{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:D3MKVMGI53X3TRWJVMZPLAQOMA","short_pith_number":"pith:D3MKVMGI","schema_version":"1.0","canonical_sha256":"1ed8aab0c8eeefb9c6c9ab32f5820e6004815f0541e184730e41d11259543a16","source":{"kind":"arxiv","id":"2503.06220","version":3},"attestation_state":"computed","paper":{"title":"StreamMind: Unlocking Full Frame Rate Streaming Video Dialogue through Event-Gated Cognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Donglin Bai, Hao Wu, Shiqi Jiang, Ting Cao, Xin Ding, Yifan Yang, Zhibo Chen","submitted_at":"2025-03-08T13:44:38Z","abstract_excerpt":"With the rise of real-world human-AI interaction applications, such as AI assistants, the need for Streaming Video Dialogue is critical. To address this need, we introduce StreamMind, a video LLM framework that achieves ultra-FPS streaming video processing (100 fps on a single A100) and enables proactive, always-on responses in real time, without explicit user intervention.\n  To solve the key challenge of the contradiction between linear video streaming speed and quadratic transformer computation cost, we propose a novel perception-cognition interleaving paradigm named ''event-gated LLM invoca"},"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":"2503.06220","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-08T13:44:38Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"40ec32296ac133358afd53e51e9330e36a6b0cbb3ae235597c0e4fe82b11c56d","abstract_canon_sha256":"a3e788852a7ca5ef26065845c35b3a1e52c6b2a46c7584cef90705c90f288b1c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:06:07.624796Z","signature_b64":"X0caRBWpiq08TORYeNHu0AtsakLMVtijbcNn22me2Z0Rnod7SC+iACiOq0MM57ChPNhM94QGYAYbnSSgb2DXDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1ed8aab0c8eeefb9c6c9ab32f5820e6004815f0541e184730e41d11259543a16","last_reissued_at":"2026-07-05T12:06:07.624289Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:06:07.624289Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"StreamMind: Unlocking Full Frame Rate Streaming Video Dialogue through Event-Gated Cognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Donglin Bai, Hao Wu, Shiqi Jiang, Ting Cao, Xin Ding, Yifan Yang, Zhibo Chen","submitted_at":"2025-03-08T13:44:38Z","abstract_excerpt":"With the rise of real-world human-AI interaction applications, such as AI assistants, the need for Streaming Video Dialogue is critical. To address this need, we introduce StreamMind, a video LLM framework that achieves ultra-FPS streaming video processing (100 fps on a single A100) and enables proactive, always-on responses in real time, without explicit user intervention.\n  To solve the key challenge of the contradiction between linear video streaming speed and quadratic transformer computation cost, we propose a novel perception-cognition interleaving paradigm named ''event-gated LLM invoca"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.06220","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/2503.06220/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":"2503.06220","created_at":"2026-07-05T12:06:07.624355+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.06220v3","created_at":"2026-07-05T12:06:07.624355+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.06220","created_at":"2026-07-05T12:06:07.624355+00:00"},{"alias_kind":"pith_short_12","alias_value":"D3MKVMGI53X3","created_at":"2026-07-05T12:06:07.624355+00:00"},{"alias_kind":"pith_short_16","alias_value":"D3MKVMGI53X3TRWJ","created_at":"2026-07-05T12:06:07.624355+00:00"},{"alias_kind":"pith_short_8","alias_value":"D3MKVMGI","created_at":"2026-07-05T12:06:07.624355+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.17798","citing_title":"LiveStarPro: Proactive Streaming Video Understanding with Hierarchical Memory for Long-Horizon Streams","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2606.09547","citing_title":"Streaming Interventions: Can Video Large Language Models Correct Mistakes as They Occur?","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2606.06991","citing_title":"Don't Pause: Streaming Video-Language Synchrony for Online Video Understanding","ref_index":27,"is_internal_anchor":false},{"citing_arxiv_id":"2511.21998","citing_title":"Can Multi-Modal LLMs Provide Live Step-by-Step Task Guidance?","ref_index":14,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/D3MKVMGI53X3TRWJVMZPLAQOMA","json":"https://pith.science/pith/D3MKVMGI53X3TRWJVMZPLAQOMA.json","graph_json":"https://pith.science/api/pith-number/D3MKVMGI53X3TRWJVMZPLAQOMA/graph.json","events_json":"https://pith.science/api/pith-number/D3MKVMGI53X3TRWJVMZPLAQOMA/events.json","paper":"https://pith.science/paper/D3MKVMGI"},"agent_actions":{"view_html":"https://pith.science/pith/D3MKVMGI53X3TRWJVMZPLAQOMA","download_json":"https://pith.science/pith/D3MKVMGI53X3TRWJVMZPLAQOMA.json","view_paper":"https://pith.science/paper/D3MKVMGI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.06220&json=true","fetch_graph":"https://pith.science/api/pith-number/D3MKVMGI53X3TRWJVMZPLAQOMA/graph.json","fetch_events":"https://pith.science/api/pith-number/D3MKVMGI53X3TRWJVMZPLAQOMA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D3MKVMGI53X3TRWJVMZPLAQOMA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D3MKVMGI53X3TRWJVMZPLAQOMA/action/storage_attestation","attest_author":"https://pith.science/pith/D3MKVMGI53X3TRWJVMZPLAQOMA/action/author_attestation","sign_citation":"https://pith.science/pith/D3MKVMGI53X3TRWJVMZPLAQOMA/action/citation_signature","submit_replication":"https://pith.science/pith/D3MKVMGI53X3TRWJVMZPLAQOMA/action/replication_record"}},"created_at":"2026-07-05T12:06:07.624355+00:00","updated_at":"2026-07-05T12:06:07.624355+00:00"}