{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:IAN7KNTLXPQI6YOW6L77USL7RV","short_pith_number":"pith:IAN7KNTL","schema_version":"1.0","canonical_sha256":"401bf5366bbbe08f61d6f2fffa497f8d4f5f9aecb28bfaffa780f2b5f856e673","source":{"kind":"arxiv","id":"2606.31352","version":1},"attestation_state":"computed","paper":{"title":"Dualformer: Efficient Feature Extractor for Complex-valued Blind Communication Signal Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Jingreng Lei, Wanlong Zhang, Xiang Wang, Yik-Chung Wu, Yurui Zhao, Zhitao Huang","submitted_at":"2026-06-30T08:49:01Z","abstract_excerpt":"Designing effective feature extractors is critical for blind signal analysis tasks such as automatic modulation recognition (AMR), signal scheme recognition (SSR), and \\color{black} signal structure parsing (SSP). In this work, we propose dual-channel neural network (DualNN) that efficiently exploits complex-valued signals through parameter sharing across IQ channels. Unlike traditional real-valued or complex-valued models, DualNN is a groundbreaking framework which shares the network parameters for processing the real and imaginary parts of the complex-valued signals, and is theoretically sho"},"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":"2606.31352","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-30T08:49:01Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"5e7a0b453d1e84b1908038cda6fd10f45d2e728c2da902838306c7fbc6756e12","abstract_canon_sha256":"0d025261c20c34e101a8226977eb461155abf66be2258e544480e6814fb6caf3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-01T01:18:00.203424Z","signature_b64":"wrDDGnTPIJCCuCUsKHS5LlESd9m7VJTkMQDonbCVZZS3uqZceVHM0Tjz+Cw2skbpFop0WODgIB1Mjzl1TVNuCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"401bf5366bbbe08f61d6f2fffa497f8d4f5f9aecb28bfaffa780f2b5f856e673","last_reissued_at":"2026-07-01T01:18:00.202971Z","signature_status":"signed_v1","first_computed_at":"2026-07-01T01:18:00.202971Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dualformer: Efficient Feature Extractor for Complex-valued Blind Communication Signal Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Jingreng Lei, Wanlong Zhang, Xiang Wang, Yik-Chung Wu, Yurui Zhao, Zhitao Huang","submitted_at":"2026-06-30T08:49:01Z","abstract_excerpt":"Designing effective feature extractors is critical for blind signal analysis tasks such as automatic modulation recognition (AMR), signal scheme recognition (SSR), and \\color{black} signal structure parsing (SSP). In this work, we propose dual-channel neural network (DualNN) that efficiently exploits complex-valued signals through parameter sharing across IQ channels. Unlike traditional real-valued or complex-valued models, DualNN is a groundbreaking framework which shares the network parameters for processing the real and imaginary parts of the complex-valued signals, and is theoretically sho"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.31352","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/2606.31352/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":"2606.31352","created_at":"2026-07-01T01:18:00.203044+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.31352v1","created_at":"2026-07-01T01:18:00.203044+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.31352","created_at":"2026-07-01T01:18:00.203044+00:00"},{"alias_kind":"pith_short_12","alias_value":"IAN7KNTLXPQI","created_at":"2026-07-01T01:18:00.203044+00:00"},{"alias_kind":"pith_short_16","alias_value":"IAN7KNTLXPQI6YOW","created_at":"2026-07-01T01:18:00.203044+00:00"},{"alias_kind":"pith_short_8","alias_value":"IAN7KNTL","created_at":"2026-07-01T01:18:00.203044+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/IAN7KNTLXPQI6YOW6L77USL7RV","json":"https://pith.science/pith/IAN7KNTLXPQI6YOW6L77USL7RV.json","graph_json":"https://pith.science/api/pith-number/IAN7KNTLXPQI6YOW6L77USL7RV/graph.json","events_json":"https://pith.science/api/pith-number/IAN7KNTLXPQI6YOW6L77USL7RV/events.json","paper":"https://pith.science/paper/IAN7KNTL"},"agent_actions":{"view_html":"https://pith.science/pith/IAN7KNTLXPQI6YOW6L77USL7RV","download_json":"https://pith.science/pith/IAN7KNTLXPQI6YOW6L77USL7RV.json","view_paper":"https://pith.science/paper/IAN7KNTL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.31352&json=true","fetch_graph":"https://pith.science/api/pith-number/IAN7KNTLXPQI6YOW6L77USL7RV/graph.json","fetch_events":"https://pith.science/api/pith-number/IAN7KNTLXPQI6YOW6L77USL7RV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IAN7KNTLXPQI6YOW6L77USL7RV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IAN7KNTLXPQI6YOW6L77USL7RV/action/storage_attestation","attest_author":"https://pith.science/pith/IAN7KNTLXPQI6YOW6L77USL7RV/action/author_attestation","sign_citation":"https://pith.science/pith/IAN7KNTLXPQI6YOW6L77USL7RV/action/citation_signature","submit_replication":"https://pith.science/pith/IAN7KNTLXPQI6YOW6L77USL7RV/action/replication_record"}},"created_at":"2026-07-01T01:18:00.203044+00:00","updated_at":"2026-07-01T01:18:00.203044+00:00"}