{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:CPCDD6YKYMIHXERGTHGSOALRIF","short_pith_number":"pith:CPCDD6YK","canonical_record":{"source":{"id":"2407.10805","kind":"arxiv","version":7},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-07-15T15:20:40Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"842d3773efdeab588e144c394e921417cf0612c00dff41ea5102f0b419ab78ed","abstract_canon_sha256":"16ba703eb2bc0fb318bf36aa99027263d5c680304bca3b60e61ca93ea6b56865"},"schema_version":"1.0"},"canonical_sha256":"13c431fb0ac3107b922699cd2701714148e3438e096d124e6279babe87300824","source":{"kind":"arxiv","id":"2407.10805","version":7},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.10805","created_at":"2026-07-05T10:11:22Z"},{"alias_kind":"arxiv_version","alias_value":"2407.10805v7","created_at":"2026-07-05T10:11:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.10805","created_at":"2026-07-05T10:11:22Z"},{"alias_kind":"pith_short_12","alias_value":"CPCDD6YKYMIH","created_at":"2026-07-05T10:11:22Z"},{"alias_kind":"pith_short_16","alias_value":"CPCDD6YKYMIHXERG","created_at":"2026-07-05T10:11:22Z"},{"alias_kind":"pith_short_8","alias_value":"CPCDD6YK","created_at":"2026-07-05T10:11:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:CPCDD6YKYMIHXERGTHGSOALRIF","target":"record","payload":{"canonical_record":{"source":{"id":"2407.10805","kind":"arxiv","version":7},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-07-15T15:20:40Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"842d3773efdeab588e144c394e921417cf0612c00dff41ea5102f0b419ab78ed","abstract_canon_sha256":"16ba703eb2bc0fb318bf36aa99027263d5c680304bca3b60e61ca93ea6b56865"},"schema_version":"1.0"},"canonical_sha256":"13c431fb0ac3107b922699cd2701714148e3438e096d124e6279babe87300824","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:11:22.149448Z","signature_b64":"RXqN2kWomAxvWLAspuQVqBSKsMSlL+RZ0civ5tgYxwOSx+smjn77NCnAtw55SSGqvDcK4gROcoBgvvrbY+o2Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"13c431fb0ac3107b922699cd2701714148e3438e096d124e6279babe87300824","last_reissued_at":"2026-07-05T10:11:22.148938Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:11:22.148938Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2407.10805","source_version":7,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:11:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6L6Y7+LnGoZqCxbniS7Kyj4fnx3vdnMKT49Xcxzlv1e/gmM8vnE2Sb5L470hqU4bpvRq4GYYscgf1+FHLD70Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T23:46:24.707411Z"},"content_sha256":"2cf0372ab1e148906acb21d6c4b5154b68758a7bafecdd1c2949dbf89a8deae1","schema_version":"1.0","event_id":"sha256:2cf0372ab1e148906acb21d6c4b5154b68758a7bafecdd1c2949dbf89a8deae1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:CPCDD6YKYMIHXERGTHGSOALRIF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Cehao Yang, Chengjin Xu, Huaren Qu, Jian Guo, Jiaxin Mao, Muzhi Li, Shengjie Ma, Xuhui Jiang","submitted_at":"2024-07-15T15:20:40Z","abstract_excerpt":"Retrieval-augmented generation (RAG) has improved large language models (LLMs) by using knowledge retrieval to overcome knowledge deficiencies. However, current RAG methods often fall short of ensuring the depth and completeness of retrieved information, which is necessary for complex reasoning tasks. In this work, we introduce Think-on-Graph 2.0 (ToG-2), a hybrid RAG framework that iteratively retrieves information from both unstructured and structured knowledge sources in a tight-coupling manner. Specifically, ToG-2 leverages knowledge graphs (KGs) to link documents via entities, facilitatin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.10805","kind":"arxiv","version":7},"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/2407.10805/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":"2026-07-05T10:11:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xxwIxQhlGB+DkL2d9rwN3fKHyHU7y/pZo7SrB4LdPvqzPo8dtxufEiAxaEUSVjOuqw4kwIJxTqkMMQ0bA2XxAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T23:46:24.707810Z"},"content_sha256":"7ec857188701b8775bff9f0fdad9df7642c15a9ecbb2c386b015f4dbed51f9c9","schema_version":"1.0","event_id":"sha256:7ec857188701b8775bff9f0fdad9df7642c15a9ecbb2c386b015f4dbed51f9c9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CPCDD6YKYMIHXERGTHGSOALRIF/bundle.json","state_url":"https://pith.science/pith/CPCDD6YKYMIHXERGTHGSOALRIF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CPCDD6YKYMIHXERGTHGSOALRIF/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":"2026-08-07T23:46:24Z","links":{"resolver":"https://pith.science/pith/CPCDD6YKYMIHXERGTHGSOALRIF","bundle":"https://pith.science/pith/CPCDD6YKYMIHXERGTHGSOALRIF/bundle.json","state":"https://pith.science/pith/CPCDD6YKYMIHXERGTHGSOALRIF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CPCDD6YKYMIHXERGTHGSOALRIF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:CPCDD6YKYMIHXERGTHGSOALRIF","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":"16ba703eb2bc0fb318bf36aa99027263d5c680304bca3b60e61ca93ea6b56865","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-07-15T15:20:40Z","title_canon_sha256":"842d3773efdeab588e144c394e921417cf0612c00dff41ea5102f0b419ab78ed"},"schema_version":"1.0","source":{"id":"2407.10805","kind":"arxiv","version":7}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.10805","created_at":"2026-07-05T10:11:22Z"},{"alias_kind":"arxiv_version","alias_value":"2407.10805v7","created_at":"2026-07-05T10:11:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.10805","created_at":"2026-07-05T10:11:22Z"},{"alias_kind":"pith_short_12","alias_value":"CPCDD6YKYMIH","created_at":"2026-07-05T10:11:22Z"},{"alias_kind":"pith_short_16","alias_value":"CPCDD6YKYMIHXERG","created_at":"2026-07-05T10:11:22Z"},{"alias_kind":"pith_short_8","alias_value":"CPCDD6YK","created_at":"2026-07-05T10:11:22Z"}],"graph_snapshots":[{"event_id":"sha256:7ec857188701b8775bff9f0fdad9df7642c15a9ecbb2c386b015f4dbed51f9c9","target":"graph","created_at":"2026-07-05T10:11:22Z","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/2407.10805/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Retrieval-augmented generation (RAG) has improved large language models (LLMs) by using knowledge retrieval to overcome knowledge deficiencies. However, current RAG methods often fall short of ensuring the depth and completeness of retrieved information, which is necessary for complex reasoning tasks. In this work, we introduce Think-on-Graph 2.0 (ToG-2), a hybrid RAG framework that iteratively retrieves information from both unstructured and structured knowledge sources in a tight-coupling manner. Specifically, ToG-2 leverages knowledge graphs (KGs) to link documents via entities, facilitatin","authors_text":"Cehao Yang, Chengjin Xu, Huaren Qu, Jian Guo, Jiaxin Mao, Muzhi Li, Shengjie Ma, Xuhui Jiang","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-07-15T15:20:40Z","title":"Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.10805","kind":"arxiv","version":7},"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":"sha256:2cf0372ab1e148906acb21d6c4b5154b68758a7bafecdd1c2949dbf89a8deae1","target":"record","created_at":"2026-07-05T10:11:22Z","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":"16ba703eb2bc0fb318bf36aa99027263d5c680304bca3b60e61ca93ea6b56865","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-07-15T15:20:40Z","title_canon_sha256":"842d3773efdeab588e144c394e921417cf0612c00dff41ea5102f0b419ab78ed"},"schema_version":"1.0","source":{"id":"2407.10805","kind":"arxiv","version":7}},"canonical_sha256":"13c431fb0ac3107b922699cd2701714148e3438e096d124e6279babe87300824","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"13c431fb0ac3107b922699cd2701714148e3438e096d124e6279babe87300824","first_computed_at":"2026-07-05T10:11:22.148938Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:11:22.148938Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RXqN2kWomAxvWLAspuQVqBSKsMSlL+RZ0civ5tgYxwOSx+smjn77NCnAtw55SSGqvDcK4gROcoBgvvrbY+o2Ag==","signature_status":"signed_v1","signed_at":"2026-07-05T10:11:22.149448Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.10805","source_kind":"arxiv","source_version":7}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2cf0372ab1e148906acb21d6c4b5154b68758a7bafecdd1c2949dbf89a8deae1","sha256:7ec857188701b8775bff9f0fdad9df7642c15a9ecbb2c386b015f4dbed51f9c9"],"state_sha256":"30a8b87e7301c35cccee571a82c1f24f5ea90466abfb0681464c084130b3385f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YJNOoD27rfmIcj1dbFxBN7HePrw4NP8Cnhi8YDWC9VEATyzM8kt6iWkl5NujWzAnmtL+y4UBebKQ0/a12KrQCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T23:46:24.711271Z","bundle_sha256":"22220f6aeb2a56e3093923adeb3227539a445942b355c037a7757f7ce4ed2722"}}