{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:LCD4RHRPR3PSGI5XERFKK56MRA","short_pith_number":"pith:LCD4RHRP","canonical_record":{"source":{"id":"2503.04798","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.RO","submitted_at":"2025-03-03T05:26:59Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"8a1d05fe05d26e02406ae31ba0f999addd4de44099f530306b63411cadf5ad66","abstract_canon_sha256":"6a7700a0c7358c40aeb120e757e1aee6d2d8e1ba2074d2cd21af0f2ffef59cb4"},"schema_version":"1.0"},"canonical_sha256":"5887c89e2f8edf2323b7244aa577cc88333d5936352dafabf9858bca938f5f4f","source":{"kind":"arxiv","id":"2503.04798","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.04798","created_at":"2026-06-23T02:13:14Z"},{"alias_kind":"arxiv_version","alias_value":"2503.04798v3","created_at":"2026-06-23T02:13:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.04798","created_at":"2026-06-23T02:13:14Z"},{"alias_kind":"pith_short_12","alias_value":"LCD4RHRPR3PS","created_at":"2026-06-23T02:13:14Z"},{"alias_kind":"pith_short_16","alias_value":"LCD4RHRPR3PSGI5X","created_at":"2026-06-23T02:13:14Z"},{"alias_kind":"pith_short_8","alias_value":"LCD4RHRP","created_at":"2026-06-23T02:13:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:LCD4RHRPR3PSGI5XERFKK56MRA","target":"record","payload":{"canonical_record":{"source":{"id":"2503.04798","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.RO","submitted_at":"2025-03-03T05:26:59Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"8a1d05fe05d26e02406ae31ba0f999addd4de44099f530306b63411cadf5ad66","abstract_canon_sha256":"6a7700a0c7358c40aeb120e757e1aee6d2d8e1ba2074d2cd21af0f2ffef59cb4"},"schema_version":"1.0"},"canonical_sha256":"5887c89e2f8edf2323b7244aa577cc88333d5936352dafabf9858bca938f5f4f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T02:13:14.430639Z","signature_b64":"n9Nk766ao5W2KroBAm3jNAH2RSDCd8R59ZJirw9/mKAHULzVHbFnSM8UyD9xdr8IuHp2aIZI2YGn3eHxExKeBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5887c89e2f8edf2323b7244aa577cc88333d5936352dafabf9858bca938f5f4f","last_reissued_at":"2026-06-23T02:13:14.430131Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T02:13:14.430131Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2503.04798","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-23T02:13:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BuDQBCPGpyw7cDXDjfZIYxP672oGWP2vhwThNci4WonfcDwk+lx4h6LWrdYVZovuMr1Fpj1rZBP3c5GOhs+MBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-23T10:48:45.137743Z"},"content_sha256":"fd77c21612e1a6df447fcc63b025220c60cd175136ddc86d2dab1c4da5e98d3e","schema_version":"1.0","event_id":"sha256:fd77c21612e1a6df447fcc63b025220c60cd175136ddc86d2dab1c4da5e98d3e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:LCD4RHRPR3PSGI5XERFKK56MRA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Advancing MAPF Toward the Real World: A Scalable Multi-Agent Realistic Testbed (SMART)","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"SMART provides a physics-engine simulator and Action Dependency Graph monitor to test MAPF planners realistically on thousands of robots.","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Daniel Harabor, Jiaoyang Li, Jingtian Yan, Kevin Zheng, Stephen F. Smith, William Kang, Yue Zhang, Yulun Zhang, Zhe Chen, Zhifei Li","submitted_at":"2025-03-03T05:26:59Z","abstract_excerpt":"We present Scalable Multi-Agent Realistic Testbed (SMART), a realistic and efficient software tool for evaluating Multi-Agent Path Finding (MAPF) algorithms. MAPF focuses on planning collision-free paths for a group of robots. While state-of-the-art MAPF planners can plan paths for hundreds of robots in seconds, they often rely on simplified robot models, making their real-world performance unclear. Researchers typically lack access to hundreds of physical robots in laboratory settings to evaluate the algorithms. Meanwhile, industrial professionals who lack expertise in MAPF require an easy-to"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"SMART fills this gap with several advantages: (1) SMART uses physics-engine-based simulators to create realistic simulation environments, accounting for complex real-world factors such as robot kinodynamics and execution uncertainties, (2) SMART uses an execution monitor framework based on the Action Dependency Graph, facilitating seamless integration with various MAPF planners and robot models, and (3) SMART scales to thousands of robots.