{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:BINEDFD6IEC5L4NTJGF2OQB7JQ","short_pith_number":"pith:BINEDFD6","schema_version":"1.0","canonical_sha256":"0a1a41947e4105d5f1b3498ba7403f4c294d0cc36ea381cc2500030b5056e69f","source":{"kind":"arxiv","id":"2607.15271","version":1},"attestation_state":"computed","paper":{"title":"Online Neural Space Time Memory for Dynamic Novel View Synthesis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.GR","cs.LG"],"primary_cat":"cs.CV","authors_text":"Ali Behrouz, Baback Elmieh, Gabor Csapo, Lynn Tsai, Srinivas Kaza, Stephen Lombardi, Steven M. Seitz, Tiancheng Sun, Xuan Luo, Yuan Deng, Zeman Li","submitted_at":"2026-07-16T17:58:18Z","abstract_excerpt":"Online novel view synthesis from multi-view streaming videos faces a fundamental trade-off: maintaining a persistent, long-horizon memory to reconstruct temporarily occluded regions while operating under strict real-time constraints. While Test-Time Training (TTT) offers a powerful memory mechanism, standard models mandate gradient-based memory updates at every frame to adapt to the changing motion in dynamic scenes. The computational cost of heavy memory updates precludes real-time application and can lead to instability over long contexts. Given that memory updates are more demanding than me"},"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":"2607.15271","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-16T17:58:18Z","cross_cats_sorted":["cs.GR","cs.LG"],"title_canon_sha256":"e41625a5ec465112ecb393e1438d74b6bca5583206f91cecd4102ffd6305f38b","abstract_canon_sha256":"fe31b54014a78fc79399e582b11cec688d2154f620f5f4420860056a5b7e9f80"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-17T01:22:17.928307Z","signature_b64":"OxQlaKg+UitmqtuntQwpoISgrpLsVqF82pC0yila5pxkC7RQ5U1+65b6w4CahL4jtzDfG+PZ8fyErjeGssRJDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0a1a41947e4105d5f1b3498ba7403f4c294d0cc36ea381cc2500030b5056e69f","last_reissued_at":"2026-07-17T01:22:17.927456Z","signature_status":"signed_v1","first_computed_at":"2026-07-17T01:22:17.927456Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Online Neural Space Time Memory for Dynamic Novel View Synthesis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.GR","cs.LG"],"primary_cat":"cs.CV","authors_text":"Ali Behrouz, Baback Elmieh, Gabor Csapo, Lynn Tsai, Srinivas Kaza, Stephen Lombardi, Steven M. Seitz, Tiancheng Sun, Xuan Luo, Yuan Deng, Zeman Li","submitted_at":"2026-07-16T17:58:18Z","abstract_excerpt":"Online novel view synthesis from multi-view streaming videos faces a fundamental trade-off: maintaining a persistent, long-horizon memory to reconstruct temporarily occluded regions while operating under strict real-time constraints. While Test-Time Training (TTT) offers a powerful memory mechanism, standard models mandate gradient-based memory updates at every frame to adapt to the changing motion in dynamic scenes. The computational cost of heavy memory updates precludes real-time application and can lead to instability over long contexts. Given that memory updates are more demanding than me"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.15271","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/2607.15271/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":"2607.15271","created_at":"2026-07-17T01:22:17.927893+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.15271v1","created_at":"2026-07-17T01:22:17.927893+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.15271","created_at":"2026-07-17T01:22:17.927893+00:00"},{"alias_kind":"pith_short_12","alias_value":"BINEDFD6IEC5","created_at":"2026-07-17T01:22:17.927893+00:00"},{"alias_kind":"pith_short_16","alias_value":"BINEDFD6IEC5L4NT","created_at":"2026-07-17T01:22:17.927893+00:00"},{"alias_kind":"pith_short_8","alias_value":"BINEDFD6","created_at":"2026-07-17T01:22:17.927893+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/BINEDFD6IEC5L4NTJGF2OQB7JQ","json":"https://pith.science/pith/BINEDFD6IEC5L4NTJGF2OQB7JQ.json","graph_json":"https://pith.science/api/pith-number/BINEDFD6IEC5L4NTJGF2OQB7JQ/graph.json","events_json":"https://pith.science/api/pith-number/BINEDFD6IEC5L4NTJGF2OQB7JQ/events.json","paper":"https://pith.science/paper/BINEDFD6"},"agent_actions":{"view_html":"https://pith.science/pith/BINEDFD6IEC5L4NTJGF2OQB7JQ","download_json":"https://pith.science/pith/BINEDFD6IEC5L4NTJGF2OQB7JQ.json","view_paper":"https://pith.science/paper/BINEDFD6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.15271&json=true","fetch_graph":"https://pith.science/api/pith-number/BINEDFD6IEC5L4NTJGF2OQB7JQ/graph.json","fetch_events":"https://pith.science/api/pith-number/BINEDFD6IEC5L4NTJGF2OQB7JQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BINEDFD6IEC5L4NTJGF2OQB7JQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BINEDFD6IEC5L4NTJGF2OQB7JQ/action/storage_attestation","attest_author":"https://pith.science/pith/BINEDFD6IEC5L4NTJGF2OQB7JQ/action/author_attestation","sign_citation":"https://pith.science/pith/BINEDFD6IEC5L4NTJGF2OQB7JQ/action/citation_signature","submit_replication":"https://pith.science/pith/BINEDFD6IEC5L4NTJGF2OQB7JQ/action/replication_record"}},"created_at":"2026-07-17T01:22:17.927893+00:00","updated_at":"2026-07-17T01:22:17.927893+00:00"}