{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:IDTMCYZQQD7EIJ4DPZQD5GJVQU","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":"ddc7fc8952349302e5444b789bfd76e2c5df97b838b1132e644d1563386b19ff","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2026-01-09T06:38:47Z","title_canon_sha256":"21c676097cad0fa78b01764f46e9c8c45d80535d2d1f42e5dca2a82bfc41c1f1"},"schema_version":"1.0","source":{"id":"2601.05569","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2601.05569","created_at":"2026-05-17T23:39:00Z"},{"alias_kind":"arxiv_version","alias_value":"2601.05569v2","created_at":"2026-05-17T23:39:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.05569","created_at":"2026-05-17T23:39:00Z"},{"alias_kind":"pith_short_12","alias_value":"IDTMCYZQQD7E","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"IDTMCYZQQD7EIJ4D","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"IDTMCYZQ","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:abb94334fe47d5922db5603f810d59a7630a30b5a87c3ffe63538024c9229d58","target":"graph","created_at":"2026-05-17T23:39:00Z","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":"The framework maintains dual memory systems tracking both long term performance patterns and short term workload statistics and achieves 87.3 percent memory utilization efficiency and 142.5 operations per second compared to Ray Distributed at 72.1 percent and 98.7 operations per second, while reducing communication latency by 30.2 percent to 171.2 milliseconds and improving resource utilization to 82.7 percent."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The assumption that dynamic partitioning based on device characteristics, memory aware peer selection, and continuous reconfiguration can be effectively implemented and will lead to the reported performance gains without additional overheads or complexities in real-world NAT-constrained networks."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A unified three-layer memory management framework for distributed AI achieves 87.3% memory utilization and 142.5 ops/sec on benchmarks, outperforming Ray Distributed."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A self-evolving three-layer memory architecture unifies computation, communication, and deployment management in distributed AI systems."}],"snapshot_sha256":"be2dec875bb9724f2e025999f313731bce82458ced8966fac18ecacf0ab22bab"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Distributed AI systems face critical memory management challenges across computation, communication, and deployment layers. RRAM based in memory computing suffers from scalability limitations due to device non idealities and fixed array sizes. Decentralized AI frameworks struggle with memory efficiency across NAT constrained networks due to static routing that ignores computational load. Multi agent deployment systems tightly couple application logic with execution environments, preventing adaptive memory optimization. These challenges stem from a fundamental lack of coordinated memory managem","authors_text":"Chuanzhen Wang, Haotian Sun, Zixuan Li","cross_cats":[],"headline":"A self-evolving three-layer memory architecture unifies computation, communication, and deployment management in distributed AI systems.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2026-01-09T06:38:47Z","title":"Self-Evolving Distributed Memory Architecture for Scalable AI Systems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.05569","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-16T16:00:11.254344Z","id":"0b82a055-c250-4bf9-b79f-6ecdfbf9f57a","model_set":{"reader":"grok-4.3"},"one_line_summary":"A unified three-layer memory management framework for distributed AI achieves 87.3% memory utilization and 142.5 ops/sec on benchmarks, outperforming Ray Distributed.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A self-evolving three-layer memory architecture unifies computation, communication, and deployment management in distributed AI systems.","strongest_claim":"The framework maintains dual memory systems tracking both long term performance patterns and short term workload statistics and achieves 87.3 percent memory utilization efficiency and 142.5 operations per second compared to Ray Distributed at 72.1 percent and 98.7 operations per second, while reducing communication latency by 30.2 percent to 171.2 milliseconds and improving resource utilization to 82.7 percent.","weakest_assumption":"The assumption that dynamic partitioning based on device characteristics, memory aware peer selection, and continuous reconfiguration can be effectively implemented and will lead to the reported performance gains without additional overheads or complexities in real-world NAT-constrained networks."}},"verdict_id":"0b82a055-c250-4bf9-b79f-6ecdfbf9f57a"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:8b93e37fd3f230900862548da571e65b03311d21cbda8d79f41e19c43b5412cf","target":"record","created_at":"2026-05-17T23:39:00Z","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":"ddc7fc8952349302e5444b789bfd76e2c5df97b838b1132e644d1563386b19ff","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.DC","submitted_at":"2026-01-09T06:38:47Z","title_canon_sha256":"21c676097cad0fa78b01764f46e9c8c45d80535d2d1f42e5dca2a82bfc41c1f1"},"schema_version":"1.0","source":{"id":"2601.05569","kind":"arxiv","version":2}},"canonical_sha256":"40e6c1633080fe4427837e603e99358529daf23d57f87f49db9ba7ff6864c440","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"40e6c1633080fe4427837e603e99358529daf23d57f87f49db9ba7ff6864c440","first_computed_at":"2026-05-17T23:39:00.295449Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:39:00.295449Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"oy+Y0w6j6OEoxPk94eF4Hes+e3RboTndbVtx24CbvDS/6D+WGx5/Czp0yfEek+Yj+v8FKJR0U6sqNdG9pPtQDQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:39:00.296169Z","signed_message":"canonical_sha256_bytes"},"source_id":"2601.05569","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8b93e37fd3f230900862548da571e65b03311d21cbda8d79f41e19c43b5412cf","sha256:abb94334fe47d5922db5603f810d59a7630a30b5a87c3ffe63538024c9229d58"],"state_sha256":"019104857f907a156e54384f5505350a9f204b35813ca8e0c7d23a92a0515308"}