{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:WPR5JH6FEY3IOVSZ6YBIBUMUJQ","short_pith_number":"pith:WPR5JH6F","canonical_record":{"source":{"id":"2405.17794","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-05-28T03:45:32Z","cross_cats_sorted":[],"title_canon_sha256":"f4f7723ae94f6adc05bac08f3022afeda56adafe327958b0e5b700f48112e17a","abstract_canon_sha256":"222727b013cc789a35e15bd1f9b854932aa7a34a91bc0a3f6e67d36bf4595b6a"},"schema_version":"1.0"},"canonical_sha256":"b3e3d49fc52636875659f60280d1944c0c97c263260e061b8d8d1493a0887b70","source":{"kind":"arxiv","id":"2405.17794","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.17794","created_at":"2026-07-05T10:07:41Z"},{"alias_kind":"arxiv_version","alias_value":"2405.17794v3","created_at":"2026-07-05T10:07:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.17794","created_at":"2026-07-05T10:07:41Z"},{"alias_kind":"pith_short_12","alias_value":"WPR5JH6FEY3I","created_at":"2026-07-05T10:07:41Z"},{"alias_kind":"pith_short_16","alias_value":"WPR5JH6FEY3IOVSZ","created_at":"2026-07-05T10:07:41Z"},{"alias_kind":"pith_short_8","alias_value":"WPR5JH6F","created_at":"2026-07-05T10:07:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:WPR5JH6FEY3IOVSZ6YBIBUMUJQ","target":"record","payload":{"canonical_record":{"source":{"id":"2405.17794","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-05-28T03:45:32Z","cross_cats_sorted":[],"title_canon_sha256":"f4f7723ae94f6adc05bac08f3022afeda56adafe327958b0e5b700f48112e17a","abstract_canon_sha256":"222727b013cc789a35e15bd1f9b854932aa7a34a91bc0a3f6e67d36bf4595b6a"},"schema_version":"1.0"},"canonical_sha256":"b3e3d49fc52636875659f60280d1944c0c97c263260e061b8d8d1493a0887b70","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:07:41.725514Z","signature_b64":"N4cwa1WMfxhuj3DlJvtOvGMKey/up1qJzV8/r+XqMu29xBEzY4HY0CaCI9NIQ7Mba+N55OzTAXRzxr1A9imMAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b3e3d49fc52636875659f60280d1944c0c97c263260e061b8d8d1493a0887b70","last_reissued_at":"2026-07-05T10:07:41.725016Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:07:41.725016Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.17794","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":"2026-07-05T10:07:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aSx9/e4JzGo8cLveZlEWxaWmEVD+ySxwL+1TzTKoDwJI1X3KzGHZofg+MdhxZDaaOUrNMky40sII59YvQrPqCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T20:50:23.841310Z"},"content_sha256":"53962cad4a65bf242116795bbf65e98873f6f19510c32157a6e01f6dd327f40d","schema_version":"1.0","event_id":"sha256:53962cad4a65bf242116795bbf65e98873f6f19510c32157a6e01f6dd327f40d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:WPR5JH6FEY3IOVSZ6YBIBUMUJQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LNS2+RL: Combining Multi-Agent Reinforcement Learning with Large Neighborhood Search in Multi-Agent Path Finding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Guillaume Sartoretti, Jiaoyang Li, Tanishq Duhan, Yutong Wang","submitted_at":"2024-05-28T03:45:32Z","abstract_excerpt":"Multi-Agent Path Finding (MAPF) is a critical component of logistics and warehouse management, which focuses on planning collision-free paths for a team of robots in a known environment. Recent work introduced a novel MAPF approach, LNS2, which proposed to repair a quickly obtained set of infeasible paths via iterative replanning, by relying on a fast, yet lower-quality, prioritized planning (PP) algorithm. At the same time, there has been a recent push for Multi-Agent Reinforcement Learning (MARL) based MAPF algorithms, which exhibit improved cooperation over such PP algorithms, although inev"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.17794","kind":"arxiv","version":3},"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/2405.17794/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:07:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d3v5c3drkK9RL9QxPEek2N/okpEGfpC6t/JTGivKtNx9Mo8CnL79CXztokN8qBBa+ax15YjfsvPSv3atioAlBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T20:50:23.842082Z"},"content_sha256":"bc36616ef0606275178ac0cf300b991e28be9406588db6aa79c455d7da23d617","schema_version":"1.0","event_id":"sha256:bc36616ef0606275178ac0cf300b991e28be9406588db6aa79c455d7da23d617"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WPR5JH6FEY3IOVSZ6YBIBUMUJQ/bundle.json","state_url":