{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:77MXAPOULIIAJLLOWV77JHF3XY","short_pith_number":"pith:77MXAPOU","canonical_record":{"source":{"id":"2406.05815","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-09T15:03:36Z","cross_cats_sorted":[],"title_canon_sha256":"269847ddb64fe7e5be90a6904fe6f3d6ccb3db12a7d514feec864fcccf264f32","abstract_canon_sha256":"6270c36196b32774c63b5590ee0bee587238dc3f87cd18a5e1fa2c40a768e2a1"},"schema_version":"1.0"},"canonical_sha256":"ffd9703dd45a1004ad6eb57ff49cbbbe12cc556a10fa2cef88838a239c8f2fd1","source":{"kind":"arxiv","id":"2406.05815","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.05815","created_at":"2026-07-05T09:15:33Z"},{"alias_kind":"arxiv_version","alias_value":"2406.05815v2","created_at":"2026-07-05T09:15:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.05815","created_at":"2026-07-05T09:15:33Z"},{"alias_kind":"pith_short_12","alias_value":"77MXAPOULIIA","created_at":"2026-07-05T09:15:33Z"},{"alias_kind":"pith_short_16","alias_value":"77MXAPOULIIAJLLO","created_at":"2026-07-05T09:15:33Z"},{"alias_kind":"pith_short_8","alias_value":"77MXAPOU","created_at":"2026-07-05T09:15:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:77MXAPOULIIAJLLOWV77JHF3XY","target":"record","payload":{"canonical_record":{"source":{"id":"2406.05815","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-09T15:03:36Z","cross_cats_sorted":[],"title_canon_sha256":"269847ddb64fe7e5be90a6904fe6f3d6ccb3db12a7d514feec864fcccf264f32","abstract_canon_sha256":"6270c36196b32774c63b5590ee0bee587238dc3f87cd18a5e1fa2c40a768e2a1"},"schema_version":"1.0"},"canonical_sha256":"ffd9703dd45a1004ad6eb57ff49cbbbe12cc556a10fa2cef88838a239c8f2fd1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:15:33.376293Z","signature_b64":"PHfm9KKGPPUxQIRaVWGz7P9+pPclCvVNk1oTB+3HIrT9Vc8kTxmo1bhKNAk9JRX5JrHNXGzC6RD4PeqWy0Y9AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ffd9703dd45a1004ad6eb57ff49cbbbe12cc556a10fa2cef88838a239c8f2fd1","last_reissued_at":"2026-07-05T09:15:33.375752Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:15:33.375752Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2406.05815","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:15:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"27HSb5vk8CgZXig7vzFo/dIz66AYFVH9C6SsqUyrxHzKOgYMrH8a38w6SVGnwKoFvq8FuGo+Vrio2wfzKSr5Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T15:00:54.307956Z"},"content_sha256":"9d53c8f2cd393a45aeb4474dba5edc3a9e62d48752adf2caecf1df5c1f80d017","schema_version":"1.0","event_id":"sha256:9d53c8f2cd393a45aeb4474dba5edc3a9e62d48752adf2caecf1df5c1f80d017"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:77MXAPOULIIAJLLOWV77JHF3XY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"What Can We Learn from State Space Models for Machine Learning on Graphs?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Pan Li, Siqi Miao, Yinan Huang","submitted_at":"2024-06-09T15:03:36Z","abstract_excerpt":"Machine learning on graphs has recently found extensive applications across domains. However, the commonly used Message Passing Neural Networks (MPNNs) suffer from limited expressive power and struggle to capture long-range dependencies. Graph transformers offer a strong alternative due to their global attention mechanism, but they come with great computational overheads, especially for large graphs. In recent years, State Space Models (SSMs) have emerged as a compelling approach to replace full attention in transformers to model sequential data. It blends the strengths of RNNs and CNNs, offer"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.05815","kind":"arxiv","version":2},"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/2406.05815/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":"2026-07-05T09:15:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wB4rl1liJ2LP7DUEg2u5dcx+yxLFfZL5aNqbmZxOimISuYcFiADaQ0tSCivU02MEJOagBaL1/nA7WCjSKwlqDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T15:00:54.308543Z"},"content_sha256":"f29bf8ce3591f31b9115b8714fa4b2a2c25eba85e4d5a48d5144c3dc394818b5","schema_version":"1.0","event_id":"sha256:f29bf8ce3591f31b9115b8714fa4b2a2c25eba85e4d5a48d5144c3dc394818b5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/77MXAPOULIIAJLLOWV77JHF3XY/bundle.json","state_url":"https://pith.science