{"as_of":"2026-08-08T09:57:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:459b28d063bd6f9fc3b89c5af67b2a0fc650cc1f706461fcba8694152454fb4f","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":19,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":19,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":19,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":19,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T14:58:36.096469Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":9,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2407.10805","last_updated":"2025-02-10T03:16:09Z","snapshot_observed_at":"2026-08-01T01:40:45.155448Z","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","version":7},"cited_work":{"arxiv_id":"2407.10805","doi":"10.48550/arxiv.2407.10805","metadata_source":"arxiv_reference","pith_arxiv_id":"2407.10805","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Think-on-graph 2.0: Deep and faithful large language model reasoning with knowledge- guided retrieval augmented generation.arXiv preprint arXiv:2407.10805","venue":"arXiv (Cornell University)","work_id":"7efc1432-6506-480c-a5c7-a38d6f45c7da","year":2025},"citing_paper":{"arxiv_id":"2501.00309","last_updated":"2025-01-08T05:16:25Z","snapshot_observed_at":"2026-08-07T12:37:34.294206Z","submitted_at":"2024-12-31T06:59:35Z","title":"Retrieval-Augmented Generation with Graphs (GraphRAG)","version":2},"reference_index":278,"source":"pdf_text","source_observed_at":"2026-05-18T04:33:39.076517Z"},"links":{"cited_paper":"/paper/2407.10805","citing_paper":"/paper/2501.00309"},"observation_digest":"sha256:446c8ff2f447a0669d716b61278526578c0329a7a47dc44b864b3c378a3e8e40","observation_id":"d469cd77-5360-4b39-be0c-ab0e9fc5e841","resolution":{"observed_at":"2026-05-18T04:33:39.781476Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10805","last_updated":"2025-02-10T03:16:09Z","snapshot_observed_at":"2026-08-01T01:40:45.155448Z","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","version":7},"cited_work":{"arxiv_id":"2407.10805","doi":"10.48550/arxiv.2407.10805","metadata_source":"arxiv_reference","pith_arxiv_id":"2407.10805","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Think-on-graph 2.0: Deep and faithful large language model reasoning with knowledge- guided retrieval augmented generation.arXiv preprint arXiv:2407.10805","venue":"arXiv (Cornell University)","work_id":"7efc1432-6506-480c-a5c7-a38d6f45c7da","year":2025},"citing_paper":{"arxiv_id":"2502.09891","last_updated":"2026-05-11T03:00:07Z","snapshot_observed_at":"2026-07-06T20:36:26.822412Z","submitted_at":"2025-02-14T03:28:36Z","title":"ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation","version":4},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-05-23T03:28:13.313028Z"},"links":{"cited_paper":"/paper/2407.10805","citing_paper":"/paper/2502.09891"},"observation_digest":"sha256:bf866fdf32aadf01577c1dddf029ecee3bacd90406b7585d1e31dd5e17a4c134","observation_id":"03e6b4ee-614f-4a06-9b62-8e656f744de0","resolution":{"observed_at":"2026-05-23T03:32:28.652259Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10805","last_updated":"2025-02-10T03:16:09Z","snapshot_observed_at":"2026-08-01T01:40:45.155448Z","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","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.10805","snapshot_observed_at":"2026-08-07T14:58:36.096469Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.16849","last_updated":"2025-05-28T07:44:23Z","snapshot_observed_at":"2026-08-07T14:51:51.125807Z","submitted_at":"2025-05-22T16:11:35Z","title":"Walk&Retrieve: Simple Yet Effective Zero-shot Retrieval-Augmented Generation via Knowledge Graph