{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:PUPEL7FFCWNSFQ5E5UU7IHT2W3","short_pith_number":"pith:PUPEL7FF","canonical_record":{"source":{"id":"2605.14051","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-13T19:12:24Z","cross_cats_sorted":[],"title_canon_sha256":"9851e0c3639448ad30710ec5fb12e2194b63b7d017dfa434d613e41cd263e002","abstract_canon_sha256":"2f06b8f498ca5d763c5da01469a189b29e97e7e93a8dff3937c8bf20e93635a8"},"schema_version":"1.0"},"canonical_sha256":"7d1e45fca5159b22c3a4ed29f41e7ab6c26d7c97542fef59f0c8610628c62b9b","source":{"kind":"arxiv","id":"2605.14051","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.14051","created_at":"2026-05-17T23:39:12Z"},{"alias_kind":"arxiv_version","alias_value":"2605.14051v1","created_at":"2026-05-17T23:39:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.14051","created_at":"2026-05-17T23:39:12Z"},{"alias_kind":"pith_short_12","alias_value":"PUPEL7FFCWNS","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"PUPEL7FFCWNSFQ5E","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"PUPEL7FF","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:PUPEL7FFCWNSFQ5E5UU7IHT2W3","target":"record","payload":{"canonical_record":{"source":{"id":"2605.14051","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-13T19:12:24Z","cross_cats_sorted":[],"title_canon_sha256":"9851e0c3639448ad30710ec5fb12e2194b63b7d017dfa434d613e41cd263e002","abstract_canon_sha256":"2f06b8f498ca5d763c5da01469a189b29e97e7e93a8dff3937c8bf20e93635a8"},"schema_version":"1.0"},"canonical_sha256":"7d1e45fca5159b22c3a4ed29f41e7ab6c26d7c97542fef59f0c8610628c62b9b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:12.641380Z","signature_b64":"5ZtFDtuT0r9vmHUn95fxV5ZinCQHgDsEpw0QOiZ0BKUGOWOtuzd3ynVovWMo+dq8tccWppk1zXYB8GvWRU0mCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7d1e45fca5159b22c3a4ed29f41e7ab6c26d7c97542fef59f0c8610628c62b9b","last_reissued_at":"2026-05-17T23:39:12.640635Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:12.640635Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.14051","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:39:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hQsDRTdhljBtwxPlmIwmTYS4+fVV6o3EegD3sNU3riIqlO0mKrSrVZtBHC0/su1fAkLOxTVy/AFf+Gg+G69EAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T21:14:04.334914Z"},"content_sha256":"f38d4f8546b09ed0ce467ab623f8bf84638c09785369388c8d550879f234a93c","schema_version":"1.0","event_id":"sha256:f38d4f8546b09ed0ce467ab623f8bf84638c09785369388c8d550879f234a93c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:PUPEL7FFCWNSFQ5E5UU7IHT2W3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SPIN: Structural LLM Planning via Iterative Navigation for Industrial Tasks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"SPIN wraps LLM planners with DAG validation and prefix execution control to produce shorter, more reliable industrial workflows.","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Dhaval Patel, Yusuke Ozaki","submitted_at":"2026-05-13T19:12:24Z","abstract_excerpt":"Industrial LLM agent systems often separate planning from execution, yet LLM planners frequently produce structurally invalid or unnecessarily long workflows, leading to brittle failures and avoidable tool and API cost. We propose \\texttt{SPIN}, a planning wrapper that combines validated Directed Acyclic Graph (DAG) planning with prefix based execution control. \\texttt{SPIN} enforces a strict DAG contract through \\texttt{\\_validate\\_plan\\_text} and repair prompting, producing executable plans before downstream execution, and then evaluates DAG prefixes incrementally to stop when the current pr"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"On AssetOpsBench across 261 scenarios, SPIN reduces executed tasks from 1061 to 623 and improves Accomplished from 0.638 to 0.706, while reducing tool calls from 11.81 to 6.82 per run. On MCP Bench it improves planning, grounding, and dependency scores for GPT OSS1 and Llama 4 Maverick.