{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:POYIYXO66TJS6OMXHJ6YGXYWEJ","short_pith_number":"pith:POYIYXO6","schema_version":"1.0","canonical_sha256":"7bb08c5ddef4d32f39973a7d835f16224d394c56510095d218856f987ecfb45b","source":{"kind":"arxiv","id":"2607.00811","version":1},"attestation_state":"computed","paper":{"title":"From Pixels to Temporal Correlations: Learning Informative Representations for Reinforcement Learning Pre-training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jinwen Wang, Kai Lv, Sheng Han, Shuo Wang, Siyu Yang, Xiaobo Hu, Youfang Lin","submitted_at":"2026-07-01T11:38:58Z","abstract_excerpt":"Unsupervised pre-training on large-scale datasets has demonstrated significant potential for improving the sample efficiency and performance of Reinforcement Learning (RL). Given the large-scale action-free internet videos, existing methods utilize single-step transition prediction and image reconstruction to learn representations. However, these methods prefer to preserve large-proportion stationary information in the pixel space, neglecting small but crucial information. To preserve enough information in the representation, it is essential to pay equal attention to each element in videos. Sp"},"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":"2607.00811","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-01T11:38:58Z","cross_cats_sorted":[],"title_canon_sha256":"ce3d745ac159954d2c68b766a1c258d038d817f3a1cacac19289b7d5c1ffd303","abstract_canon_sha256":"52c12ad7bc5ee2fe0e7ca223665b5da7ca370cd0569722b6c3472c1dce69b4a8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-02T01:17:56.054709Z","signature_b64":"Ev4p0cYXg7TP9MfU8+Y2YSQjQPvQL2p2G/xBCdHO2ASnsjOh4UAfGOtVDXbn8xb3NZKHjE0UPFkdbFUT1Bw+AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7bb08c5ddef4d32f39973a7d835f16224d394c56510095d218856f987ecfb45b","last_reissued_at":"2026-07-02T01:17:56.054262Z","signature_status":"signed_v1","first_computed_at":"2026-07-02T01:17:56.054262Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"From Pixels to Temporal Correlations: Learning Informative Representations for Reinforcement Learning Pre-training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jinwen Wang, Kai Lv, Sheng Han, Shuo Wang, Siyu Yang, Xiaobo Hu, Youfang Lin","submitted_at":"2026-07-01T11:38:58Z","abstract_excerpt":"Unsupervised pre-training on large-scale datasets has demonstrated significant potential for improving the sample efficiency and performance of Reinforcement Learning (RL). Given the large-scale action-free internet videos, existing methods utilize single-step transition prediction and image reconstruction to learn representations. However, these methods prefer to preserve large-proportion stationary information in the pixel space, neglecting small but crucial information. To preserve enough information in the representation, it is essential to pay equal attention to each element in videos. Sp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.00811","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/2607.00811/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":"2607.00811","created_at":"2026-07-02T01:17:56.054331+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.00811v1","created_at":"2026-07-02T01:17:56.054331+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.00811","created_at":"2026-07-02T01:17:56.054331+00:00"},{"alias_kind":"pith_short_12","alias_value":"POYIYXO66TJS","created_at":"2026-07-02T01:17:56.054331+00:00"},{"alias_kind":"pith_short_16","alias_value":"POYIYXO66TJS6OMX","created_at":"2026-07-02T01:17:56.054331+00:00"},{"alias_kind":"pith_short_8","alias_value":"POYIYXO6","created_at":"2026-07-02T01:17:56.054331+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/POYIYXO66TJS6OMXHJ6YGXYWEJ","json":"https://pith.science/pith/POYIYXO66TJS6OMXHJ6YGXYWEJ.json","graph_json":"https://pith.science/api/pith-number/POYIYXO66TJS6OMXHJ6YGXYWEJ/graph.json","events_json":"https://pith.science/api/pith-number/POYIYXO66TJS6OMXHJ6YGXYWEJ/events.json","paper":"https://pith.science/paper/POYIYXO6"},"agent_actions":{"view_html":"https://pith.science/pith/POYIYXO66TJS6OMXHJ6YGXYWEJ","download_json":"https://pith.science/pith/POYIYXO66TJS6OMXHJ6YGXYWEJ.json","view_paper":"https://pith.science/paper/POYIYXO6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.00811&json=true","fetch_graph":"https://pith.science/api/pith-number/POYIYXO66TJS6OMXHJ6YGXYWEJ/graph.json","fetch_events":"https://pith.science/api/pith-number/POYIYXO66TJS6OMXHJ6YGXYWEJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/POYIYXO66TJS6OMXHJ6YGXYWEJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/POYIYXO66TJS6OMXHJ6YGXYWEJ/action/storage_attestation","attest_author":"https://pith.science/pith/POYIYXO66TJS6OMXHJ6YGXYWEJ/action/author_attestation","sign_citation":"https://pith.science/pith/POYIYXO66TJS6OMXHJ6YGXYWEJ/action/citation_signature","submit_replication":"https://pith.science/pith/POYIYXO66TJS6OMXHJ6YGXYWEJ/action/replication_record"}},"created_at":"2026-07-02T01:17:56.054331+00:00","updated_at":"2026-07-02T01:17:56.054331+00:00"}