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the physics-engine simulation and Action Dependency Graph framework will produce execution behavior sufficiently close to real hardware that results transfer meaningfully, without major unmodeled discrepancies in kinodynamics or uncertainties.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"SMART is a physics-engine-based, scalable testbed for realistic evaluation of multi-agent path finding algorithms with an Action Dependency Graph integration framework.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"SMART provides a physics-engine simulator and Action Dependency Graph monitor to test MAPF planners realistically on thousands of robots.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"1f7044f73738d4c404330dca7636ecf1169e74faea2599192412bf3a8d671ccf"},"source":{"id":"2503.04798","kind":"arxiv","version":3},"verdict":{"id":"bdffb983-be84-4f4e-ae5d-8a001529c982","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-23T01:58:27.367051Z","strongest_claim":"SMART fills this gap with several advantages: (1) SMART uses physics-engine-based simulators to create realistic simulation environments, accounting for complex real-world factors such as robot kinodynamics and execution uncertainties, (2) SMART uses an execution monitor framework based on the Action Dependency Graph, facilitating seamless integration with various MAPF planners and robot models, and (3) SMART scales to thousands of robots.","one_line_summary":"SMART is a physics-engine-based, scalable testbed for realistic evaluation of multi-agent path finding algorithms with an Action Dependency Graph integration framework.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the physics-engine simulation and Action Dependency Graph framework will produce execution behavior sufficiently close to real hardware that results transfer meaningfully, without major unmodeled discrepancies in kinodynamics or uncertainties.","pith_extraction_headline":"SMART provides a physics-engine simulator and Action Dependency Graph monitor to test MAPF planners realistically on thousands of robots."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2503.04798/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":36,"sample":[{"doi":"","year":2019,"title":"Multi-agent pathfinding: Definitions, variants, and benchmarks","work_id":"4de6c3a8-4d7e-4aa4-82c8-3ddb2b6de166","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2017,"title":"Feasibility study: Moving non-homogeneous teams in congested video game environ- ments","work_id":"fb42e629-7fec-4295-afbd-2adda374a4aa","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2019,"title":"Persistent and robust execution of MAPF schedules in warehouses","work_id":"2ce4ccf9-6de5-4ac8-8fd1-ccb9a50864b9","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Multi-agent path finding in unmanned aircraft system traffic management with scheduling and speed variation","work_id":"6087a601-cec7-4dd5-8abd-8871f8a64d18","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Graph-based multi-robot path finding and planning","work_id":"eca69a25-4e2e-4f01-8a16-f04473987cf7","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":36,"snapshot_sha256":"9fa6fe3338158622dbc0d7dae1b91553352aaf185a13fafd4a594f7f5e49c506","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"82f1518ab2057c0a3ade7efc68789b151d95d826f4fb9864ae0728d456a7ee49"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"bdffb983-be84-4f4e-ae5d-8a001529c982"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-23T02:13:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"asNmi90hh8EiucHXlTPD90lGvdRlvhUBBkDB8KXxoTDxNZgnBcJ0E8HBasESn3khzvC5z7M3v31DqxoxUls1AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-23T10:48:45.138279Z"},"content_sha256":"3ea88d065279dc4c1455393b8bfd395499a1495b6324360ad87f4b6ef75f5400","schema_version":"1.0","event_id":"sha256:3ea88d065279dc4c1455393b8bfd395499a1495b6324360ad87f4b6ef75f5400"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LCD4RHRPR3PSGI5XERFKK56MRA/bundle.json