{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:CPZWJQ7C3V4XJDNWXKPQRYS5XJ","short_pith_number":"pith:CPZWJQ7C","canonical_record":{"source":{"id":"2507.14261","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.DS","submitted_at":"2025-07-18T12:53:58Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d4969e4809f0bd0c3e3b054f031b3a93641ca6fb34abf6bb1fb7635f6b92fd74","abstract_canon_sha256":"44e225e5729feb434a48f221257072517d0a08d8923afcdfdb2816909bb79f20"},"schema_version":"1.0"},"canonical_sha256":"13f364c3e2dd79748db6ba9f08e25dba49aa9273d2f33a1f5a1f1409f5917fae","source":{"kind":"arxiv","id":"2507.14261","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.14261","created_at":"2026-07-05T11:40:02Z"},{"alias_kind":"arxiv_version","alias_value":"2507.14261v1","created_at":"2026-07-05T11:40:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.14261","created_at":"2026-07-05T11:40:02Z"},{"alias_kind":"pith_short_12","alias_value":"CPZWJQ7C3V4X","created_at":"2026-07-05T11:40:02Z"},{"alias_kind":"pith_short_16","alias_value":"CPZWJQ7C3V4XJDNW","created_at":"2026-07-05T11:40:02Z"},{"alias_kind":"pith_short_8","alias_value":"CPZWJQ7C","created_at":"2026-07-05T11:40:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:CPZWJQ7C3V4XJDNWXKPQRYS5XJ","target":"record","payload":{"canonical_record":{"source":{"id":"2507.14261","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.DS","submitted_at":"2025-07-18T12:53:58Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d4969e4809f0bd0c3e3b054f031b3a93641ca6fb34abf6bb1fb7635f6b92fd74","abstract_canon_sha256":"44e225e5729feb434a48f221257072517d0a08d8923afcdfdb2816909bb79f20"},"schema_version":"1.0"},"canonical_sha256":"13f364c3e2dd79748db6ba9f08e25dba49aa9273d2f33a1f5a1f1409f5917fae","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:40:02.944400Z","signature_b64":"yy7WwiuhOgiL2RiB7Yq6MCa+Y8K63CNlbiYeaag9V2l9L64X7r3ArhLiCDKaq3jG70C6eODqnm6dP3/6c+ClBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"13f364c3e2dd79748db6ba9f08e25dba49aa9273d2f33a1f5a1f1409f5917fae","last_reissued_at":"2026-07-05T11:40:02.943920Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:40:02.943920Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.14261","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":"2026-07-05T11:40:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NZxD0q8lPB0lFsnlZNvorKDEpGfbXcMbnVfJ2CEXKMlKATmwbGSJ/m1g5P2TtMH5RSHM+cutgLj8D/8j5p+yCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T02:36:19.183176Z"},"content_sha256":"452109c4889e6ecfe23326495aa63fbeabb4fd60ba82869d8bb48b9b90733cf5","schema_version":"1.0","event_id":"sha256:452109c4889e6ecfe23326495aa63fbeabb4fd60ba82869d8bb48b9b90733cf5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:CPZWJQ7C3V4XJDNWXKPQRYS5XJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FAMST: Fast Approximate Minimum Spanning Tree Construction for Large-Scale and High-Dimensional Data","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.DS","authors_text":"Mahmood K. M. Almansoori, Miklos Telek","submitted_at":"2025-07-18T12:53:58Z","abstract_excerpt":"We present Fast Approximate Minimum Spanning Tree (FAMST), a novel algorithm that addresses the computational challenges of constructing Minimum Spanning Trees (MSTs) for large-scale and high-dimensional datasets. FAMST utilizes a three-phase approach: Approximate Nearest Neighbor (ANN) graph construction, ANN inter-component connection, and iterative edge refinement. For a dataset of $n$ points in a $d$-dimensional space, FAMST achieves $\\mathcal{O}(dn \\log n)$ time complexity and $\\mathcal{O}(dn + kn)$ space complexity when $k$ nearest neighbors are considered, which is a significant improve"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.14261","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/2507.14261/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":"2026-07-05T11:40:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gM5BKulCQ2YqM7P+aVlHrgREbTde7+baCf58AjQ38Q5m+yWEC0HYouXqGWvjbSpragTVP+zzzkOvm6Qs7o+mAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T02:36:19.184073Z"},"content_sha256":"58942098e98ba8805c2629dc42e4d915166414258bd2b1eaed858e2afb7a6df2","schema_version":"1.0","event_id":"sha256:58942098e98ba8805c2629dc42e4d915166414258bd2b1eaed858e2afb7a6df2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CPZWJQ7C3V4XJDNWXKPQRYS5XJ/bundle.json","state_url":"https://pith.science/pith/CPZWJQ7C3V4XJDNWXKPQRYS5XJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CPZWJQ7C3V4XJDNWXKPQRYS5XJ/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":"2026-08-17T02:36:19Z","links":{"resolver":"https://pith.science/pith/CPZWJQ7C3V4XJDNWXKPQRYS5XJ","bundle":"https://pith.science/pith/CPZWJQ7C3V4XJDNWXKPQRYS5XJ/bundle.json","state":"https://pith.science/pith/CPZWJQ7C3V4XJDNWXKPQRYS5XJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CPZWJQ7C3V4XJDNWXKPQRYS5XJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:CPZWJQ7C3V4XJDNWXKPQRYS5XJ","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":"44e225e5729feb434a48f221257072517d0a08d8923afcdfdb2816909bb79f20","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.DS","submitted_at":"2025-07-18T12:53:58Z","title_canon_sha256":"d4969e4809f0bd0c3e3b054f031b3a93641ca6fb34abf6bb1fb7635f6b92fd74"},"schema_version":"1.0","source":{"id":"2507.14261","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.14261","created_at":"2026-07-05T11:40:02Z"},{"alias_kind":"arxiv_version","alias_value":"2507.14261v1","created_at":"2026-07-05T11:40:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.14261","created_at":"2026-07-05T11:40:02Z"},{"alias_kind":"pith_short_12","alias_value":"CPZWJQ7C3V4X","created_at":"2026-07-05T11:40:02Z"},{"alias_kind":"pith_short_16","alias_value":"CPZWJQ7C3V4XJDNW","created_at":"2026-07-05T11:40:02Z"},{"alias_kind":"pith_short_8","alias_value":"CPZWJQ7C","created_at":"2026-07-05T11:40:02Z"}],"graph_snapshots":[{"event_id":"sha256:58942098e98ba8805c2629dc42e4d915166414258bd2b1eaed858e2afb7a6df2","target":"graph","created_at":"2026-07-05T11:40:02Z","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/2507.14261/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present Fast Approximate Minimum Spanning Tree (FAMST), a novel algorithm that addresses the computational challenges of constructing Minimum Spanning Trees (MSTs) for large-scale and high-dimensional datasets. FAMST utilizes a three-phase approach: Approximate Nearest Neighbor (ANN) graph construction, ANN inter-component connection, and iterative edge refinement. For a dataset of $n$ points in a $d$-dimensional space, FAMST achieves $\\mathcal{O}(dn \\log n)$ time complexity and $\\mathcal{O}(dn + kn)$ space complexity when $k$ nearest neighbors are considered, which is a significant improve","authors_text":"Mahmood K. M. Almansoori, Miklos Telek","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.DS","submitted_at":"2025-07-18T12:53:58Z","title":"FAMST: Fast Approximate Minimum Spanning Tree Construction for Large-Scale and High-Dimensional Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.14261","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":"sha256:452109c4889e6ecfe23326495aa63fbeabb4fd60ba82869d8bb48b9b90733cf5","target":"record","created_at":"2026-07-05T11:40:02Z","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":"44e225e5729feb434a48f221257072517d0a08d8923afcdfdb2816909bb79f20","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.DS","submitted_at":"2025-07-18T12:53:58Z","title_canon_sha256":"d4969e4809f0bd0c3e3b054f031b3a93641ca6fb34abf6bb1fb7635f6b92fd74"},"schema_version":"1.0","source":{"id":"2507.14261","kind":"arxiv","version":1}},"canonical_sha256":"13f364c3e2dd79748db6ba9f08e25dba49aa9273d2f33a1f5a1f1409f5917fae","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"13f364c3e2dd79748db6ba9f08e25dba49aa9273d2f33a1f5a1f1409f5917fae","first_computed_at":"2026-07-05T11:40:02.943920Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:40:02.943920Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yy7WwiuhOgiL2RiB7Yq6MCa+Y8K63CNlbiYeaag9V2l9L64X7r3ArhLiCDKaq3jG70C6eODqnm6dP3/6c+ClBw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:40:02.944400Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.14261","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:452109c4889e6ecfe23326495aa63fbeabb4fd60ba82869d8bb48b9b90733cf5","sha256:58942098e98ba8805c2629dc42e4d915166414258bd2b1eaed858e2afb7a6df2"],"state_sha256":"6ea9662aa1bd04730ef2072a4d6700a003c7315bfd3f620f50b06afc7b7b138e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gTtGjIzD5RQE9hLwIsfZbBOD55QPTiJqD94x1wrgGG2oNOC8D68mw9n+ITxhumqND/lCVQzsVkokgpGXYl8hCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T02:36:19.248003Z","bundle_sha256":"1392035ddaa32c1769be6b663a365c0bde98bbde13dab9dc3da6b8d7af53eaf8"}}