{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:6DIS53JXY2NTGFLKFVN7RW4LFQ","short_pith_number":"pith:6DIS53JX","schema_version":"1.0","canonical_sha256":"f0d12eed37c69b33156a2d5bf8db8b2c044fc1bef864417e91e7cded05b8a766","source":{"kind":"arxiv","id":"2502.00585","version":3},"attestation_state":"computed","paper":{"title":"Converting Transformers into DGNNs Form","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Bo-Wei Chiu, Jie Zhang, Mao-Hsuan Mao, Min-Te Sun","submitted_at":"2025-02-01T22:44:46Z","abstract_excerpt":"Recent advances in deep learning have established Transformer architectures as the predominant modeling paradigm. Central to the success of Transformers is the self-attention mechanism, which scores the similarity between query and key matrices to modulate a value matrix. This operation bears striking similarities to digraph convolution, prompting an investigation into whether digraph convolution could serve as an alternative to self-attention. In this study, we formalize this concept by introducing a synthetic unitary digraph convolution based on the digraph Fourier transform. The resulting m"},"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":"2502.00585","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-01T22:44:46Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"1dc91a9ae7c20bafb9ccbcd970524f28d95d863acac61afe625803dfe181ee96","abstract_canon_sha256":"478def4e0636060d1cdc98234ca8bf16ba39d6f2108e3f55b2684009e17f8f42"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:23:28.961740Z","signature_b64":"3INqyWxDAT1oIPPRTT6vXr79XAz1VDN238O23Ywt0PukI3RXG5x9TK1w0OtbKnIheOCuGTga1S+LGfFa+hxfCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f0d12eed37c69b33156a2d5bf8db8b2c044fc1bef864417e91e7cded05b8a766","last_reissued_at":"2026-07-05T10:23:28.960702Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:23:28.960702Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Converting Transformers into DGNNs Form","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Bo-Wei Chiu, Jie Zhang, Mao-Hsuan Mao, Min-Te Sun","submitted_at":"2025-02-01T22:44:46Z","abstract_excerpt":"Recent advances in deep learning have established Transformer architectures as the predominant modeling paradigm. Central to the success of Transformers is the self-attention mechanism, which scores the similarity between query and key matrices to modulate a value matrix. This operation bears striking similarities to digraph convolution, prompting an investigation into whether digraph convolution could serve as an alternative to self-attention. In this study, we formalize this concept by introducing a synthetic unitary digraph convolution based on the digraph Fourier transform. The resulting m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.00585","kind":"arxiv","version":3},"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/2502.00585/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":"2502.00585","created_at":"2026-07-05T10:23:28.960863+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.00585v3","created_at":"2026-07-05T10:23:28.960863+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.00585","created_at":"2026-07-05T10:23:28.960863+00:00"},{"alias_kind":"pith_short_12","alias_value":"6DIS53JXY2NT","created_at":"2026-07-05T10:23:28.960863+00:00"},{"alias_kind":"pith_short_16","alias_value":"6DIS53JXY2NTGFLK","created_at":"2026-07-05T10:23:28.960863+00:00"},{"alias_kind":"pith_short_8","alias_value":"6DIS53JX","created_at":"2026-07-05T10:23:28.960863+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/6DIS53JXY2NTGFLKFVN7RW4LFQ","json":"https://pith.science/pith/6DIS53JXY2NTGFLKFVN7RW4LFQ.json","graph_json":"https://pith.science/api/pith-number/6DIS53JXY2NTGFLKFVN7RW4LFQ/graph.json","events_json":"https://pith.science/api/pith-number/6DIS53JXY2NTGFLKFVN7RW4LFQ/events.json","paper":"https://pith.science/paper/6DIS53JX"},"agent_actions":{"view_html":"https://pith.science/pith/6DIS53JXY2NTGFLKFVN7RW4LFQ","download_json":"https://pith.science/pith/6DIS53JXY2NTGFLKFVN7RW4LFQ.json","view_paper":"https://pith.science/paper/6DIS53JX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.00585&json=true","fetch_graph":"https://pith.science/api/pith-number/6DIS53JXY2NTGFLKFVN7RW4LFQ/graph.json","fetch_events":"https://pith.science/api/pith-number/6DIS53JXY2NTGFLKFVN7RW4LFQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6DIS53JXY2NTGFLKFVN7RW4LFQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6DIS53JXY2NTGFLKFVN7RW4LFQ/action/storage_attestation","attest_author":"https://pith.science/pith/6DIS53JXY2NTGFLKFVN7RW4LFQ/action/author_attestation","sign_citation":"https://pith.science/pith/6DIS53JXY2NTGFLKFVN7RW4LFQ/action/citation_signature","submit_replication":"https://pith.science/pith/6DIS53JXY2NTGFLKFVN7RW4LFQ/action/replication_record"}},"created_at":"2026-07-05T10:23:28.960863+00:00","updated_at":"2026-07-05T10:23:28.960863+00:00"}