{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:QTS6PQ3SRI7H5XO2DWZJUQI27G","short_pith_number":"pith:QTS6PQ3S","schema_version":"1.0","canonical_sha256":"84e5e7c3728a3e7eddda1db29a411af9a76840da4d6392774de9599dfea16cf7","source":{"kind":"arxiv","id":"2505.22111","version":2},"attestation_state":"computed","paper":{"title":"Autoregression-free video prediction using diffusion model for mitigating error propagation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Il Yong Chun, Jin Bok Park, Woonho Ko","submitted_at":"2025-05-28T08:40:38Z","abstract_excerpt":"Existing long-term video prediction methods often rely on an autoregressive video prediction mechanism. However, this approach suffers from error propagation, particularly in distant future frames. To address this limitation, this paper proposes the first AutoRegression-Free (ARFree) video prediction framework using diffusion models. Different from an autoregressive video prediction mechanism, ARFree directly predicts any future frame tuples from the context frame tuple. The proposed ARFree consists of two key components: 1) a motion prediction module that predicts a future motion using motion"},"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":"2505.22111","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-28T08:40:38Z","cross_cats_sorted":[],"title_canon_sha256":"fcdcc6b16c124dbd336f57e796e139a265fcbdb61e6ca5c3a89bb258d1512ac0","abstract_canon_sha256":"49d56ea88f571777f4638d8111aabf21ed6a15e45bf10068c32fdce694a53321"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:12:34.005059Z","signature_b64":"XZ/GccwaFmKpAbtap9GrLRRu/1OfBDTNmYuM9hsAjjKKbY+aird1NztwfEGqn+DJnCGnskES2a4mzIa041UEBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"84e5e7c3728a3e7eddda1db29a411af9a76840da4d6392774de9599dfea16cf7","last_reissued_at":"2026-07-05T11:12:34.004499Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:12:34.004499Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Autoregression-free video prediction using diffusion model for mitigating error propagation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Il Yong Chun, Jin Bok Park, Woonho Ko","submitted_at":"2025-05-28T08:40:38Z","abstract_excerpt":"Existing long-term video prediction methods often rely on an autoregressive video prediction mechanism. However, this approach suffers from error propagation, particularly in distant future frames. To address this limitation, this paper proposes the first AutoRegression-Free (ARFree) video prediction framework using diffusion models. Different from an autoregressive video prediction mechanism, ARFree directly predicts any future frame tuples from the context frame tuple. The proposed ARFree consists of two key components: 1) a motion prediction module that predicts a future motion using motion"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.22111","kind":"arxiv","version":2},"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/2505.22111/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":"2505.22111","created_at":"2026-07-05T11:12:34.004562+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.22111v2","created_at":"2026-07-05T11:12:34.004562+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.22111","created_at":"2026-07-05T11:12:34.004562+00:00"},{"alias_kind":"pith_short_12","alias_value":"QTS6PQ3SRI7H","created_at":"2026-07-05T11:12:34.004562+00:00"},{"alias_kind":"pith_short_16","alias_value":"QTS6PQ3SRI7H5XO2","created_at":"2026-07-05T11:12:34.004562+00:00"},{"alias_kind":"pith_short_8","alias_value":"QTS6PQ3S","created_at":"2026-07-05T11:12:34.004562+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.22111","citing_title":"Autoregression-free video prediction using diffusion model for mitigating error propagation","ref_index":1,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QTS6PQ3SRI7H5XO2DWZJUQI27G","json":"https://pith.science/pith/QTS6PQ3SRI7H5XO2DWZJUQI27G.json","graph_json":"https://pith.science/api/pith-number/QTS6PQ3SRI7H5XO2DWZJUQI27G/graph.json","events_json":"https://pith.science/api/pith-number/QTS6PQ3SRI7H5XO2DWZJUQI27G/events.json","paper":"https://pith.science/paper/QTS6PQ3S"},"agent_actions":{"view_html":"https://pith.science/pith/QTS6PQ3SRI7H5XO2DWZJUQI27G","download_json":"https://pith.science/pith/QTS6PQ3SRI7H5XO2DWZJUQI27G.json","view_paper":"https://pith.science/paper/QTS6PQ3S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.22111&json=true","fetch_graph":"https://pith.science/api/pith-number/QTS6PQ3SRI7H5XO2DWZJUQI27G/graph.json","fetch_events":"https://pith.science/api/pith-number/QTS6PQ3SRI7H5XO2DWZJUQI27G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QTS6PQ3SRI7H5XO2DWZJUQI27G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QTS6PQ3SRI7H5XO2DWZJUQI27G/action/storage_attestation","attest_author":"https://pith.science/pith/QTS6PQ3SRI7H5XO2DWZJUQI27G/action/author_attestation","sign_citation":"https://pith.science/pith/QTS6PQ3SRI7H5XO2DWZJUQI27G/action/citation_signature","submit_replication":"https://pith.science/pith/QTS6PQ3SRI7H5XO2DWZJUQI27G/action/replication_record"}},"created_at":"2026-07-05T11:12:34.004562+00:00","updated_at":"2026-07-05T11:12:34.004562+00:00"}