{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:3EWA4S3NIUFWQFSKXFFKDBBJS3","short_pith_number":"pith:3EWA4S3N","schema_version":"1.0","canonical_sha256":"d92c0e4b6d450b68164ab94aa1842996cafdb20a8e3618bb7ed2671147be9a0e","source":{"kind":"arxiv","id":"2306.06574","version":1},"attestation_state":"computed","paper":{"title":"Learnable Digital Twin for Efficient Wireless Network Evaluation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SY","eess.SY"],"primary_cat":"cs.NI","authors_text":"Abhishek Kumar, Ananthram Swami, Boning Li, Gunjan Verma, Jose Cortes, Santiago Segarra, Timofey Efimov","submitted_at":"2023-06-11T03:43:39Z","abstract_excerpt":"Network digital twins (NDTs) facilitate the estimation of key performance indicators (KPIs) before physically implementing a network, thereby enabling efficient optimization of the network configuration. In this paper, we propose a learning-based NDT for network simulators. The proposed method offers a holistic representation of information flow in a wireless network by integrating node, edge, and path embeddings. Through this approach, the model is trained to map the network configuration to KPIs in a single forward pass. Hence, it offers a more efficient alternative to traditional simulation"},"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":"2306.06574","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2023-06-11T03:43:39Z","cross_cats_sorted":["cs.LG","cs.SY","eess.SY"],"title_canon_sha256":"a2c44cf7ebe8b738275c0c3577e8dd144839a23223f6b76292d1cee5f923eb13","abstract_canon_sha256":"9ac60e7a0ab73cbf58c98f4f026e68784d6ce32581a05ea9a488ab0a6f38214d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:19:26.424538Z","signature_b64":"O3hC/b4Q9ERwuaZWXAZbfWn8PP4DhAXMheEe3kucd56E6hD9DufBmWPo99MXegbRdgpqc6hNbxOy2YIg+cMPBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d92c0e4b6d450b68164ab94aa1842996cafdb20a8e3618bb7ed2671147be9a0e","last_reissued_at":"2026-07-05T06:19:26.424059Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:19:26.424059Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learnable Digital Twin for Efficient Wireless Network Evaluation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SY","eess.SY"],"primary_cat":"cs.NI","authors_text":"Abhishek Kumar, Ananthram Swami, Boning Li, Gunjan Verma, Jose Cortes, Santiago Segarra, Timofey Efimov","submitted_at":"2023-06-11T03:43:39Z","abstract_excerpt":"Network digital twins (NDTs) facilitate the estimation of key performance indicators (KPIs) before physically implementing a network, thereby enabling efficient optimization of the network configuration. In this paper, we propose a learning-based NDT for network simulators. The proposed method offers a holistic representation of information flow in a wireless network by integrating node, edge, and path embeddings. Through this approach, the model is trained to map the network configuration to KPIs in a single forward pass. Hence, it offers a more efficient alternative to traditional simulation"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.06574","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/2306.06574/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":"2306.06574","created_at":"2026-07-05T06:19:26.424124+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.06574v1","created_at":"2026-07-05T06:19:26.424124+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.06574","created_at":"2026-07-05T06:19:26.424124+00:00"},{"alias_kind":"pith_short_12","alias_value":"3EWA4S3NIUFW","created_at":"2026-07-05T06:19:26.424124+00:00"},{"alias_kind":"pith_short_16","alias_value":"3EWA4S3NIUFWQFSK","created_at":"2026-07-05T06:19:26.424124+00:00"},{"alias_kind":"pith_short_8","alias_value":"3EWA4S3N","created_at":"2026-07-05T06:19:26.424124+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/3EWA4S3NIUFWQFSKXFFKDBBJS3","json":"https://pith.science/pith/3EWA4S3NIUFWQFSKXFFKDBBJS3.json","graph_json":"https://pith.science/api/pith-number/3EWA4S3NIUFWQFSKXFFKDBBJS3/graph.json","events_json":"https://pith.science/api/pith-number/3EWA4S3NIUFWQFSKXFFKDBBJS3/events.json","paper":"https://pith.science/paper/3EWA4S3N"},"agent_actions":{"view_html":"https://pith.science/pith/3EWA4S3NIUFWQFSKXFFKDBBJS3","download_json":"https://pith.science/pith/3EWA4S3NIUFWQFSKXFFKDBBJS3.json","view_paper":"https://pith.science/paper/3EWA4S3N","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.06574&json=true","fetch_graph":"https://pith.science/api/pith-number/3EWA4S3NIUFWQFSKXFFKDBBJS3/graph.json","fetch_events":"https://pith.science/api/pith-number/3EWA4S3NIUFWQFSKXFFKDBBJS3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3EWA4S3NIUFWQFSKXFFKDBBJS3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3EWA4S3NIUFWQFSKXFFKDBBJS3/action/storage_attestation","attest_author":"https://pith.science/pith/3EWA4S3NIUFWQFSKXFFKDBBJS3/action/author_attestation","sign_citation":"https://pith.science/pith/3EWA4S3NIUFWQFSKXFFKDBBJS3/action/citation_signature","submit_replication":"https://pith.science/pith/3EWA4S3NIUFWQFSKXFFKDBBJS3/action/replication_record"}},"created_at":"2026-07-05T06:19:26.424124+00:00","updated_at":"2026-07-05T06:19:26.424124+00:00"}