Walks","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T14:58:36.096469Z"},"links":{"cited_paper":"/paper/2407.10805","citing_paper":"/paper/2505.16849"},"observation_digest":"sha256:abb16a54ff27d87c571a54afbe9bab068cd77665daa6b1fbfa90de347b74cac4","observation_id":"68ee5685-dd9e-48cb-b073-da7e5ecf13eb","resolution":{"observed_at":"2026-08-07T14:58:36.096469Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10805","last_updated":"2025-02-10T03:16:09Z","snapshot_observed_at":"2026-08-01T01:40:45.155448Z","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","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.10805","snapshot_observed_at":"2026-08-07T11:32:56.766660Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.01954","last_updated":"2025-06-02T17:59:51Z","snapshot_observed_at":"2026-08-07T11:28:24.486308Z","submitted_at":"2025-06-02T17:59:51Z","title":"DRAG: Distilling RAG for SLMs from LLMs to Transfer Knowledge and Mitigate Hallucination via Evidence and Graph-based Distillation","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-07T11:32:56.766660Z"},"links":{"cited_paper":"/paper/2407.10805","citing_paper":"/paper/2506.01954"},"observation_digest":"sha256:d3ad64b58b0ecd2c3daaedaa5c48d614706c28f3c13911ce6d4ff016717ffdfa","observation_id":"6e0f51af-416b-4456-b823-d4ba5c9a9287","resolution":{"observed_at":"2026-08-07T11:32:56.766660Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10805","last_updated":"2025-02-10T03:16:09Z","snapshot_observed_at":"2026-08-01T01:40:45.155448Z","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","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.10805","snapshot_observed_at":"2026-08-07T05:40:09.418153Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.07449","last_updated":"2025-06-09T05:52:03Z","snapshot_observed_at":"2026-08-07T05:31:08.553616Z","submitted_at":"2025-06-09T05:52:03Z","title":"LlamaRec-LKG-RAG: A Single-Pass, Learnable Knowledge Graph-RAG Framework for LLM-Based Ranking","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T05:40:09.418153Z"},"links":{"cited_paper":"/paper/2407.10805","citing_paper":"/paper/2506.07449"},"observation_digest":"sha256:c2da09d1bdc7d9ce7d9d099c39f870f9488f2ebd4db0c8d75cd9aaec996237c7","observation_id":"e77b66fe-49ae-43d7-9076-51ccb987454b","resolution":{"observed_at":"2026-08-07T05:40:09.418153Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10805","last_updated":"2025-02-10T03:16:09Z","snapshot_observed_at":"2026-08-01T01:40:45.155448Z","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","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.10805","snapshot_observed_at":"2026-08-07T04:48:08.680583Z","title":"Think-on-Graph 2.0: Deep and Interpretable Large Language Model Reasoning with Knowledge Graph-guided Retrieval.arXiv preprint arXiv:2407.10805, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.09566","last_updated":"2025-06-11T09:58:14Z","snapshot_observed_at":"2026-08-07T04:43:00.864186Z","submitted_at":"2025-06-11T09:58:14Z","title":"From Symbolic to Neural and Back: Exploring Knowledge Graph-Large Language Model Synergies","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T04:48:08.680583Z"},"links":{"cited_paper":"/paper/2407.10805","citing_paper":"/paper/2506.09566"},"observation_digest":"sha256:b141835254bae76dfdf77f0107bc39bf81d3cd030186e1edce90ae861a7b692e","observation_id":"bc272e87-0bbb-4186-ab94-2e17387b5b68","resolution":{"observed_at":"2026-08-07T04:48:08.680583Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10805","last_updated":"2025-02-10T03:16:09Z","snapshot_observed_at":"2026-08-01T01:40:45.155448Z","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","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.10805","snapshot_observed_at":"2026-08-07T05:10:57.026254Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.13782","last_updated":"2025-06-10T09:14:30Z","snapshot_observed_at":"2026-08-07T20:33:56.432904Z","submitted_at":"2025-06-10T09:14:30Z","title":"XGraphRAG: Interactive Visual Analysis for Graph-based Retrieval-Augmented