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That LLM-based validation and repair prompting will consistently produce executable DAG plans without introducing new structural errors or missing invalid cases, and that the LLM can accurately judge when a prefix is sufficient.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"SPIN enforces DAG-valid plans and prefix-based stopping for LLM agents, cutting executed tasks from 1061 to 623 and tool calls from 11.81 to 6.82 per run on AssetOpsBench while raising success from 0.638 to 0.706.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"SPIN wraps LLM planners with DAG validation and prefix execution control to produce shorter, more reliable industrial workflows.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"7c1bf8142b011e293f0fa6926aaa5215da42ce20250ad00aac117cb912fee84a"},"source":{"id":"2605.14051","kind":"arxiv","version":1},"verdict":{"id":"040b1eec-0fb5-45c3-a628-9938dc49870f","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T05:15:39.043056Z","strongest_claim":"On AssetOpsBench across 261 scenarios, SPIN reduces executed tasks from 1061 to 623 and improves Accomplished from 0.638 to 0.706, while reducing tool calls from 11.81 to 6.82 per run. On MCP Bench it improves planning, grounding, and dependency scores for GPT OSS1 and Llama 4 Maverick.","one_line_summary":"SPIN enforces DAG-valid plans and prefix-based stopping for LLM agents, cutting executed tasks from 1061 to 623 and tool calls from 11.81 to 6.82 per run on AssetOpsBench while raising success from 0.638 to 0.706.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That LLM-based validation and repair prompting will consistently produce executable DAG plans without introducing new structural errors or missing invalid cases, and that the LLM can accurately judge when a prefix is sufficient.","pith_extraction_headline":"SPIN wraps LLM planners with DAG validation and prefix execution control to produce shorter, more reliable industrial workflows."},"references":{"count":21,"sample":[{"doi":"","year":2024,"title":"Automating thought of search: A journey towards soundness and completeness, 2024","work_id":"eab266d9-75af-410f-b6df-4db4690da914","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.14778/3750601.3750611","year":2025,"title":"Chang and Longling Geng","work_id":"482ffb18-33ce-4650-aaa1-9f767fe8391f","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"Assetopsbench – codabench competition","work_id":"0b4ee1d6-ea2b-4b36-a985-b6b84ae7c013","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.18653/v1/2023.emnlp-main.674","year":2023,"title":"Grammar-constrained decoding for structured NLP tasks without finetuning","work_id":"1ae61f4c-d60b-4301-a5d5-272b0c17d3d9","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"Jsonschemabench: A rigorous benchmark of structured outputs for language models","work_id":"0a01531a-03bb-463b-8bec-3777fe5a120b","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":21,"snapshot_sha256":"bf74835433753d63aed2bca6f569ade17c1b2bb9d878ade6b13fd2ffcfc80284","internal_anchors":2},"formal_canon":{"evidence_count":2,"snapshot_sha256":"3f6c85bbc44a299c1d91f2bca049cd8721c7feef873736fe7d3f871b490229fd"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"040b1eec-0fb5-45c3-a628-9938dc49870f"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:39:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4TKdDBdo5qqHs6saIVtPhmYpgj4hKKKFJwuu8Fd+NCgu+ReS6DDtueXdsEbDzTxTnkZt3FKqRhlQjcqk6O33BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T21:14:04.335581Z"},"content_sha256":"f98ea55a19d01f18382eb5bb7558a5f7f19c0b382af2c156fdc7158fd4aeafe9","schema_version":"1.0","event_id":"sha256:f98ea55a19d01f18382eb5bb7558a5f7f19c0b382af2c156fdc7158fd4aeafe9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PUPEL7FFCWNSFQ5E5UU7IHT2W3/bundle.json