{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:A7KLK5VBPYBZKSKV24GV5VMF6Q","short_pith_number":"pith:A7KLK5VB","schema_version":"1.0","canonical_sha256":"07d4b576a17e03954955d70d5ed585f406286c4494f28d8ba2ebd815a88bdc2f","source":{"kind":"arxiv","id":"2607.17482","version":1},"attestation_state":"computed","paper":{"title":"Generative Transmission: Rethinking Computation, Bandwidth, and Memory in Communication","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chi Zhang, Haibin Huang, Jixiang Luo, Xiangyu Chen, Xuelong Li, Yuankai Fan","submitted_at":"2026-07-20T02:11:26Z","abstract_excerpt":"Under the AI Flow framework, communication is shifting from transmitting fidelity-oriented information flows toward delivering task-oriented and perception-oriented token flows across heterogeneous network resources. Video communication is a fundamental component of modern information networks. However, under ultra-low-bandwidth and weak-network conditions, conventional video coding and transmission methods, which are primarily optimized for pixel-level fidelity, often struggle to balance visual usability, transmission efficiency, and robustness to unstable links. With the rapid advancement of"},"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":"2607.17482","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-20T02:11:26Z","cross_cats_sorted":[],"title_canon_sha256":"1d3890e6770820476bffb69662d3a4f4a7e9f12cc66153cc764d6c247f111dd4","abstract_canon_sha256":"78880f4784fd5e93c5328464c8c34fbb758b0c31d91c5ef7195cc5778ddc24ce"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-21T01:21:47.738277Z","signature_b64":"gis8KDjRWw8Z+Va6Fpj76lGFDYZmBQMmRBLlOT5/Ehfmj8sDujH8a7c50Q6bdK14VMlI8wLG5C3La7KxadeuBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"07d4b576a17e03954955d70d5ed585f406286c4494f28d8ba2ebd815a88bdc2f","last_reissued_at":"2026-07-21T01:21:47.737368Z","signature_status":"signed_v1","first_computed_at":"2026-07-21T01:21:47.737368Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generative Transmission: Rethinking Computation, Bandwidth, and Memory in Communication","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chi Zhang, Haibin Huang, Jixiang Luo, Xiangyu Chen, Xuelong Li, Yuankai Fan","submitted_at":"2026-07-20T02:11:26Z","abstract_excerpt":"Under the AI Flow framework, communication is shifting from transmitting fidelity-oriented information flows toward delivering task-oriented and perception-oriented token flows across heterogeneous network resources. Video communication is a fundamental component of modern information networks. However, under ultra-low-bandwidth and weak-network conditions, conventional video coding and transmission methods, which are primarily optimized for pixel-level fidelity, often struggle to balance visual usability, transmission efficiency, and robustness to unstable links. With the rapid advancement of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.17482","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/2607.17482/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":"2607.17482","created_at":"2026-07-21T01:21:47.737815+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.17482v1","created_at":"2026-07-21T01:21:47.737815+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.17482","created_at":"2026-07-21T01:21:47.737815+00:00"},{"alias_kind":"pith_short_12","alias_value":"A7KLK5VBPYBZ","created_at":"2026-07-21T01:21:47.737815+00:00"},{"alias_kind":"pith_short_16","alias_value":"A7KLK5VBPYBZKSKV","created_at":"2026-07-21T01:21:47.737815+00:00"},{"alias_kind":"pith_short_8","alias_value":"A7KLK5VB","created_at":"2026-07-21T01:21:47.737815+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.23669","citing_title":"RRTrack: Robust and Recoverable Object 6D Pose Tracking for Dynamic Scenes","ref_index":28,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/A7KLK5VBPYBZKSKV24GV5VMF6Q","json":"https://pith.science/pith/A7KLK5VBPYBZKSKV24GV5VMF6Q.json","graph_json":"https://pith.science/api/pith-number/A7KLK5VBPYBZKSKV24GV5VMF6Q/graph.json","events_json":"https://pith.science/api/pith-number/A7KLK5VBPYBZKSKV24GV5VMF6Q/events.json","paper":"https://pith.science/paper/A7KLK5VB"},"agent_actions":{"view_html":"https://pith.science/pith/A7KLK5VBPYBZKSKV24GV5VMF6Q","download_json":"https://pith.science/pith/A7KLK5VBPYBZKSKV24GV5VMF6Q.json","view_paper":"https://pith.science/paper/A7KLK5VB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.17482&json=true","fetch_graph":"https://pith.science/api/pith-number/A7KLK5VBPYBZKSKV24GV5VMF6Q/graph.json","fetch_events":"https://pith.science/api/pith-number/A7KLK5VBPYBZKSKV24GV5VMF6Q/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A7KLK5VBPYBZKSKV24GV5VMF6Q/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A7KLK5VBPYBZKSKV24GV5VMF6Q/action/storage_attestation","attest_author":"https://pith.science/pith/A7KLK5VBPYBZKSKV24GV5VMF6Q/action/author_attestation","sign_citation":"https://pith.science/pith/A7KLK5VBPYBZKSKV24GV5VMF6Q/action/citation_signature","submit_replication":"https://pith.science/pith/A7KLK5VBPYBZKSKV24GV5VMF6Q/action/replication_record"}},"created_at":"2026-07-21T01:21:47.737815+00:00","updated_at":"2026-07-21T01:21:47.737815+00:00"}