{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:N4DI56SHWDQGRLCWBXA7NZJJXL","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":"77c885b0d77c6a859265469138d8a457517131ef596e2014c52c631357a59e35","cross_cats_sorted":["cs.AI","cs.MA"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2026-05-30T08:18:53Z","title_canon_sha256":"7b8deb08b0cee523f8f88e9014b51a94fc6d7cf50d0859348123d6b1307fa1b6"},"schema_version":"1.0","source":{"id":"2606.00610","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.00610","created_at":"2026-06-02T01:03:59Z"},{"alias_kind":"arxiv_version","alias_value":"2606.00610v1","created_at":"2026-06-02T01:03:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.00610","created_at":"2026-06-02T01:03:59Z"},{"alias_kind":"pith_short_12","alias_value":"N4DI56SHWDQG","created_at":"2026-06-02T01:03:59Z"},{"alias_kind":"pith_short_16","alias_value":"N4DI56SHWDQGRLCW","created_at":"2026-06-02T01:03:59Z"},{"alias_kind":"pith_short_8","alias_value":"N4DI56SH","created_at":"2026-06-02T01:03:59Z"}],"graph_snapshots":[{"event_id":"sha256:6bf2a293a7b6d881c5db18568778d2a024e087eb60f2b60cebea086f2c0f6810","target":"graph","created_at":"2026-06-02T01:03:59Z","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/2606.00610/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Retrieval-Augmented Generation (RAG) has become an essential method for mitigating hallucinations in Large Language Models (LLMs) by leveraging external knowledge. Although effective for simple queries, traditional RAG struggles with large-scale, unstructured corpora where information is highly fragmented. Graph-based RAG (GraphRAG) incorporates knowledge graphs to capture structural relationships, enabling more comprehensive retrieval for complex reasoning. However, existing GraphRAG methods rely on isolated, fragment-level extraction for graph construction, lacking a global perspective on th","authors_text":"Chuanjie Wu, Jinsong Su, Qinggang Zhang, Yunbo Tang, Zerui Chen, Zhishang Xiang","cross_cats":["cs.AI","cs.MA"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2026-05-30T08:18:53Z","title":"MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.00610","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":"sha256:76b7ab8f2a105a4d5123c5a44584fba5af6d88668307606160c44e28bdf56a77","target":"record","created_at":"2026-06-02T01:03:59Z","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":"77c885b0d77c6a859265469138d8a457517131ef596e2014c52c631357a59e35","cross_cats_sorted":["cs.AI","cs.MA"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2026-05-30T08:18:53Z","title_canon_sha256":"7b8deb08b0cee523f8f88e9014b51a94fc6d7cf50d0859348123d6b1307fa1b6"},"schema_version":"1.0","source":{"id":"2606.00610","kind":"arxiv","version":1}},"canonical_sha256":"6f068efa47b0e068ac560dc1f6e529bad6e3464f69dba33201787a2da45b512d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6f068efa47b0e068ac560dc1f6e529bad6e3464f69dba33201787a2da45b512d","first_computed_at":"2026-06-02T01:03:59.594753Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-02T01:03:59.594753Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Og+sni1ZswPZm2B6ODJ17QzDo9KYLJUuhEahPpdA7cQERuYMKh0x+b0nMkmM+GRj/LfVNNHwPYWWEHxXXUPqCQ==","signature_status":"signed_v1","signed_at":"2026-06-02T01:03:59.595168Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.00610","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:76b7ab8f2a105a4d5123c5a44584fba5af6d88668307606160c44e28bdf56a77","sha256:6bf2a293a7b6d881c5db18568778d2a024e087eb60f2b60cebea086f2c0f6810"],"state_sha256":"bc5697eef4e35a46c96de9155e56af839e3f9406e10e76c4a3c8b859f933a480"}