Generation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T05:10:57.026254Z"},"links":{"cited_paper":"/paper/2407.10805","citing_paper":"/paper/2506.13782"},"observation_digest":"sha256:10e57388ca01b5ad615af277a164bfd1d115eae1cc1bb829d94962dc5499f2e8","observation_id":"11f28cf7-1b54-492e-b3ea-35277df19a36","resolution":{"observed_at":"2026-08-07T05:10:57.026254Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10805","last_updated":"2025-02-10T03:16:09Z","snapshot_observed_at":"2026-08-01T01:40:45.155448Z","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","version":7},"cited_work":{"arxiv_id":"2407.10805","doi":"10.48550/arxiv.2407.10805","metadata_source":"arxiv_reference","pith_arxiv_id":"2407.10805","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Think-on-graph 2.0: Deep and faithful large language model reasoning with knowledge- guided retrieval augmented generation.arXiv preprint arXiv:2407.10805","venue":"arXiv (Cornell University)","work_id":"7efc1432-6506-480c-a5c7-a38d6f45c7da","year":2025},"citing_paper":{"arxiv_id":"2510.20505","last_updated":"2026-04-23T06:10:19Z","snapshot_observed_at":"2026-07-06T22:33:53.834577Z","submitted_at":"2025-10-23T12:48:18Z","title":"RELOOP: Recursive Retrieval with Multi-Hop Reasoner and Planners for Heterogeneous QA","version":4},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-18T04:55:03.309430Z"},"links":{"cited_paper":"/paper/2407.10805","citing_paper":"/paper/2510.20505"},"observation_digest":"sha256:affb9f5f10119358a5d7455b7eaadde287fe1828a5aa48b6170459481abde101","observation_id":"30105193-f17d-483a-b40a-88c20b43adf6","resolution":{"observed_at":"2026-05-18T04:55:54.126353Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10805","last_updated":"2025-02-10T03:16:09Z","snapshot_observed_at":"2026-08-01T01:40:45.155448Z","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","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.10805","snapshot_observed_at":"2026-08-03T23:33:47.103675Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2511.05385","last_updated":"2026-07-23T06:36:18Z","snapshot_observed_at":"2026-08-07T22:51:42.589401Z","submitted_at":"2025-11-07T16:08:34Z","title":"TeaRAG: A Token-Efficient Agentic Retrieval-Augmented Generation Framework","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-03T23:33:47.103675Z"},"links":{"cited_paper":"/paper/2407.10805","citing_paper":"/paper/2511.05385"},"observation_digest":"sha256:54b56f3b1455d6010f2bd738f13e0c4894f9c9e7ae8ed11f397843b92cdf94ab","observation_id":"c36c8717-5cda-4ccb-9be6-4d13d2f1e037","resolution":{"observed_at":"2026-08-03T23:33:47.103675Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10805","last_updated":"2025-02-10T03:16:09Z","snapshot_observed_at":"2026-08-01T01:40:45.155448Z","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","version":7},"cited_work":{"arxiv_id":"2407.10805","doi":"10.48550/arxiv.2407.10805","metadata_source":"arxiv_reference","pith_arxiv_id":"2407.10805","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Think-on-graph 2.0: Deep and faithful large language model reasoning with knowledge- guided retrieval augmented generation.arXiv preprint arXiv:2407.10805","venue":"arXiv (Cornell University)","work_id":"7efc1432-6506-480c-a5c7-a38d6f45c7da","year":2025},"citing_paper":{"arxiv_id":"2603.23231","last_updated":"2026-05-17T13:59:13Z","snapshot_observed_at":"2026-08-02T05:44:37.993945Z","submitted_at":"2026-03-24T14:04:11Z","title":"PERMA: Benchmarking Personalized Memory Agents via Event-Driven Preference and Realistic Task Environments","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-21T09:55:17.236296Z"},"links":{"cited_paper":"/paper/2407.10805","citing_paper":"/paper/2603.23231"},"observation_digest":"sha256:111ca7cf33888d869462b410490930c9fa8e7be3bc00ada0c9e3f9cc3b5eba24","observation_id":"444242c6-0f67-4e67-bc4a-2eced48693ec","resolution":{"observed_at":"2026-05-21T09:59:58.997784Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10805","last_updated":"2025-02-10T03:16:09Z","snapshot_observed_at":"2026-08-01T01:40:45.155448Z","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","version":7},"cited_work":{"arxiv_id":"2407.10805","doi":"10.48550/arxiv.2407.10805","metadata_source":"arxiv_reference","pith_arxiv_id":"2407.10805","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Think-on-graph 