","state_url":"https://pith.science/pith/PUPEL7FFCWNSFQ5E5UU7IHT2W3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PUPEL7FFCWNSFQ5E5UU7IHT2W3/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-30T21:14:04Z","links":{"resolver":"https://pith.science/pith/PUPEL7FFCWNSFQ5E5UU7IHT2W3","bundle":"https://pith.science/pith/PUPEL7FFCWNSFQ5E5UU7IHT2W3/bundle.json","state":"https://pith.science/pith/PUPEL7FFCWNSFQ5E5UU7IHT2W3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PUPEL7FFCWNSFQ5E5UU7IHT2W3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:PUPEL7FFCWNSFQ5E5UU7IHT2W3","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":"2f06b8f498ca5d763c5da01469a189b29e97e7e93a8dff3937c8bf20e93635a8","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-13T19:12:24Z","title_canon_sha256":"9851e0c3639448ad30710ec5fb12e2194b63b7d017dfa434d613e41cd263e002"},"schema_version":"1.0","source":{"id":"2605.14051","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.14051","created_at":"2026-05-17T23:39:12Z"},{"alias_kind":"arxiv_version","alias_value":"2605.14051v1","created_at":"2026-05-17T23:39:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.14051","created_at":"2026-05-17T23:39:12Z"},{"alias_kind":"pith_short_12","alias_value":"PUPEL7FFCWNS","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"PUPEL7FFCWNSFQ5E","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"PUPEL7FF","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:f98ea55a19d01f18382eb5bb7558a5f7f19c0b382af2c156fdc7158fd4aeafe9","target":"graph","created_at":"2026-05-17T23:39:12Z","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":"On AssetOpsBench across 261 scenarios, SPIN reduces executed tasks from 1061 to 623 and improves Accomplished from 0.638 to 0.706, while reducing tool calls from 11.81 to 6.82 per run. On MCP Bench it improves planning, grounding, and dependency scores for GPT OSS1 and Llama 4 Maverick."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That LLM-based validation and repair prompting will consistently produce executable DAG plans without introducing new structural errors or missing invalid cases, and that the LLM can accurately judge when a prefix is sufficient."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"SPIN enforces DAG-valid plans and prefix-based stopping for LLM agents, cutting executed tasks from 1061 to 623 and tool calls from 11.81 to 6.82 per run on AssetOpsBench while raising success from 0.638 to 0.706."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"SPIN wraps LLM planners with DAG validation and prefix execution control to produce shorter, more reliable industrial workflows."}],"snapshot_sha256":"7c1bf8142b011e293f0fa6926aaa5215da42ce20250ad00aac117cb912fee84a"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"3f6c85bbc44a299c1d91f2bca049cd8721c7feef873736fe7d3f871b490229fd"},"paper":{"abstract_excerpt":"Industrial LLM agent systems often separate planning from execution, yet LLM planners frequently produce structurally invalid or unnecessarily long workflows, leading to brittle failures and avoidable tool and API cost. We propose \\texttt{SPIN}, a planning wrapper that combines validated Directed Acyclic Graph (DAG) planning with prefix based execution control. \\texttt{SPIN} enforces a strict DAG contract through \\texttt{\\_validate\\_plan\\_text} and repair prompting, producing executable plans before downstream execution, and then evaluates DAG prefixes incrementally to stop when the current pr","authors_text":"Dhaval Patel, Yusuke Ozaki","cross_cats":[],"headline":"SPIN wraps LLM planners with DAG validation and prefix execution control to produce shorter, more reliable industrial workflows.