{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:YNPXEG5Q42IYA7EJWZQ5G3AHYW","short_pith_number":"pith:YNPXEG5Q","schema_version":"1.0","canonical_sha256":"c35f721bb0e691807c89b661d36c07c599cc5c503b334f56d34c1fbad728475b","source":{"kind":"arxiv","id":"2604.23165","version":2},"attestation_state":"computed","paper":{"title":"BSViT: A Burst Spiking Vision Transformer for Expressive and Efficient Visual Representation Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Burst spikes and dual-channel attention improve accuracy in spiking vision transformers without sacrificing energy efficiency.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dewei Bai, Hong Qu, Hongxiang Peng","submitted_at":"2026-04-25T06:33:04Z","abstract_excerpt":"Spiking Vision Transformers (S-ViTs) offer a promising framework for energy-efficient visual learning. However, existing designs remain limited by two fundamental issues: the restricted information capacity of binary spike coding and the dense token interactions introduced by global self-attention. To address these challenges, this work proposes BSViT, a burst spiking-driven Vision Transformer featuring a Dual-Channel Burst Spiking Self-Attention (DBSSA) mechanism. DBSSA encodes queries with binary spikes and keys with burst spikes to enhance representational capacity. The value pathway adopts"},"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":"2604.23165","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-04-25T06:33:04Z","cross_cats_sorted":[],"title_canon_sha256":"4c4310311245a3712017f79ff07013c6a64cd891d2fc50e3031b47847568c095","abstract_canon_sha256":"2a6ef00bd0c79f55bcf79de1c6c0f8a429dcbe870fe407398ac11a1f117a3b0e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-12T01:08:25.105598Z","signature_b64":"aVaKrBhwG0+meNOKpOp3ecEAzOXbIhqQIy0lJvsiwb9VcqoDZQcWr+gPzx0AmJfNDirKfjnpzcYiDF0BnaNLCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c35f721bb0e691807c89b661d36c07c599cc5c503b334f56d34c1fbad728475b","last_reissued_at":"2026-06-12T01:08:25.104658Z","signature_status":"signed_v1","first_computed_at":"2026-06-12T01:08:25.104658Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BSViT: A Burst Spiking Vision Transformer for Expressive and Efficient Visual Representation Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Burst spikes and dual-channel attention improve accuracy in spiking vision transformers without sacrificing energy efficiency.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dewei Bai, Hong Qu, Hongxiang Peng","submitted_at":"2026-04-25T06:33:04Z","abstract_excerpt":"Spiking Vision Transformers (S-ViTs) offer a promising framework for energy-efficient visual learning. However, existing designs remain limited by two fundamental issues: the restricted information capacity of binary spike coding and the dense token interactions introduced by global self-attention. To address these challenges, this work proposes BSViT, a burst spiking-driven Vision Transformer featuring a Dual-Channel Burst Spiking Self-Attention (DBSSA) mechanism. DBSSA encodes queries with binary spikes and keys with burst spikes to enhance representational capacity. The value pathway adopts"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Extensive experiments on both static and event-based vision benchmarks demonstrate that BSViT consistently outperforms existing spiking Transformers in accuracy while maintaining competitive energy efficiency.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That encoding queries with binary spikes, keys with burst spikes, and values with dual excitatory/inhibitory binary channels will increase representational capacity and spike interactions while preserving addition-only computation and hardware compatibility.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"BSViT introduces burst spike coding and dual-channel burst spiking self-attention in a Vision Transformer, outperforming prior spiking transformers on static and event-based vision tasks with competitive energy efficiency.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Burst spikes and dual-channel attention improve accuracy in spiking vision transformers without sacrificing energy efficiency.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"b3901281e8c719d79a2e06a4f1dadbb546725bd79f72c74ada6978ea8451a39b"},"source":{"id":"2604.23165","kind":"arxiv","version":2},"verdict":{"id":"fc6d6b58-8b5c-4516-9b4d-a9b7127d09b0","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-08T08:31:41.534091Z","strongest_claim":"Extensive experiments on both static and event-based vision benchmarks demonstrate that BSViT consistently outperforms existing spiking Transformers in accuracy while maintaining competitive energy efficiency.","one_line_summary":"BSViT introduces burst spike coding and dual-channel burst spiking self-attention in a Vision Transformer, outperforming prior spiking transformers on static and event-based vision tasks with competitive energy efficiency.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That encoding queries with binary spikes, keys with burst spikes, and values with dual excitatory/inhibitory binary channels will increase representational capacity and spike interactions while preserving addition-only computation and hardware compatibility.","pith_extraction_headline":"Burst spikes and dual-channel attention improve accuracy in spiking vision transformers without sacrificing energy efficiency."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.23165/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-21T09:38:13.009615Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T23:24:32.113815Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"93985dd619c658160b0316430b56dc9c672a5f0df604c75701d93d5765fd3438"},"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":"2604.23165","created_at":"2026-06-12T01:08:25.104782+00:00"},{"alias_kind":"arxiv_version","alias_value":"2604.23165v2","created_at":"2026-06-12T01:08:25.104782+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.23165","created_at":"2026-06-12T01:08:25.104782+00:00"},{"alias_kind":"pith_short_12","alias_value":"YNPXEG5Q42IY","created_at":"2026-06-12T01:08:25.104782+00:00"},{"alias_kind":"pith_short_16","alias_value":"YNPXEG5Q42IYA7EJ","created_at":"2026-06-12T01:08:25.104782+00:00"},{"alias_kind":"pith_short_8","alias_value":"YNPXEG5Q","created_at":"2026-06-12T01:08:25.104782+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/YNPXEG5Q42IYA7EJWZQ5G3AHYW","json":"https://pith.science/pith/YNPXEG5Q42IYA7EJWZQ5G3AHYW.json","graph_json":"https://pith.science/api/pith-number/YNPXEG5Q42IYA7EJWZQ5G3AHYW/graph.json","events_json":"https://pith.science/api/pith-number/YNPXEG5Q42IYA7EJWZQ5G3AHYW/events.json","paper":"https://pith.science/paper/YNPXEG5Q"},"agent_actions":{"view_html":"https://pith.science/pith/YNPXEG5Q42IYA7EJWZQ5G3AHYW","download_json":"https://pith.science/pith/YNPXEG5Q42IYA7EJWZQ5G3AHYW.json","view_paper":"https://pith.science/paper/YNPXEG5Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2604.23165&json=true","fetch_graph":"https://pith.science/api/pith-number/YNPXEG5Q42IYA7EJWZQ5G3AHYW/graph.json","fetch_events":"https://pith.science/api/pith-number/YNPXEG5Q42IYA7EJWZQ5G3AHYW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YNPXEG5Q42IYA7EJWZQ5G3AHYW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YNPXEG5Q42IYA7EJWZQ5G3AHYW/action/storage_attestation","attest_author":"https://pith.science/pith/YNPXEG5Q42IYA7EJWZQ5G3AHYW/action/author_attestation","sign_citation":"https://pith.science/pith/YNPXEG5Q42IYA7EJWZQ5G3AHYW/action/citation_signature","submit_replication":"https://pith.science/pith/YNPXEG5Q42IYA7EJWZQ5G3AHYW/action/replication_record"}},"created_at":"2026-06-12T01:08:25.104782+00:00","updated_at":"2026-06-12T01:08:25.104782+00:00"}