{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:NYS7BWZ2TCA6BB5HGMEYWLCW33","short_pith_number":"pith:NYS7BWZ2","schema_version":"1.0","canonical_sha256":"6e25f0db3a9881e087a733098b2c56dec7a7b1a82bb6bb995cd2e78c458105eb","source":{"kind":"arxiv","id":"2607.15275","version":1},"attestation_state":"computed","paper":{"title":"RoboTTT: Context Scaling for Robot Policies","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.RO","authors_text":"Fengyuan Hu, Jimmy Wu, Li Fei-Fei, Linxi \"Jim\" Fan, Ruijie Zheng, Scott Reed, Tianyuan Dai, Yevgen Chebotar, Yuke Zhu, Yunfan Jiang, Yunhao Ge","submitted_at":"2026-07-16T17:59:06Z","abstract_excerpt":"Recent robot foundation models operate with single-step or short-history visuomotor context. We introduce Test-Time-Training Robot Policies (RoboTTT), a robot model and training recipe that scale visuomotor context to 8K timesteps, three orders of magnitude beyond state-of-the-art policies, without growing inference latency. At this context length, we unlock new robot capabilities: one-shot in-context imitation from human video demonstrations, on-the-fly policy improvement, robustness to perturbations, and stronger performance on multi-stage, long-horizon tasks. We also observe, for the first "},"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":"2607.15275","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-07-16T17:59:06Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"687997080e6c5dd1400c521728ce11c01ee53ea43c5436097974e2adcaaf68cd","abstract_canon_sha256":"dc9c90afaff24d18021d418042653a6c6d274b205cb58e5f0607e006727f4b77"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-17T01:22:18.188640Z","signature_b64":"74MUMMmgq0ktvksLSXObwJvkZNXmAV2BY4yZxSS5PoNtxhmEyAtrsrXLHiW7Y7Bh9Scb+It4HWoPetuDUSJUDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6e25f0db3a9881e087a733098b2c56dec7a7b1a82bb6bb995cd2e78c458105eb","last_reissued_at":"2026-07-17T01:22:18.187795Z","signature_status":"signed_v1","first_computed_at":"2026-07-17T01:22:18.187795Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RoboTTT: Context Scaling for Robot Policies","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.RO","authors_text":"Fengyuan Hu, Jimmy Wu, Li Fei-Fei, Linxi \"Jim\" Fan, Ruijie Zheng, Scott Reed, Tianyuan Dai, Yevgen Chebotar, Yuke Zhu, Yunfan Jiang, Yunhao Ge","submitted_at":"2026-07-16T17:59:06Z","abstract_excerpt":"Recent robot foundation models operate with single-step or short-history visuomotor context. We introduce Test-Time-Training Robot Policies (RoboTTT), a robot model and training recipe that scale visuomotor context to 8K timesteps, three orders of magnitude beyond state-of-the-art policies, without growing inference latency. At this context length, we unlock new robot capabilities: one-shot in-context imitation from human video demonstrations, on-the-fly policy improvement, robustness to perturbations, and stronger performance on multi-stage, long-horizon tasks. We also observe, for the first "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.15275","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/2607.15275/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":"2607.15275","created_at":"2026-07-17T01:22:18.188227+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.15275v1","created_at":"2026-07-17T01:22:18.188227+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.15275","created_at":"2026-07-17T01:22:18.188227+00:00"},{"alias_kind":"pith_short_12","alias_value":"NYS7BWZ2TCA6","created_at":"2026-07-17T01:22:18.188227+00:00"},{"alias_kind":"pith_short_16","alias_value":"NYS7BWZ2TCA6BB5H","created_at":"2026-07-17T01:22:18.188227+00:00"},{"alias_kind":"pith_short_8","alias_value":"NYS7BWZ2","created_at":"2026-07-17T01:22:18.188227+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/NYS7BWZ2TCA6BB5HGMEYWLCW33","json":"https://pith.science/pith/NYS7BWZ2TCA6BB5HGMEYWLCW33.json","graph_json":"https://pith.science/api/pith-number/NYS7BWZ2TCA6BB5HGMEYWLCW33/graph.json","events_json":"https://pith.science/api/pith-number/NYS7BWZ2TCA6BB5HGMEYWLCW33/events.json","paper":"https://pith.science/paper/NYS7BWZ2"},"agent_actions":{"view_html":"https://pith.science/pith/NYS7BWZ2TCA6BB5HGMEYWLCW33","download_json":"https://pith.science/pith/NYS7BWZ2TCA6BB5HGMEYWLCW33.json","view_paper":"https://pith.science/paper/NYS7BWZ2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.15275&json=true","fetch_graph":"https://pith.science/api/pith-number/NYS7BWZ2TCA6BB5HGMEYWLCW33/graph.json","fetch_events":"https://pith.science/api/pith-number/NYS7BWZ2TCA6BB5HGMEYWLCW33/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NYS7BWZ2TCA6BB5HGMEYWLCW33/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NYS7BWZ2TCA6BB5HGMEYWLCW33/action/storage_attestation","attest_author":"https://pith.science/pith/NYS7BWZ2TCA6BB5HGMEYWLCW33/action/author_attestation","sign_citation":"https://pith.science/pith/NYS7BWZ2TCA6BB5HGMEYWLCW33/action/citation_signature","submit_replication":"https://pith.science/pith/NYS7BWZ2TCA6BB5HGMEYWLCW33/action/replication_record"}},"created_at":"2026-07-17T01:22:18.188227+00:00","updated_at":"2026-07-17T01:22:18.188227+00:00"}