"https://pith.science/pith/WPR5JH6FEY3IOVSZ6YBIBUMUJQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WPR5JH6FEY3IOVSZ6YBIBUMUJQ/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":"2026-08-11T20:50:23Z","links":{"resolver":"https://pith.science/pith/WPR5JH6FEY3IOVSZ6YBIBUMUJQ","bundle":"https://pith.science/pith/WPR5JH6FEY3IOVSZ6YBIBUMUJQ/bundle.json","state":"https://pith.science/pith/WPR5JH6FEY3IOVSZ6YBIBUMUJQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WPR5JH6FEY3IOVSZ6YBIBUMUJQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:WPR5JH6FEY3IOVSZ6YBIBUMUJQ","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":"222727b013cc789a35e15bd1f9b854932aa7a34a91bc0a3f6e67d36bf4595b6a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-05-28T03:45:32Z","title_canon_sha256":"f4f7723ae94f6adc05bac08f3022afeda56adafe327958b0e5b700f48112e17a"},"schema_version":"1.0","source":{"id":"2405.17794","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.17794","created_at":"2026-07-05T10:07:41Z"},{"alias_kind":"arxiv_version","alias_value":"2405.17794v3","created_at":"2026-07-05T10:07:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.17794","created_at":"2026-07-05T10:07:41Z"},{"alias_kind":"pith_short_12","alias_value":"WPR5JH6FEY3I","created_at":"2026-07-05T10:07:41Z"},{"alias_kind":"pith_short_16","alias_value":"WPR5JH6FEY3IOVSZ","created_at":"2026-07-05T10:07:41Z"},{"alias_kind":"pith_short_8","alias_value":"WPR5JH6F","created_at":"2026-07-05T10:07:41Z"}],"graph_snapshots":[{"event_id":"sha256:bc36616ef0606275178ac0cf300b991e28be9406588db6aa79c455d7da23d617","target":"graph","created_at":"2026-07-05T10:07:41Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2405.17794/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multi-Agent Path Finding (MAPF) is a critical component of logistics and warehouse management, which focuses on planning collision-free paths for a team of robots in a known environment. Recent work introduced a novel MAPF approach, LNS2, which proposed to repair a quickly obtained set of infeasible paths via iterative replanning, by relying on a fast, yet lower-quality, prioritized planning (PP) algorithm. At the same time, there has been a recent push for Multi-Agent Reinforcement Learning (MARL) based MAPF algorithms, which exhibit improved cooperation over such PP algorithms, although inev","authors_text":"Guillaume Sartoretti, Jiaoyang Li, Tanishq Duhan, Yutong Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-05-28T03:45:32Z","title":"LNS2+RL: Combining Multi-Agent Reinforcement Learning with Large Neighborhood Search in Multi-Agent Path Finding"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.17794","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:53962cad4a65bf242116795bbf65e98873f6f19510c32157a6e01f6dd327f40d","target":"record","created_at":"2026-07-05T10:07:41Z","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":"222727b013cc789a35e15bd1f9b854932aa7a34a91bc0a3f6e67d36bf4595b6a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-05-28T03:45:32Z","title_canon_sha256":"f4f7723ae94f6adc05bac08f3022afeda56adafe327958b0e5b700f48112e17a"},"schema_version":"1.0","source":{"id":"2405.17794","kind":"arxiv","version":3}},"canonical_sha256":"b3e3d49fc52636875659f60280d1944c0c97c263260e061b8d8d1493a0887b70","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b3e3d49fc52636875659f60280d1944c0c97c263260e061b8d8d1493a0887b70","first_computed_at":"2026-07-05T10:07:41.725016Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:07:41.725016Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"N4cwa1WMfxhuj3DlJvtOvGMKey/up1qJzV8/r+XqMu29xBEzY4HY0CaCI9NIQ7Mba+N55OzTAXRzxr1A9imMAA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:07:41.725514Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.17794","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:53962cad4a65bf242116795bbf65e98873f6f19510c32157a6e01f6dd327f40d","sha256:bc36616ef0606275178ac0cf300b991e28be9406588db6aa79c455d7da23d617"],"state_sha256":"556c0c9d4db581fdd93b80f1eb17963a303469aea0d0242b620188ba8326a275"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Y112+DO8EkIFeoxFtaDl5PSqPjY9Yibl4RYaDP9tyhWIhdMAqTn34IgW6wpR1hO4h7K+3x1Ik+eV7ZIIaCtVCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T20:50:23.854573Z","bundle_sha256":"69cb0f4c33563ad71907e65f75a2d8e0b501653df8bd8c82e5383c375b774ee5"}}