/pith/77MXAPOULIIAJLLOWV77JHF3XY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/77MXAPOULIIAJLLOWV77JHF3XY/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":"2026-08-04T15:00:54Z","links":{"resolver":"https://pith.science/pith/77MXAPOULIIAJLLOWV77JHF3XY","bundle":"https://pith.science/pith/77MXAPOULIIAJLLOWV77JHF3XY/bundle.json","state":"https://pith.science/pith/77MXAPOULIIAJLLOWV77JHF3XY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/77MXAPOULIIAJLLOWV77JHF3XY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:77MXAPOULIIAJLLOWV77JHF3XY","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":"6270c36196b32774c63b5590ee0bee587238dc3f87cd18a5e1fa2c40a768e2a1","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-09T15:03:36Z","title_canon_sha256":"269847ddb64fe7e5be90a6904fe6f3d6ccb3db12a7d514feec864fcccf264f32"},"schema_version":"1.0","source":{"id":"2406.05815","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.05815","created_at":"2026-07-05T09:15:33Z"},{"alias_kind":"arxiv_version","alias_value":"2406.05815v2","created_at":"2026-07-05T09:15:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.05815","created_at":"2026-07-05T09:15:33Z"},{"alias_kind":"pith_short_12","alias_value":"77MXAPOULIIA","created_at":"2026-07-05T09:15:33Z"},{"alias_kind":"pith_short_16","alias_value":"77MXAPOULIIAJLLO","created_at":"2026-07-05T09:15:33Z"},{"alias_kind":"pith_short_8","alias_value":"77MXAPOU","created_at":"2026-07-05T09:15:33Z"}],"graph_snapshots":[{"event_id":"sha256:f29bf8ce3591f31b9115b8714fa4b2a2c25eba85e4d5a48d5144c3dc394818b5","target":"graph","created_at":"2026-07-05T09:15:33Z","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/2406.05815/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Machine learning on graphs has recently found extensive applications across domains. However, the commonly used Message Passing Neural Networks (MPNNs) suffer from limited expressive power and struggle to capture long-range dependencies. Graph transformers offer a strong alternative due to their global attention mechanism, but they come with great computational overheads, especially for large graphs. In recent years, State Space Models (SSMs) have emerged as a compelling approach to replace full attention in transformers to model sequential data. It blends the strengths of RNNs and CNNs, offer","authors_text":"Pan Li, Siqi Miao, Yinan Huang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-09T15:03:36Z","title":"What Can We Learn from State Space Models for Machine Learning on Graphs?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.05815","kind":"arxiv","version":2},"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":"sha256:9d53c8f2cd393a45aeb4474dba5edc3a9e62d48752adf2caecf1df5c1f80d017","target":"record","created_at":"2026-07-05T09:15:33Z","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":"6270c36196b32774c63b5590ee0bee587238dc3f87cd18a5e1fa2c40a768e2a1","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-09T15:03:36Z","title_canon_sha256":"269847ddb64fe7e5be90a6904fe6f3d6ccb3db12a7d514feec864fcccf264f32"},"schema_version":"1.0","source":{"id":"2406.05815","kind":"arxiv","version":2}},"canonical_sha256":"ffd9703dd45a1004ad6eb57ff49cbbbe12cc556a10fa2cef88838a239c8f2fd1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ffd9703dd45a1004ad6eb57ff49cbbbe12cc556a10fa2cef88838a239c8f2fd1","first_computed_at":"2026-07-05T09:15:33.375752Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:15:33.375752Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PHfm9KKGPPUxQIRaVWGz7P9+pPclCvVNk1oTB+3HIrT9Vc8kTxmo1bhKNAk9JRX5JrHNXGzC6RD4PeqWy0Y9AA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:15:33.376293Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.05815","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9d53c8f2cd393a45aeb4474dba5edc3a9e62d48752adf2caecf1df5c1f80d017","sha256:f29bf8ce3591f31b9115b8714fa4b2a2c25eba85e4d5a48d5144c3dc394818b5"],"state_sha256":"87d63b55b40a833001717ebf2fb0425e6c674eeea6c7145152065e0dde2a044d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"r2E50ybjvVG13BucnGofx28T+PlXYmFrEh5x+CFMrnUXMtkhvbdd2lcSbUem5sM/04rSTzTBv1xjSOsWaoF/Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T15:00:54.349580Z","bundle_sha256":"8bbe2a95f2cdb4bfb09bed2edd898e6825e53373203e6dec76b5dddb4c8b3e2f"}}