2.0: Deep and faithful large language model reasoning with knowledge- guided retrieval augmented generation.arXiv preprint arXiv:2407.10805","venue":"arXiv (Cornell University)","work_id":"7efc1432-6506-480c-a5c7-a38d6f45c7da","year":2025},"citing_paper":{"arxiv_id":"2604.05076","last_updated":"2026-04-06T18:21:32Z","snapshot_observed_at":"2026-07-06T22:53:55.926521Z","submitted_at":"2026-04-06T18:21:32Z","title":"GLANCE: A Global-Local Coordination Multi-Agent Framework for Music-Grounded Non-Linear Video Editing","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-10T19:13:30.400059Z"},"links":{"cited_paper":"/paper/2407.10805","citing_paper":"/paper/2604.05076"},"observation_digest":"sha256:0fb80ad11e3b8433a7697df2f02a5e44602ee5c38765304f027154be2369b435","observation_id":"5ffcb8d0-6b0b-4465-aefe-c8ae3a7d52a9","resolution":{"observed_at":"2026-05-10T23:20:51.194685Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10805","last_updated":"2025-02-10T03:16:09Z","snapshot_observed_at":"2026-08-01T01:40:45.155448Z","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","version":7},"cited_work":{"arxiv_id":"2407.10805","doi":"10.48550/arxiv.2407.10805","metadata_source":"arxiv_reference","pith_arxiv_id":"2407.10805","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Think-on-graph 2.0: Deep and faithful large language model reasoning with knowledge- guided retrieval augmented generation.arXiv preprint arXiv:2407.10805","venue":"arXiv (Cornell University)","work_id":"7efc1432-6506-480c-a5c7-a38d6f45c7da","year":2025},"citing_paper":{"arxiv_id":"2604.08603","last_updated":"2026-04-08T06:07:48Z","snapshot_observed_at":"2026-08-03T09:06:40.497418Z","submitted_at":"2026-04-08T06:07:48Z","title":"From Business Events to Auditable Decisions: Ontology-Governed Graph Simulation for Enterprise AI","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-10T18:26:16.639817Z"},"links":{"cited_paper":"/paper/2407.10805","citing_paper":"/paper/2604.08603"},"observation_digest":"sha256:449f0306ce23bc5176afa3b2ba8bde9d4d0aae15ee520fddaef548f590db44c8","observation_id":"997a5caa-6224-455e-aa62-ea4c4ac70bec","resolution":{"observed_at":"2026-05-11T00:35:51.111860Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10805","last_updated":"2025-02-10T03:16:09Z","snapshot_observed_at":"2026-08-01T01:40:45.155448Z","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","version":7},"cited_work":{"arxiv_id":"2407.10805","doi":"10.48550/arxiv.2407.10805","metadata_source":"arxiv_reference","pith_arxiv_id":"2407.10805","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Think-on-graph 2.0: Deep and faithful large language model reasoning with knowledge- guided retrieval augmented generation.arXiv preprint arXiv:2407.10805","venue":"arXiv (Cornell University)","work_id":"7efc1432-6506-480c-a5c7-a38d6f45c7da","year":2025},"citing_paper":{"arxiv_id":"2604.11193","last_updated":"2026-04-13T08:49:39Z","snapshot_observed_at":"2026-07-06T22:59:40.900521Z","submitted_at":"2026-04-13T08:49:39Z","title":"TRACE: An Experiential Framework for Coherent Multi-hop Knowledge Graph Question Answering","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-10T15:17:59.538800Z"},"links":{"cited_paper":"/paper/2407.10805","citing_paper":"/paper/2604.11193"},"observation_digest":"sha256:60b8a032b0a8a110682d8bf674447e87112b1c6405c4f3561ff863af087c4ff1","observation_id":"57552569-a4b5-4b8a-a12f-3e79abaf95b5","resolution":{"observed_at":"2026-05-11T10:51:03.083899Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10805","last_updated":"2025-02-10T03:16:09Z","snapshot_observed_at":"2026-08-01T01:40:45.155448Z","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","version":7},"cited_work":{"arxiv_id":"2407.10805","doi":"10.48550/arxiv.2407.10805","metadata_source":"arxiv_reference","pith_arxiv_id":"2407.10805","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Think-on-graph 