{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KZKOC3EAFCRGG4WUB4RUG65U6I","short_pith_number":"pith:KZKOC3EA","schema_version":"1.0","canonical_sha256":"5654e16c8028a26372d40f23437bb4f22564d1adf8be223110b4373581c0a462","source":{"kind":"arxiv","id":"2404.06921","version":1},"attestation_state":"computed","paper":{"title":"GoEX: Perspectives and Designs Towards a Runtime for Autonomous LLM Applications","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Aaron Hao, Ion Stoica, Joseph E. Gonzalez, Martin Casado, Noppapon C., Raluca Ada Popa, Roy Huang, Shishir G. Patil, Tianjun Zhang, Vivian Fang","submitted_at":"2024-04-10T11:17:33Z","abstract_excerpt":"Large Language Models (LLMs) are evolving beyond their classical role of providing information within dialogue systems to actively engaging with tools and performing actions on real-world applications and services. Today, humans verify the correctness and appropriateness of the LLM-generated outputs (e.g., code, functions, or actions) before putting them into real-world execution. This poses significant challenges as code comprehension is well known to be notoriously difficult. In this paper, we study how humans can efficiently collaborate with, delegate to, and supervise autonomous LLMs in th"},"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":"2404.06921","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-04-10T11:17:33Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"ef32c03f53e1a95b7d0d5f6fbfa434702fdc90c31de7ad63a19573909d57362a","abstract_canon_sha256":"6532b3b4feb3195e585bca57b5a4834b77d33853e836ab84aa53c2e2954851d6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:06:32.284466Z","signature_b64":"0s1oOInfveEnkneL6zQDN133jxr/uxRd9j76//IcPUphOcdRoRegBofOBXgd65+a53aUgiMu/hXdLDplK5QeDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5654e16c8028a26372d40f23437bb4f22564d1adf8be223110b4373581c0a462","last_reissued_at":"2026-07-05T08:06:32.284017Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:06:32.284017Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GoEX: Perspectives and Designs Towards a Runtime for Autonomous LLM Applications","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Aaron Hao, Ion Stoica, Joseph E. Gonzalez, Martin Casado, Noppapon C., Raluca Ada Popa, Roy Huang, Shishir G. Patil, Tianjun Zhang, Vivian Fang","submitted_at":"2024-04-10T11:17:33Z","abstract_excerpt":"Large Language Models (LLMs) are evolving beyond their classical role of providing information within dialogue systems to actively engaging with tools and performing actions on real-world applications and services. Today, humans verify the correctness and appropriateness of the LLM-generated outputs (e.g., code, functions, or actions) before putting them into real-world execution. This poses significant challenges as code comprehension is well known to be notoriously difficult. In this paper, we study how humans can efficiently collaborate with, delegate to, and supervise autonomous LLMs in th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.06921","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/2404.06921/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":"2404.06921","created_at":"2026-07-05T08:06:32.284073+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.06921v1","created_at":"2026-07-05T08:06:32.284073+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.06921","created_at":"2026-07-05T08:06:32.284073+00:00"},{"alias_kind":"pith_short_12","alias_value":"KZKOC3EAFCRG","created_at":"2026-07-05T08:06:32.284073+00:00"},{"alias_kind":"pith_short_16","alias_value":"KZKOC3EAFCRGG4WU","created_at":"2026-07-05T08:06:32.284073+00:00"},{"alias_kind":"pith_short_8","alias_value":"KZKOC3EA","created_at":"2026-07-05T08:06:32.284073+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2603.00991","citing_title":"Tracking Capabilities for Safer Agents","ref_index":60,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KZKOC3EAFCRGG4WUB4RUG65U6I","json":"https://pith.science/pith/KZKOC3EAFCRGG4WUB4RUG65U6I.json","graph_json":"https://pith.science/api/pith-number/KZKOC3EAFCRGG4WUB4RUG65U6I/graph.json","events_json":"https://pith.science/api/pith-number/KZKOC3EAFCRGG4WUB4RUG65U6I/events.json","paper":"https://pith.science/paper/KZKOC3EA"},"agent_actions":{"view_html":"https://pith.science/pith/KZKOC3EAFCRGG4WUB4RUG65U6I","download_json":"https://pith.science/pith/KZKOC3EAFCRGG4WUB4RUG65U6I.json","view_paper":"https://pith.science/paper/KZKOC3EA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.06921&json=true","fetch_graph":"https://pith.science/api/pith-number/KZKOC3EAFCRGG4WUB4RUG65U6I/graph.json","fetch_events":"https://pith.science/api/pith-number/KZKOC3EAFCRGG4WUB4RUG65U6I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KZKOC3EAFCRGG4WUB4RUG65U6I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KZKOC3EAFCRGG4WUB4RUG65U6I/action/storage_attestation","attest_author":"https://pith.science/pith/KZKOC3EAFCRGG4WUB4RUG65U6I/action/author_attestation","sign_citation":"https://pith.science/pith/KZKOC3EAFCRGG4WUB4RUG65U6I/action/citation_signature","submit_replication":"https://pith.science/pith/KZKOC3EAFCRGG4WUB4RUG65U6I/action/replication_record"}},"created_at":"2026-07-05T08:06:32.284073+00:00","updated_at":"2026-07-05T08:06:32.284073+00:00"}