","state_url":"https://pith.science/pith/LCD4RHRPR3PSGI5XERFKK56MRA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LCD4RHRPR3PSGI5XERFKK56MRA/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":"2026-07-23T10:48:45Z","links":{"resolver":"https://pith.science/pith/LCD4RHRPR3PSGI5XERFKK56MRA","bundle":"https://pith.science/pith/LCD4RHRPR3PSGI5XERFKK56MRA/bundle.json","state":"https://pith.science/pith/LCD4RHRPR3PSGI5XERFKK56MRA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LCD4RHRPR3PSGI5XERFKK56MRA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:LCD4RHRPR3PSGI5XERFKK56MRA","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":"6a7700a0c7358c40aeb120e757e1aee6d2d8e1ba2074d2cd21af0f2ffef59cb4","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.RO","submitted_at":"2025-03-03T05:26:59Z","title_canon_sha256":"8a1d05fe05d26e02406ae31ba0f999addd4de44099f530306b63411cadf5ad66"},"schema_version":"1.0","source":{"id":"2503.04798","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.04798","created_at":"2026-06-23T02:13:14Z"},{"alias_kind":"arxiv_version","alias_value":"2503.04798v3","created_at":"2026-06-23T02:13:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.04798","created_at":"2026-06-23T02:13:14Z"},{"alias_kind":"pith_short_12","alias_value":"LCD4RHRPR3PS","created_at":"2026-06-23T02:13:14Z"},{"alias_kind":"pith_short_16","alias_value":"LCD4RHRPR3PSGI5X","created_at":"2026-06-23T02:13:14Z"},{"alias_kind":"pith_short_8","alias_value":"LCD4RHRP","created_at":"2026-06-23T02:13:14Z"}],"graph_snapshots":[{"event_id":"sha256:3ea88d065279dc4c1455393b8bfd395499a1495b6324360ad87f4b6ef75f5400","target":"graph","created_at":"2026-06-23T02:13:14Z","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":"SMART fills this gap with several advantages: (1) SMART uses physics-engine-based simulators to create realistic simulation environments, accounting for complex real-world factors such as robot kinodynamics and execution uncertainties, (2) SMART uses an execution monitor framework based on the Action Dependency Graph, facilitating seamless integration with various MAPF planners and robot models, and (3) SMART scales to thousands of robots."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the physics-engine simulation and Action Dependency Graph framework will produce execution behavior sufficiently close to real hardware that results transfer meaningfully, without major unmodeled discrepancies in kinodynamics or uncertainties."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"SMART is a physics-engine-based, scalable testbed for realistic evaluation of multi-agent path finding algorithms with an Action Dependency Graph integration framework."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"SMART provides a physics-engine simulator and Action Dependency Graph monitor to test MAPF planners realistically on thousands of robots."}],"snapshot_sha256":"1f7044f73738d4c404330dca7636ecf1169e74faea2599192412bf3a8d671ccf"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"82f1518ab2057c0a3ade7efc68789b151d95d826f4fb9864ae0728d456a7ee49"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2503.04798/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present Scalable Multi-Agent Realistic Testbed (SMART), a realistic and efficient software tool for evaluating Multi-Agent Path Finding (MAPF) algorithms. MAPF focuses on planning collision-free paths for a group of robots. While state-of-the-art MAPF planners can plan paths for hundreds of robots in seconds, they often rely on simplified robot models, making their real-world performance unclear. Researchers typically lack access to hundreds of physical robots in laboratory settings to evaluate the algorithms. Meanwhile, industrial professionals who lack expertise in MAPF require an easy-to","authors_text":"Daniel Harabor, Jiaoyang Li, Jingtian Yan, Kevin Zheng, Stephen F. Smith, William Kang, Yue Zhang, Yulun Zhang, Zhe Chen, Zhifei Li","cross_cats":["cs.AI"],"headline":"SMART provides a physics-engine simulator and Action Dependency Graph monitor to test MAPF planners realistically on thousands of robots.","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.RO","submitted_at":"2025-03-03T05:26:59Z","title":"Advancing MAPF