{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZZKDARHNHNDBGTFJSCWZJ6UE5U","short_pith_number":"pith:ZZKDARHN","schema_version":"1.0","canonical_sha256":"ce543044ed3b46134ca990ad94fa84ed3fb52b1947c14e0f1e227262fe460663","source":{"kind":"arxiv","id":"2405.18757","version":1},"attestation_state":"computed","paper":{"title":"Multi-objective Cross-task Learning via Goal-conditioned GPT-based Decision Transformers for Surgical Robot Task Automation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Jiawei Fu, Kai Chen, Qi Dou, Wang Wei, Yonghao Long","submitted_at":"2024-05-29T04:50:53Z","abstract_excerpt":"Surgical robot task automation has been a promising research topic for improving surgical efficiency and quality. Learning-based methods have been recognized as an interesting paradigm and been increasingly investigated. However, existing approaches encounter difficulties in long-horizon goal-conditioned tasks due to the intricate compositional structure, which requires decision-making for a sequence of sub-steps and understanding of inherent dynamics of goal-reaching tasks. In this paper, we propose a new learning-based framework by leveraging the strong reasoning capability of the GPT-based "},"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":"2405.18757","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-05-29T04:50:53Z","cross_cats_sorted":[],"title_canon_sha256":"bf4192622f7df6baea1803b1d69685bcba2ec11b69f6eb944d205f11bd4bf7e2","abstract_canon_sha256":"420727a2a624d870ccb6521291fdc3c7326f6c3045c867cf6dacddce163abe0c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:24:29.906500Z","signature_b64":"7+g4XxDhfTm7fD/jpBLidF9EhT1JJhkcenUMRye+dbMRa6CRPCYEwGanijrMvYrGg3Qmi3alOTm+1jtdgxIlDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ce543044ed3b46134ca990ad94fa84ed3fb52b1947c14e0f1e227262fe460663","last_reissued_at":"2026-07-05T08:24:29.906046Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:24:29.906046Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-objective Cross-task Learning via Goal-conditioned GPT-based Decision Transformers for Surgical Robot Task Automation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Jiawei Fu, Kai Chen, Qi Dou, Wang Wei, Yonghao Long","submitted_at":"2024-05-29T04:50:53Z","abstract_excerpt":"Surgical robot task automation has been a promising research topic for improving surgical efficiency and quality. Learning-based methods have been recognized as an interesting paradigm and been increasingly investigated. However, existing approaches encounter difficulties in long-horizon goal-conditioned tasks due to the intricate compositional structure, which requires decision-making for a sequence of sub-steps and understanding of inherent dynamics of goal-reaching tasks. In this paper, we propose a new learning-based framework by leveraging the strong reasoning capability of the GPT-based "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.18757","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/2405.18757/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":"2405.18757","created_at":"2026-07-05T08:24:29.906110+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.18757v1","created_at":"2026-07-05T08:24:29.906110+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.18757","created_at":"2026-07-05T08:24:29.906110+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZZKDARHNHNDB","created_at":"2026-07-05T08:24:29.906110+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZZKDARHNHNDBGTFJ","created_at":"2026-07-05T08:24:29.906110+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZZKDARHN","created_at":"2026-07-05T08:24:29.906110+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2409.13107","citing_title":"Towards Robust Surgical Automation via Digital Twin Representations from Foundation Models","ref_index":8,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZZKDARHNHNDBGTFJSCWZJ6UE5U","json":"https://pith.science/pith/ZZKDARHNHNDBGTFJSCWZJ6UE5U.json","graph_json":"https://pith.science/api/pith-number/ZZKDARHNHNDBGTFJSCWZJ6UE5U/graph.json","events_json":"https://pith.science/api/pith-number/ZZKDARHNHNDBGTFJSCWZJ6UE5U/events.json","paper":"https://pith.science/paper/ZZKDARHN"},"agent_actions":{"view_html":"https://pith.science/pith/ZZKDARHNHNDBGTFJSCWZJ6UE5U","download_json":"https://pith.science/pith/ZZKDARHNHNDBGTFJSCWZJ6UE5U.json","view_paper":"https://pith.science/paper/ZZKDARHN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.18757&json=true","fetch_graph":"https://pith.science/api/pith-number/ZZKDARHNHNDBGTFJSCWZJ6UE5U/graph.json","fetch_events":"https://pith.science/api/pith-number/ZZKDARHNHNDBGTFJSCWZJ6UE5U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZZKDARHNHNDBGTFJSCWZJ6UE5U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZZKDARHNHNDBGTFJSCWZJ6UE5U/action/storage_attestation","attest_author":"https://pith.science/pith/ZZKDARHNHNDBGTFJSCWZJ6UE5U/action/author_attestation","sign_citation":"https://pith.science/pith/ZZKDARHNHNDBGTFJSCWZJ6UE5U/action/citation_signature","submit_replication":"https://pith.science/pith/ZZKDARHNHNDBGTFJSCWZJ6UE5U/action/replication_record"}},"created_at":"2026-07-05T08:24:29.906110+00:00","updated_at":"2026-07-05T08:24:29.906110+00:00"}