{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:6MXELFQSZCGW3LPBH3GBN6K3EF","short_pith_number":"pith:6MXELFQS","schema_version":"1.0","canonical_sha256":"f32e459612c88d6dade13ecc16f95b215ed1c96381bb12d050f363ab3d370ec0","source":{"kind":"arxiv","id":"2508.20193","version":2},"attestation_state":"computed","paper":{"title":"Enhancing Automatic Modulation Recognition With a Reconstruction-Driven Vision Transformer Under Limited Labels","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.CV","authors_text":"Aria Ahmadi, Banafsheh Saffari, Hossein Ahmadi, Mohammad Esmaeil Safari, Sajjad Emdadi Mahdimahalleh","submitted_at":"2025-08-27T18:11:47Z","abstract_excerpt":"Automatic modulation recognition (AMR) is critical for cognitive radio, spectrum monitoring, and secure wireless communication. However, existing solutions often rely on large labeled datasets or multi-stage training pipelines, which limit scalability and generalization in practice. We propose a unified Vision Transformer (ViT) framework that integrates supervised, self-supervised, and reconstruction objectives. The model combines a ViT encoder, a lightweight convolutional decoder, and a linear classifier; the reconstruction branch maps augmented signals back to their originals, anchoring the "},"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":"2508.20193","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-08-27T18:11:47Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"23fd5fa8abca332d5e2f4d9239584ebb2d53c04faeccf3d4fdaae994e51c2372","abstract_canon_sha256":"a99b3e411e60503b6008a0f03ac3688d6537b82a96379790902444cf5cbfbb82"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:09:23.374828Z","signature_b64":"MitxePBdv8VIDVh72Ip5ZYhLOXU8obkivx/vW4tAeThrnD877lN2u5uBINhzvUwUz4phu4DlApUq1BxDcEfuDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f32e459612c88d6dade13ecc16f95b215ed1c96381bb12d050f363ab3d370ec0","last_reissued_at":"2026-07-05T12:09:23.374350Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:09:23.374350Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhancing Automatic Modulation Recognition With a Reconstruction-Driven Vision Transformer Under Limited Labels","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.CV","authors_text":"Aria Ahmadi, Banafsheh Saffari, Hossein Ahmadi, Mohammad Esmaeil Safari, Sajjad Emdadi Mahdimahalleh","submitted_at":"2025-08-27T18:11:47Z","abstract_excerpt":"Automatic modulation recognition (AMR) is critical for cognitive radio, spectrum monitoring, and secure wireless communication. However, existing solutions often rely on large labeled datasets or multi-stage training pipelines, which limit scalability and generalization in practice. We propose a unified Vision Transformer (ViT) framework that integrates supervised, self-supervised, and reconstruction objectives. The model combines a ViT encoder, a lightweight convolutional decoder, and a linear classifier; the reconstruction branch maps augmented signals back to their originals, anchoring the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.20193","kind":"arxiv","version":2},"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/2508.20193/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":"2508.20193","created_at":"2026-07-05T12:09:23.374406+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.20193v2","created_at":"2026-07-05T12:09:23.374406+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.20193","created_at":"2026-07-05T12:09:23.374406+00:00"},{"alias_kind":"pith_short_12","alias_value":"6MXELFQSZCGW","created_at":"2026-07-05T12:09:23.374406+00:00"},{"alias_kind":"pith_short_16","alias_value":"6MXELFQSZCGW3LPB","created_at":"2026-07-05T12:09:23.374406+00:00"},{"alias_kind":"pith_short_8","alias_value":"6MXELFQS","created_at":"2026-07-05T12:09:23.374406+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.10317","citing_title":"Automatic Modulation Classification via Green Machine Learning","ref_index":16,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6MXELFQSZCGW3LPBH3GBN6K3EF","json":"https://pith.science/pith/6MXELFQSZCGW3LPBH3GBN6K3EF.json","graph_json":"https://pith.science/api/pith-number/6MXELFQSZCGW3LPBH3GBN6K3EF/graph.json","events_json":"https://pith.science/api/pith-number/6MXELFQSZCGW3LPBH3GBN6K3EF/events.json","paper":"https://pith.science/paper/6MXELFQS"},"agent_actions":{"view_html":"https://pith.science/pith/6MXELFQSZCGW3LPBH3GBN6K3EF","download_json":"https://pith.science/pith/6MXELFQSZCGW3LPBH3GBN6K3EF.json","view_paper":"https://pith.science/paper/6MXELFQS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.20193&json=true","fetch_graph":"https://pith.science/api/pith-number/6MXELFQSZCGW3LPBH3GBN6K3EF/graph.json","fetch_events":"https://pith.science/api/pith-number/6MXELFQSZCGW3LPBH3GBN6K3EF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6MXELFQSZCGW3LPBH3GBN6K3EF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6MXELFQSZCGW3LPBH3GBN6K3EF/action/storage_attestation","attest_author":"https://pith.science/pith/6MXELFQSZCGW3LPBH3GBN6K3EF/action/author_attestation","sign_citation":"https://pith.science/pith/6MXELFQSZCGW3LPBH3GBN6K3EF/action/citation_signature","submit_replication":"https://pith.science/pith/6MXELFQSZCGW3LPBH3GBN6K3EF/action/replication_record"}},"created_at":"2026-07-05T12:09:23.374406+00:00","updated_at":"2026-07-05T12:09:23.374406+00:00"}