2.0: Deep and faithful large language model reasoning with knowledge- guided retrieval augmented generation.arXiv preprint arXiv:2407.10805","venue":"arXiv (Cornell University)","work_id":"7efc1432-6506-480c-a5c7-a38d6f45c7da","year":2025},"citing_paper":{"arxiv_id":"2605.02452","last_updated":"2026-05-04T10:56:05Z","snapshot_observed_at":"2026-08-02T17:05:44.125833Z","submitted_at":"2026-05-04T10:56:05Z","title":"Position: How can Graphs Help Large Language Models?","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-08T18:48:03.257015Z"},"links":{"cited_paper":"/paper/2407.10805","citing_paper":"/paper/2605.02452"},"observation_digest":"sha256:fd5ebc9a11d20999efa5701b578688998d1df203dca974c4e25408d0f18b87c1","observation_id":"af5eec32-1f87-4755-9952-efa622703589","resolution":{"observed_at":"2026-05-09T06:10:42.830741Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10805","last_updated":"2025-02-10T03:16:09Z","snapshot_observed_at":"2026-08-01T01:40:45.155448Z","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","version":7},"cited_work":{"arxiv_id":"2407.10805","doi":"10.48550/arxiv.2407.10805","metadata_source":"arxiv_reference","pith_arxiv_id":"2407.10805","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Think-on-graph 2.0: Deep and faithful large language model reasoning with knowledge- guided retrieval augmented generation.arXiv preprint arXiv:2407.10805","venue":"arXiv (Cornell University)","work_id":"7efc1432-6506-480c-a5c7-a38d6f45c7da","year":2025},"citing_paper":{"arxiv_id":"2605.27176","last_updated":"2026-05-28T14:40:47Z","snapshot_observed_at":"2026-07-06T23:36:53.330986Z","submitted_at":"2026-05-26T15:29:41Z","title":"The Compressive Knowledge Graph Hypothesis: Which Graph Facts Matter for Scientific Hypothesis Generation?","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-29T16:59:50.113957Z"},"links":{"cited_paper":"/paper/2407.10805","citing_paper":"/paper/2605.27176"},"observation_digest":"sha256:720a920ee21583ac9938d864475e01d8b2fd0fae1eb0406145972c76036b2f0b","observation_id":"b8d36150-957b-403f-a48b-672b3056e843","resolution":{"observed_at":"2026-06-29T17:03:40.902323Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10805","last_updated":"2025-02-10T03:16:09Z","snapshot_observed_at":"2026-08-01T01:40:45.155448Z","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","version":7},"cited_work":{"arxiv_id":"2407.10805","doi":"10.48550/arxiv.2407.10805","metadata_source":"arxiv_reference","pith_arxiv_id":"2407.10805","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Think-on-graph 2.0: Deep and faithful large language model reasoning with knowledge- guided retrieval augmented generation.arXiv preprint arXiv:2407.10805","venue":"arXiv (Cornell University)","work_id":"7efc1432-6506-480c-a5c7-a38d6f45c7da","year":2025},"citing_paper":{"arxiv_id":"2606.18075","last_updated":"2026-06-16T15:44:10Z","snapshot_observed_at":"2026-07-06T23:53:36.096914Z","submitted_at":"2026-06-16T15:44:10Z","title":"A Unified Framework for Context-Aware and Relation-Aware Graph Retrieval-Augmented Generation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-27T01:17:21.685012Z"},"links":{"cited_paper":"/paper/2407.10805","citing_paper":"/paper/2606.18075"},"observation_digest":"sha256:c0dd6da269c51a9e826dfe71e4efde3bd0c8edfc515fefc55bedd21005f01da2","observation_id":"65901d65-e82f-4ba6-aa2e-5bdc6ccde633","resolution":{"observed_at":"2026-07-03T20:28:55.950083Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10805","last_updated":"2025-02-10T03:16:09Z","snapshot_observed_at":"2026-08-01T01:40:45.155448Z","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","version":7},"cited_work":{"arxiv_id":"2407.10805","doi":"10.48550/arxiv.2407.10805","metadata_source":"arxiv_reference","pith_arxiv_id":"2407.10805","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Think-on-graph 