{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZRTPPAT7UIVT43ZDQWLEFKWRMX","short_pith_number":"pith:ZRTPPAT7","schema_version":"1.0","canonical_sha256":"cc66f7827fa22b3e6f23859642aad165e99f85ecc1f0682d55e8d9dc07b3c42d","source":{"kind":"arxiv","id":"2402.15506","version":4},"attestation_state":"computed","paper":{"title":"AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.AI","authors_text":"Caiming Xiong, Huan Wang, Jianguo Zhang, Juan Carlos Niebles, Juntao Tan, Liangwei Yang, Ming Zhu, Rithesh Murthy, Shelby Heinecke, Shirley Kokane, Silvio Savarese, Thai Hoang, Tian Lan, Tulika Awalgaonkar, Weiran Yao, Yihao Feng, Zhiwei Liu, Zuxin Liu","submitted_at":"2024-02-23T18:56:26Z","abstract_excerpt":"Autonomous agents powered by large language models (LLMs) have garnered significant research attention. However, fully harnessing the potential of LLMs for agent-based tasks presents inherent challenges due to the heterogeneous nature of diverse data sources featuring multi-turn trajectories. In this paper, we introduce \\textbf{AgentOhana} as a comprehensive solution to address these challenges. \\textit{AgentOhana} aggregates agent trajectories from distinct environments, spanning a wide array of scenarios. It meticulously standardizes and unifies these trajectories into a consistent format, s"},"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":"2402.15506","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.AI","submitted_at":"2024-02-23T18:56:26Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"64d6aca8240fe9374f7be0f49b2c0bc8768ea2809b1dabd1dff278262c9f31cb","abstract_canon_sha256":"3ad472b0333af3fc954fa087e84dff5e7321834f60068e48bedbce18245b4c0d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:33:08.365109Z","signature_b64":"2t63JykP5LtP2ykLwDm7qUMgXJb4FpanlHZVNvAxoOzpiu3wUyaeoq0fXlqhKVCtQVUBJiwI5ZpxKWKSpDT9Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cc66f7827fa22b3e6f23859642aad165e99f85ecc1f0682d55e8d9dc07b3c42d","last_reissued_at":"2026-07-05T09:33:08.364481Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:33:08.364481Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.AI","authors_text":"Caiming Xiong, Huan Wang, Jianguo Zhang, Juan Carlos Niebles, Juntao Tan, Liangwei Yang, Ming Zhu, Rithesh Murthy, Shelby Heinecke, Shirley Kokane, Silvio Savarese, Thai Hoang, Tian Lan, Tulika Awalgaonkar, Weiran Yao, Yihao Feng, Zhiwei Liu, Zuxin Liu","submitted_at":"2024-02-23T18:56:26Z","abstract_excerpt":"Autonomous agents powered by large language models (LLMs) have garnered significant research attention. However, fully harnessing the potential of LLMs for agent-based tasks presents inherent challenges due to the heterogeneous nature of diverse data sources featuring multi-turn trajectories. In this paper, we introduce \\textbf{AgentOhana} as a comprehensive solution to address these challenges. \\textit{AgentOhana} aggregates agent trajectories from distinct environments, spanning a wide array of scenarios. It meticulously standardizes and unifies these trajectories into a consistent format, s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.15506","kind":"arxiv","version":4},"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/2402.15506/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":"2402.15506","created_at":"2026-07-05T09:33:08.364545+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.15506v4","created_at":"2026-07-05T09:33:08.364545+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.15506","created_at":"2026-07-05T09:33:08.364545+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZRTPPAT7UIVT","created_at":"2026-07-05T09:33:08.364545+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZRTPPAT7UIVT43ZD","created_at":"2026-07-05T09:33:08.364545+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZRTPPAT7","created_at":"2026-07-05T09:33:08.364545+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.31392","citing_title":"ReGRPO: Reflection-Augmented Policy Optimization for Tool-Using Agents","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2605.24828","citing_title":"Test-Time Deep Thinking to Explore Implicit Rules","ref_index":46,"is_internal_anchor":false},{"citing_arxiv_id":"2410.23218","citing_title":"OS-ATLAS: A Foundation Action Model for Generalist GUI Agents","ref_index":112,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZRTPPAT7UIVT43ZDQWLEFKWRMX","json":"https://pith.science/pith/ZRTPPAT7UIVT43ZDQWLEFKWRMX.json","graph_json":"https://pith.science/api/pith-number/ZRTPPAT7UIVT43ZDQWLEFKWRMX/graph.json","events_json":"https://pith.science/api/pith-number/ZRTPPAT7UIVT43ZDQWLEFKWRMX/events.json","paper":"https://pith.science/paper/ZRTPPAT7"},"agent_actions":{"view_html":"https://pith.science/pith/ZRTPPAT7UIVT43ZDQWLEFKWRMX","download_json":"https://pith.science/pith/ZRTPPAT7UIVT43ZDQWLEFKWRMX.json","view_paper":"https://pith.science/paper/ZRTPPAT7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.15506&json=true","fetch_graph":"https://pith.science/api/pith-number/ZRTPPAT7UIVT43ZDQWLEFKWRMX/graph.json","fetch_events":"https://pith.science/api/pith-number/ZRTPPAT7UIVT43ZDQWLEFKWRMX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZRTPPAT7UIVT43ZDQWLEFKWRMX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZRTPPAT7UIVT43ZDQWLEFKWRMX/action/storage_attestation","attest_author":"https://pith.science/pith/ZRTPPAT7UIVT43ZDQWLEFKWRMX/action/author_attestation","sign_citation":"https://pith.science/pith/ZRTPPAT7UIVT43ZDQWLEFKWRMX/action/citation_signature","submit_replication":"https://pith.science/pith/ZRTPPAT7UIVT43ZDQWLEFKWRMX/action/replication_record"}},"created_at":"2026-07-05T09:33:08.364545+00:00","updated_at":"2026-07-05T09:33:08.364545+00:00"}