256:8f04c893347b7ef085cd1473df0427d965394c71c70d378b7371a552b421bdfa"],"state_sha256":"e12f7ac6439773794a85e06787fd0c38432c9548b9993c5a7f117c1b6e2666e8"}