2.0: Deep and faithful large language model reasoning with knowledge- guided retrieval augmented generation.arXiv preprint arXiv:2407.10805","venue":"arXiv (Cornell University)","work_id":"7efc1432-6506-480c-a5c7-a38d6f45c7da","year":2025},"citing_paper":{"arxiv_id":"2606.22911","last_updated":"2026-06-22T06:51:42Z","snapshot_observed_at":"2026-08-06T14:21:29.598011Z","submitted_at":"2026-06-22T06:51:42Z","title":"ThermoLLM: Thermodynamics-Aware HVAC Control with Spatial-Semantic Knowledge Graph","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-26T08:37:59.374023Z"},"links":{"cited_paper":"/paper/2407.10805","citing_paper":"/paper/2606.22911"},"observation_digest":"sha256:5338edca1297e6c51d63a413c36a20aed37907e7da3891548936ff23f902a0f7","observation_id":"19b6cf2f-10c9-40d4-bd64-48117c3725fe","resolution":{"observed_at":"2026-06-26T08:39:14.798851Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10805","last_updated":"2025-02-10T03:16:09Z","snapshot_observed_at":"2026-08-01T01:40:45.155448Z","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","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.10805","snapshot_observed_at":"2026-08-01T04:03:36.205700Z","title":"Mallen, A.; Asai, A.; Zhong, V.; Das, R.; Khashabi, D.; and Hajishirzi,H.2023.WhenNottoTrustLanguageModels:In- vestigating Effectiveness of Parametric and Non-Parametric Memories","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.27652","last_updated":"2026-08-01T05:12:49Z","snapshot_observed_at":"2026-08-06T23:11:28.635235Z","submitted_at":"2026-07-30T04:05:05Z","title":"Harness-G: A Graph-Structured Harness for Search Agents","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-01T04:03:36.205700Z"},"links":{"cited_paper":"/paper/2407.10805","citing_paper":"/paper/2607.27652"},"observation_digest":"sha256:b7fa5f8940238dbe57b94dc4e066ba0c0ce411e9e8b0229c7361a7d4d69d79eb","observation_id":"c7dc75b1-67d0-4cd7-b547-4cc6dd5bf365","resolution":{"observed_at":"2026-08-01T04:03:36.205700Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10805","last_updated":"2025-02-10T03:16:09Z","snapshot_observed_at":"2026-08-01T01:40:45.155448Z","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","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.10805","snapshot_observed_at":"2026-08-04T01:23:36.225021Z","title":"Mallen, A.; Asai, A.; Zhong, V.; Das, R.; Khashabi, D.; and Hajishirzi,H.2023.WhenNottoTrustLanguageModels:In- vestigating Effectiveness of Parametric and Non-Parametric Memories","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.27652","last_updated":"2026-08-01T05:12:49Z","snapshot_observed_at":"2026-08-06T23:11:28.635235Z","submitted_at":"2026-07-30T04:05:05Z","title":"Harness-G: A Graph-Structured Harness for Search Agents","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T01:23:36.225021Z"},"links":{"cited_paper":"/paper/2407.10805","citing_paper":"/paper/2607.27652"},"observation_digest":"sha256:58a04186b845f381a4659135496e5617f192f93450d58e63986b750d03517c98","observation_id":"68703ce8-9701-41fe-94f6-15ac48102ef3","resolution":{"observed_at":"2026-08-04T01:23:36.225021Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2407.10805/citation-record","integrity":"/paper/2407.10805/integrity","json":"/paper/2407.10805/citation-record.json","paper":"/paper/2407.10805"},"outbound":[],"paper":{"arxiv_id":"2407.10805","last_updated":"2025-02-10T03:16:09Z","latest_version":7,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-01T01:40:45.155448Z","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"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 19 inbound Pith citation observations for arXiv:2407.10805."}