{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:TXUQIT7S63OMUG5RTEZLQRUHMD","short_pith_number":"pith:TXUQIT7S","schema_version":"1.0","canonical_sha256":"9de9044ff2f6dcca1bb19932b8468760f71ab3d733f4723caf9f642e6ffa0d6e","source":{"kind":"arxiv","id":"2607.22231","version":1},"attestation_state":"computed","paper":{"title":"TRaM-VSR: Importance-Aware Token Routing and Merging for One-Step Diffusion Video Super-Resolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Radu Timofte, Sicheng Gao, Tong Shen, Yixuan Liu, Zhuyun Zhou, Zongwei Wu","submitted_at":"2026-07-24T11:57:26Z","abstract_excerpt":"Video super-resolution (VSR) using large-scale Diffusion Transformer (DiT) priors achieves exceptional perceptual quality but is often impractical due to the quadratic computational cost of processing dense spatio-temporal token sequences. Existing efficiency-oriented methods risk irreversible detail loss and temporal flickering, a vulnerability especially pronounced in one-step diffusion models. To address this, we propose TRaM-VSR, a Token Routing and Merging framework for adaptive token allocation, leveraging both context-aware video priors and network-level priors. First, token importance "},"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":"2607.22231","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-24T11:57:26Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"08ac6b2d077a53459ede253aa86990b8b79a87fcea9d2faafb5569fd733abf94","abstract_canon_sha256":"6b27eb1520d8a41f867bdfed67043fc0e08d40cd55024e27c19e446e37557497"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-27T01:20:54.904880Z","signature_b64":"RZfzBr9CJwk5vFHkrlsBafk+qNPHzmXHzMzNBGi6s1Mw8dhptNcJ30Qyv2pkHvtqmAkWhIMcqcmwX4RKDyfCCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9de9044ff2f6dcca1bb19932b8468760f71ab3d733f4723caf9f642e6ffa0d6e","last_reissued_at":"2026-07-27T01:20:54.904004Z","signature_status":"signed_v1","first_computed_at":"2026-07-27T01:20:54.904004Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TRaM-VSR: Importance-Aware Token Routing and Merging for One-Step Diffusion Video Super-Resolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Radu Timofte, Sicheng Gao, Tong Shen, Yixuan Liu, Zhuyun Zhou, Zongwei Wu","submitted_at":"2026-07-24T11:57:26Z","abstract_excerpt":"Video super-resolution (VSR) using large-scale Diffusion Transformer (DiT) priors achieves exceptional perceptual quality but is often impractical due to the quadratic computational cost of processing dense spatio-temporal token sequences. Existing efficiency-oriented methods risk irreversible detail loss and temporal flickering, a vulnerability especially pronounced in one-step diffusion models. To address this, we propose TRaM-VSR, a Token Routing and Merging framework for adaptive token allocation, leveraging both context-aware video priors and network-level priors. First, token importance "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.22231","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/2607.22231/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":"2607.22231","created_at":"2026-07-27T01:20:54.904456+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.22231v1","created_at":"2026-07-27T01:20:54.904456+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.22231","created_at":"2026-07-27T01:20:54.904456+00:00"},{"alias_kind":"pith_short_12","alias_value":"TXUQIT7S63OM","created_at":"2026-07-27T01:20:54.904456+00:00"},{"alias_kind":"pith_short_16","alias_value":"TXUQIT7S63OMUG5R","created_at":"2026-07-27T01:20:54.904456+00:00"},{"alias_kind":"pith_short_8","alias_value":"TXUQIT7S","created_at":"2026-07-27T01:20:54.904456+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/TXUQIT7S63OMUG5RTEZLQRUHMD","json":"https://pith.science/pith/TXUQIT7S63OMUG5RTEZLQRUHMD.json","graph_json":"https://pith.science/api/pith-number/TXUQIT7S63OMUG5RTEZLQRUHMD/graph.json","events_json":"https://pith.science/api/pith-number/TXUQIT7S63OMUG5RTEZLQRUHMD/events.json","paper":"https://pith.science/paper/TXUQIT7S"},"agent_actions":{"view_html":"https://pith.science/pith/TXUQIT7S63OMUG5RTEZLQRUHMD","download_json":"https://pith.science/pith/TXUQIT7S63OMUG5RTEZLQRUHMD.json","view_paper":"https://pith.science/paper/TXUQIT7S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.22231&json=true","fetch_graph":"https://pith.science/api/pith-number/TXUQIT7S63OMUG5RTEZLQRUHMD/graph.json","fetch_events":"https://pith.science/api/pith-number/TXUQIT7S63OMUG5RTEZLQRUHMD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TXUQIT7S63OMUG5RTEZLQRUHMD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TXUQIT7S63OMUG5RTEZLQRUHMD/action/storage_attestation","attest_author":"https://pith.science/pith/TXUQIT7S63OMUG5RTEZLQRUHMD/action/author_attestation","sign_citation":"https://pith.science/pith/TXUQIT7S63OMUG5RTEZLQRUHMD/action/citation_signature","submit_replication":"https://pith.science/pith/TXUQIT7S63OMUG5RTEZLQRUHMD/action/replication_record"}},"created_at":"2026-07-27T01:20:54.904456+00:00","updated_at":"2026-07-27T01:20:54.904456+00:00"}