{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:V7NOWECOD6FQHTSBLVYG5PKBL6","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":"b846db834aa296dcda16129fb91c31a1d20a3fbfdab73009d097a5e9f692e79e","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-08T02:35:25Z","title_canon_sha256":"e66adc5da06238e7a3bd6da0e4041a70ff3ff2c4148543e5e3973b087a4ef8a5"},"schema_version":"1.0","source":{"id":"2607.06922","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.06922","created_at":"2026-07-09T00:19:40Z"},{"alias_kind":"arxiv_version","alias_value":"2607.06922v1","created_at":"2026-07-09T00:19:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.06922","created_at":"2026-07-09T00:19:40Z"},{"alias_kind":"pith_short_12","alias_value":"V7NOWECOD6FQ","created_at":"2026-07-09T00:19:40Z"},{"alias_kind":"pith_short_16","alias_value":"V7NOWECOD6FQHTSB","created_at":"2026-07-09T00:19:40Z"},{"alias_kind":"pith_short_8","alias_value":"V7NOWECO","created_at":"2026-07-09T00:19:40Z"}],"graph_snapshots":[{"event_id":"sha256:f22d8995d7a9c0b36b54cc1f232bc688c708c68e3a2700db5ed676501e008fc4","target":"graph","created_at":"2026-07-09T00:19:40Z","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/2607.06922/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep learning applications have been widely adopted on edge devices, to mitigate the privacy and latency issues of accessing cloud servers. Deciding the number of neurons during the design of a deep neural network to maximize performance is not intuitive. Particularly, many application scenarios are real-time and have a strict latency constraint, while conventional neural network optimization methods do not directly change the temporal cost of model inference for latency-critical edge systems. In this work, we propose a latency-oriented neural network learning method to optimize models for hig","authors_text":"Christian Makaya, Di Liu, Hao Kong, Qian Lin, Ravi Subramaniam, Shuo Huai, Weichen Liu","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-08T02:35:25Z","title":"Latency-Constrained DNN Architecture Learning for Edge Systems using Zerorized Batch Normalization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.06922","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":"sha256:8c83f49657f33cfa1cd56bc9f42a8934ab42c75ad05e7ed7a758608962cc4094","target":"record","created_at":"2026-07-09T00:19:40Z","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":"b846db834aa296dcda16129fb91c31a1d20a3fbfdab73009d097a5e9f692e79e","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-08T02:35:25Z","title_canon_sha256":"e66adc5da06238e7a3bd6da0e4041a70ff3ff2c4148543e5e3973b087a4ef8a5"},"schema_version":"1.0","source":{"id":"2607.06922","kind":"arxiv","version":1}},"canonical_sha256":"afdaeb104e1f8b03ce415d706ebd415f914282024821931afd758c244f5596e7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"afdaeb104e1f8b03ce415d706ebd415f914282024821931afd758c244f5596e7","first_computed_at":"2026-07-09T00:19:40.106347Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-09T00:19:40.106347Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ix8syOsKW9XLJxHACiJ+6j5nDHL3gngZJHto7NBkHECEYCHjXln868VdM6dZyN2JmME5wudoTSA+xwnH4IzDAA==","signature_status":"signed_v1","signed_at":"2026-07-09T00:19:40.106842Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.06922","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8c83f49657f33cfa1cd56bc9f42a8934ab42c75ad05e7ed7a758608962cc4094","sha256:f22d8995d7a9c0b36b54cc1f232bc688c708c68e3a2700db5ed676501e008fc4"],"state_sha256":"4eea07d3a87554f6dc040be4687933312f0fe9abc95ff170530fc9b14ffbafe4"}