{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:SYUC6JHPCGLPMJCKIK4BH76HSL","short_pith_number":"pith:SYUC6JHP","schema_version":"1.0","canonical_sha256":"96282f24ef1196f6244a42b813ffc792c70a856a7ad4d41a56c66cce34fec2d6","source":{"kind":"arxiv","id":"2606.31101","version":1},"attestation_state":"computed","paper":{"title":"Efficient Sim-to-Real Transfer of World-Action Models from Synthetic Priors","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Jinghuan Shang, Karl Schmeckpeper, Kausik Sivakumar, Ran Gong, Xiaohan Zhang, Yafei Hu, Zhaoming Xie, Zixing Wang","submitted_at":"2026-06-30T03:49:31Z","abstract_excerpt":"Bridging the sim-to-real gap is a core challenge in deploying learned manipulation policies. Sim-to-real learning is attractive because it can replace expensive real robot demonstrations with scalable synthetic data, yet world-action models have not previously been shown to transfer from simulation to real robotic manipulation. We study whether a world-action model can be trained from synthetic priors and deployed zero-shot in the real world. To this end, we build upon Cosmos Policy, a video diffusion model adapted for visuomotor control. We construct simulation environments with extensive dom"},"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":"2606.31101","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-06-30T03:49:31Z","cross_cats_sorted":[],"title_canon_sha256":"1daeb7401aa7860ec89849a3d216336232064e9c05665eb06b056fb652004a28","abstract_canon_sha256":"54111e52616438e9c15f40267ce74d70c67863ca70b8e0420ce4a8186d9a90a0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-01T01:17:29.207731Z","signature_b64":"aDe5RV7W33vy7hmokW8h0IlxBBD8oSV/ad8fGSkinFRitV+Ew/EtMtVzR+ozOScOYK6iv4XO6iencdubiesWAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"96282f24ef1196f6244a42b813ffc792c70a856a7ad4d41a56c66cce34fec2d6","last_reissued_at":"2026-07-01T01:17:29.207226Z","signature_status":"signed_v1","first_computed_at":"2026-07-01T01:17:29.207226Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Sim-to-Real Transfer of World-Action Models from Synthetic Priors","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Jinghuan Shang, Karl Schmeckpeper, Kausik Sivakumar, Ran Gong, Xiaohan Zhang, Yafei Hu, Zhaoming Xie, Zixing Wang","submitted_at":"2026-06-30T03:49:31Z","abstract_excerpt":"Bridging the sim-to-real gap is a core challenge in deploying learned manipulation policies. Sim-to-real learning is attractive because it can replace expensive real robot demonstrations with scalable synthetic data, yet world-action models have not previously been shown to transfer from simulation to real robotic manipulation. We study whether a world-action model can be trained from synthetic priors and deployed zero-shot in the real world. To this end, we build upon Cosmos Policy, a video diffusion model adapted for visuomotor control. We construct simulation environments with extensive dom"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.31101","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/2606.31101/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":"2606.31101","created_at":"2026-07-01T01:17:29.207298+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.31101v1","created_at":"2026-07-01T01:17:29.207298+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.31101","created_at":"2026-07-01T01:17:29.207298+00:00"},{"alias_kind":"pith_short_12","alias_value":"SYUC6JHPCGLP","created_at":"2026-07-01T01:17:29.207298+00:00"},{"alias_kind":"pith_short_16","alias_value":"SYUC6JHPCGLPMJCK","created_at":"2026-07-01T01:17:29.207298+00:00"},{"alias_kind":"pith_short_8","alias_value":"SYUC6JHP","created_at":"2026-07-01T01:17:29.207298+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/SYUC6JHPCGLPMJCKIK4BH76HSL","json":"https://pith.science/pith/SYUC6JHPCGLPMJCKIK4BH76HSL.json","graph_json":"https://pith.science/api/pith-number/SYUC6JHPCGLPMJCKIK4BH76HSL/graph.json","events_json":"https://pith.science/api/pith-number/SYUC6JHPCGLPMJCKIK4BH76HSL/events.json","paper":"https://pith.science/paper/SYUC6JHP"},"agent_actions":{"view_html":"https://pith.science/pith/SYUC6JHPCGLPMJCKIK4BH76HSL","download_json":"https://pith.science/pith/SYUC6JHPCGLPMJCKIK4BH76HSL.json","view_paper":"https://pith.science/paper/SYUC6JHP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.31101&json=true","fetch_graph":"https://pith.science/api/pith-number/SYUC6JHPCGLPMJCKIK4BH76HSL/graph.json","fetch_events":"https://pith.science/api/pith-number/SYUC6JHPCGLPMJCKIK4BH76HSL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SYUC6JHPCGLPMJCKIK4BH76HSL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SYUC6JHPCGLPMJCKIK4BH76HSL/action/storage_attestation","attest_author":"https://pith.science/pith/SYUC6JHPCGLPMJCKIK4BH76HSL/action/author_attestation","sign_citation":"https://pith.science/pith/SYUC6JHPCGLPMJCKIK4BH76HSL/action/citation_signature","submit_replication":"https://pith.science/pith/SYUC6JHPCGLPMJCKIK4BH76HSL/action/replication_record"}},"created_at":"2026-07-01T01:17:29.207298+00:00","updated_at":"2026-07-01T01:17:29.207298+00:00"}