Toward the Real World: A Scalable Multi-Agent Realistic Testbed (SMART)"},"references":{"count":36,"internal_anchors":0,"resolved_work":36,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Multi-agent pathfinding: Definitions, variants, and benchmarks","work_id":"4de6c3a8-4d7e-4aa4-82c8-3ddb2b6de166","year":2019},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Feasibility study: Moving non-homogeneous teams in congested video game environ- ments","work_id":"fb42e629-7fec-4295-afbd-2adda374a4aa","year":2017},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Persistent and robust execution of MAPF schedules in warehouses","work_id":"2ce4ccf9-6de5-4ac8-8fd1-ccb9a50864b9","year":2019},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Multi-agent path finding in unmanned aircraft system traffic management with scheduling and speed variation","work_id":"6087a601-cec7-4dd5-8abd-8871f8a64d18","year":2022},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Graph-based multi-robot path finding and planning","work_id":"eca69a25-4e2e-4f01-8a16-f04473987cf7","year":2022}],"snapshot_sha256":"9fa6fe3338158622dbc0d7dae1b91553352aaf185a13fafd4a594f7f5e49c506"},"source":{"id":"2503.04798","kind":"arxiv","version":3},"verdict":{"created_at":"2026-05-23T01:58:27.367051Z","id":"bdffb983-be84-4f4e-ae5d-8a001529c982","model_set":{"reader":"grok-4.3"},"one_line_summary":"SMART is a physics-engine-based, scalable testbed for realistic evaluation of multi-agent path finding algorithms with an Action Dependency Graph integration framework.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"SMART provides a physics-engine simulator and Action Dependency Graph monitor to test MAPF planners realistically on thousands of robots.","strongest_claim":"SMART fills this gap with several advantages: (1) SMART uses physics-engine-based simulators to create realistic simulation environments, accounting for complex real-world factors such as robot kinodynamics and execution uncertainties, (2) SMART uses an execution monitor framework based on the Action Dependency Graph, facilitating seamless integration with various MAPF planners and robot models, and (3) SMART scales to thousands of robots.","weakest_assumption":"That the physics-engine simulation and Action Dependency Graph framework will produce execution behavior sufficiently close to real hardware that results transfer meaningfully, without major unmodeled discrepancies in kinodynamics or uncertainties."}},"verdict_id":"bdffb983-be84-4f4e-ae5d-8a001529c982"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:fd77c21612e1a6df447fcc63b025220c60cd175136ddc86d2dab1c4da5e98d3e","target":"record","created_at":"2026-06-23T02:13:14Z","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":"6a7700a0c7358c40aeb120e757e1aee6d2d8e1ba2074d2cd21af0f2ffef59cb4","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.RO","submitted_at":"2025-03-03T05:26:59Z","title_canon_sha256":"8a1d05fe05d26e02406ae31ba0f999addd4de44099f530306b63411cadf5ad66"},"schema_version":"1.0","source":{"id":"2503.04798","kind":"arxiv","version":3}},"canonical_sha256":"5887c89e2f8edf2323b7244aa577cc88333d5936352dafabf9858bca938f5f4f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5887c89e2f8edf2323b7244aa577cc88333d5936352dafabf9858bca938f5f4f","first_computed_at":"2026-06-23T02:13:14.430131Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-23T02:13:14.430131Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"n9Nk766ao5W2KroBAm3jNAH2RSDCd8R59ZJirw9/mKAHULzVHbFnSM8UyD9xdr8IuHp2aIZI2YGn3eHxExKeBw==","signature_status":"signed_v1","signed_at":"2026-06-23T02:13:14.430639Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.04798","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fd77c21612e1a6df447fcc63b025220c60cd175136ddc86d2dab1c4da5e98d3e","sha256:3ea88d065279dc4c1455393b8bfd395499a1495b6324360ad87f4b6ef75f5400"],"state_sha256":"da41db227fa0f8472b24c75276221cd9943789d17c15102a70c5d37f6df082b1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SyU8zWu+pbOnRoD39IuOGr+nqi53nDGwpb7DizbmzQNruzKWHwdwfHOsZHeln2a34lMMhdhqEs7tyffnQ3IcDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-23T10:48:45.141120Z","bundle_sha256":"59c0203110639dc1ed6851409b7c5bf50874b79d48e88cb68b3b7acc5af6702f"}}