{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:7T3FOS2RQBBK6QUVHWCRVMNFMP","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"16dd01db064d2c62aee7b939ec6d294e3ef075f723ec58306d3e5f200272cc2c","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-10-11T09:13:10Z","title_canon_sha256":"3019f14dbb11216663c9ef08481998aa772d3e8d4855973c51901e64ae6d4311"},"schema_version":"1.0","source":{"id":"2510.10125","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2510.10125","created_at":"2026-05-17T23:38:49Z"},{"alias_kind":"arxiv_version","alias_value":"2510.10125v3","created_at":"2026-05-17T23:38:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.10125","created_at":"2026-05-17T23:38:49Z"},{"alias_kind":"pith_short_12","alias_value":"7T3FOS2RQBBK","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"7T3FOS2RQBBK6QUV","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"7T3FOS2R","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:1d26608735ff751b775e48d9a1cc62355d0a54d2922864da3564be617a2e1a89","target":"graph","created_at":"2026-05-17T23:38:49Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"By synthesizing successful trajectories in imagination and using them for supervised fine-tuning, our approach can improve policy success by 44.7%."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The generated trajectories are sufficiently accurate proxies for real-world dynamics on novel objects, instructions, and camera placements to enable reliable policy ranking and effective fine-tuning."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A controllable world model trained on the DROID dataset generates consistent multi-view robot trajectories for over 20 seconds and improves generalist policy success rates by 44.7% via imagined trajectory fine-tuning."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A controllable world model ranks robot policies and improves them by 44.7 percent through imagined trajectories alone."}],"snapshot_sha256":"151df77c49b7b3312d0c27b87f6f77990c2fcd09b51e91c45bd4a7dd8afada08"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"5f4eaeb564048631af71e0063430757edbdb147dd3d0e57b12560c030c4487d8"},"paper":{"abstract_excerpt":"Generalist robot policies can now perform a wide range of manipulation skills, but evaluating and improving their ability with unfamiliar objects and instructions remains a significant challenge. Rigorous evaluation requires a large number of real-world rollouts, while systematic improvement demands additional corrective data with expert labels. Both of these processes are slow, costly, and difficult to scale. World models offer a promising, scalable alternative by enabling policies to rollout within imagination space. However, a key challenge is building a controllable world model that can ha","authors_text":"Chelsea Finn, Jianyu Chen, Lucy Xiaoyang Shi, Yanjiang Guo","cross_cats":["cs.AI"],"headline":"A controllable world model ranks robot policies and improves them by 44.7 percent through imagined trajectories alone.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-10-11T09:13:10Z","title":"Ctrl-World: A Controllable Generative World Model for Robot Manipulation"},"references":{"count":56,"internal_anchors":32,"resolved_work":56,"sample":[{"cited_arxiv_id":"2501.03575","doi":"","is_internal_anchor":true,"ref_index":1,"title":"Cosmos World Foundation Model Platform for Physical AI","work_id":"a2dba24c-318d-476a-8b21-4289c265810c","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"RoboArena: Distributed real-world evaluation of generalist robot policies","work_id":"a02af411-4d93-4ac8-a15c-930c8f021765","year":null},{"cited_arxiv_id":"2409.16283","doi":"","is_internal_anchor":true,"ref_index":3,"title":"Gen2Act: Human Video Generation in Novel Scenarios enables Generalizable Robot Manipulation","work_id":"a3bde288-aace-40db-8067-3ae6656f9509","year":null},{"cited_arxiv_id":"2310.10639","doi":"","is_internal_anchor":true,"ref_index":4,"title":"Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Models","work_id":"954b4359-f4ed-4c73-ae5b-f75d486b6fc8","year":null},{"cited_arxiv_id":"2410.24164","doi":"","is_internal_anchor":true,"ref_index":5,"title":"$\\pi_0$: A Vision-Language-Action Flow Model for General Robot Control","work_id":"f790abdc-a796-482f-a40d-f8ee035ecfc2","year":null}],"snapshot_sha256":"243ff9ac1de778d6328c913e9251ca801609863084055bc3a4127ff3483d2c95"},"source":{"id":"2510.10125","kind":"arxiv","version":3},"verdict":{"created_at":"2026-05-16T01:08:54.062305Z","id":"f26e11c6-4ee4-47a8-b897-324ba90446e0","model_set":{"reader":"grok-4.3"},"one_line_summary":"A controllable world model trained on the DROID dataset generates consistent multi-view robot trajectories for over 20 seconds and improves generalist policy success rates by 44.7% via imagined trajectory fine-tuning.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A controllable world model ranks robot policies and improves them by 44.7 percent through imagined trajectories alone.","strongest_claim":"By synthesizing successful trajectories in imagination and using them for supervised fine-tuning, our approach can improve policy success by 44.7%.","weakest_assumption":"The generated trajectories are sufficiently accurate proxies for real-world dynamics on novel objects, instructions, and camera placements to enable reliable policy ranking and effective fine-tuning."}},"verdict_id":"f26e11c6-4ee4-47a8-b897-324ba90446e0"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:db670dd14bffa3b8ac78dcf5ee61b34364bfd7285253d75db6764fb2621911c7","target":"record","created_at":"2026-05-17T23:38:49Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"16dd01db064d2c62aee7b939ec6d294e3ef075f723ec58306d3e5f200272cc2c","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2025-10-11T09:13:10Z","title_canon_sha256":"3019f14dbb11216663c9ef08481998aa772d3e8d4855973c51901e64ae6d4311"},"schema_version":"1.0","source":{"id":"2510.10125","kind":"arxiv","version":3}},"canonical_sha256":"fcf6574b518042af42953d851ab1a563c626870a90cde4bf3aee4770022a874b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fcf6574b518042af42953d851ab1a563c626870a90cde4bf3aee4770022a874b","first_computed_at":"2026-05-17T23:38:49.505782Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:38:49.505782Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hg5q73HrISZKDMhoCJSOLIAV9TRwlaO9wGWFPD7qCJGeRYONyjmr2DK6apHQJHzmxI4NHL6ihrUDd9j0uliEDw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:38:49.506293Z","signed_message":"canonical_sha256_bytes"},"source_id":"2510.10125","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:db670dd14bffa3b8ac78dcf5ee61b34364bfd7285253d75db6764fb2621911c7","sha256:1d26608735ff751b775e48d9a1cc62355d0a54d2922864da3564be617a2e1a89"],"state_sha256":"90de34541d3d19251a8cdc67c9e67e764f44e952d305fd81dabcb240d2cc9e5e"}