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-13T19:12:24Z","title":"SPIN: Structural LLM Planning via Iterative Navigation for Industrial Tasks"},"references":{"count":21,"internal_anchors":2,"resolved_work":21,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Automating thought of search: A journey towards soundness and completeness, 2024","work_id":"eab266d9-75af-410f-b6df-4db4690da914","year":2024},{"cited_arxiv_id":"","doi":"10.14778/3750601.3750611","is_internal_anchor":false,"ref_index":2,"title":"Chang and Longling Geng","work_id":"482ffb18-33ce-4650-aaa1-9f767fe8391f","year":2025},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Assetopsbench – codabench competition","work_id":"0b4ee1d6-ea2b-4b36-a985-b6b84ae7c013","year":2025},{"cited_arxiv_id":"","doi":"10.18653/v1/2023.emnlp-main.674","is_internal_anchor":false,"ref_index":4,"title":"Grammar-constrained decoding for structured NLP tasks without finetuning","work_id":"1ae61f4c-d60b-4301-a5d5-272b0c17d3d9","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Jsonschemabench: A rigorous benchmark of structured outputs for language models","work_id":"0a01531a-03bb-463b-8bec-3777fe5a120b","year":2025}],"snapshot_sha256":"bf74835433753d63aed2bca6f569ade17c1b2bb9d878ade6b13fd2ffcfc80284"},"source":{"id":"2605.14051","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-15T05:15:39.043056Z","id":"040b1eec-0fb5-45c3-a628-9938dc49870f","model_set":{"reader":"grok-4.3"},"one_line_summary":"SPIN enforces DAG-valid plans and prefix-based stopping for LLM agents, cutting executed tasks from 1061 to 623 and tool calls from 11.81 to 6.82 per run on AssetOpsBench while raising success from 0.638 to 0.706.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"SPIN wraps LLM planners with DAG validation and prefix execution control to produce shorter, more reliable industrial workflows.","strongest_claim":"On AssetOpsBench across 261 scenarios, SPIN reduces executed tasks from 1061 to 623 and improves Accomplished from 0.638 to 0.706, while reducing tool calls from 11.81 to 6.82 per run. On MCP Bench it improves planning, grounding, and dependency scores for GPT OSS1 and Llama 4 Maverick.","weakest_assumption":"That LLM-based validation and repair prompting will consistently produce executable DAG plans without introducing new structural errors or missing invalid cases, and that the LLM can accurately judge when a prefix is sufficient."}},"verdict_id":"040b1eec-0fb5-45c3-a628-9938dc49870f"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f38d4f8546b09ed0ce467ab623f8bf84638c09785369388c8d550879f234a93c","target":"record","created_at":"2026-05-17T23:39:12Z","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":"2f06b8f498ca5d763c5da01469a189b29e97e7e93a8dff3937c8bf20e93635a8","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-13T19:12:24Z","title_canon_sha256":"9851e0c3639448ad30710ec5fb12e2194b63b7d017dfa434d613e41cd263e002"},"schema_version":"1.0","source":{"id":"2605.14051","kind":"arxiv","version":1}},"canonical_sha256":"7d1e45fca5159b22c3a4ed29f41e7ab6c26d7c97542fef59f0c8610628c62b9b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7d1e45fca5159b22c3a4ed29f41e7ab6c26d7c97542fef59f0c8610628c62b9b","first_computed_at":"2026-05-17T23:39:12.640635Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:39:12.640635Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5ZtFDtuT0r9vmHUn95fxV5ZinCQHgDsEpw0QOiZ0BKUGOWOtuzd3ynVovWMo+dq8tccWppk1zXYB8GvWRU0mCw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:39:12.641380Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.14051","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f38d4f8546b09ed0ce467ab623f8bf84638c09785369388c8d550879f234a93c","sha256:f98ea55a19d01f18382eb5bb7558a5f7f19c0b382af2c156fdc7158fd4aeafe9"],"state_sha256":"60e6cd77dee4ff4f7927fb74a09a75404b448a6866163a85ebeaa7d31f458a8e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VL0AV123/I8AVZBRPXQ1kwqQR2NWB5oA4q4vqRRriuSo0D2dgcRyGjkWLiS7UOprbQ3B+H89S3z+SzYEUAMgDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T21:14:04.338227Z","bundle_sha256":"f7d29fd20af64690b946a528a31756493ae39728b5009e0d5dbdafdf1765e9b1"}}