{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:J6HXY6Q3CSEXYBIDENRH4NFREE","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":"988bb82ff16f1b8d2bb5e44fcda164d80884c565d17e0b812fbd4601aa674564","cross_cats_sorted":["cs.LG","cs.MA"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-06-30T12:34:31Z","title_canon_sha256":"8ed9b4fcd2b65ecaf523925cf88fca8fcac0a5ccd81444d631631de1c02c16b9"},"schema_version":"1.0","source":{"id":"2506.23793","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.23793","created_at":"2026-07-05T11:29:28Z"},{"alias_kind":"arxiv_version","alias_value":"2506.23793v1","created_at":"2026-07-05T11:29:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.23793","created_at":"2026-07-05T11:29:28Z"},{"alias_kind":"pith_short_12","alias_value":"J6HXY6Q3CSEX","created_at":"2026-07-05T11:29:28Z"},{"alias_kind":"pith_short_16","alias_value":"J6HXY6Q3CSEXYBID","created_at":"2026-07-05T11:29:28Z"},{"alias_kind":"pith_short_8","alias_value":"J6HXY6Q3","created_at":"2026-07-05T11:29:28Z"}],"graph_snapshots":[{"event_id":"sha256:95bc27271a8fe1a48f12123b4d77c5832ba0eef751c19a5f50313ab14c65679e","target":"graph","created_at":"2026-07-05T11:29:28Z","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/2506.23793/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multi-agent pathfinding (MAPF) is a common abstraction of multi-robot trajectory planning problems, where multiple homogeneous robots simultaneously move in the shared environment. While solving MAPF optimally has been proven to be NP-hard, scalable, and efficient, solvers are vital for real-world applications like logistics, search-and-rescue, etc. To this end, decentralized suboptimal MAPF solvers that leverage machine learning have come on stage. Building on the success of the recently introduced MAPF-GPT, a pure imitation learning solver, we introduce MAPF-GPT-DDG. This novel approach effe","authors_text":"Aleksandr Panov, Alexey Skrynnik, Anton Andreychuk, Konstantin Yakovlev","cross_cats":["cs.LG","cs.MA"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-06-30T12:34:31Z","title":"Advancing Learnable Multi-Agent Pathfinding Solvers with Active Fine-Tuning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.23793","kind":"arxiv","version":1},"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":"sha256:8703d02393c4011524f2e3232a465b28e58e1d6d50480f57384f93a318e508f3","target":"record","created_at":"2026-07-05T11:29:28Z","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":"988bb82ff16f1b8d2bb5e44fcda164d80884c565d17e0b812fbd4601aa674564","cross_cats_sorted":["cs.LG","cs.MA"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-06-30T12:34:31Z","title_canon_sha256":"8ed9b4fcd2b65ecaf523925cf88fca8fcac0a5ccd81444d631631de1c02c16b9"},"schema_version":"1.0","source":{"id":"2506.23793","kind":"arxiv","version":1}},"canonical_sha256":"4f8f7c7a1b14897c050323627e34b12131cbc8113aa7e9fe41046aa19cca720e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4f8f7c7a1b14897c050323627e34b12131cbc8113aa7e9fe41046aa19cca720e","first_computed_at":"2026-07-05T11:29:28.052815Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:29:28.052815Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"oLuajKJdTtrmfzotDulvtSj61xOv/NOsMZWPBNy0tv/dWOtaQcQflWj7jb+tTaupO8YLEikYx0rz/OydxvvZAg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:29:28.053309Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.23793","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8703d02393c4011524f2e3232a465b28e58e1d6d50480f57384f93a318e508f3","sha256:95bc27271a8fe1a48f12123b4d77c5832ba0eef751c19a5f50313ab14c65679e"],"state_sha256":"9ef3ce67a336fe04419f438e6fa045b432c1e5b3ceb09e063d7901a47b0dffcf"}