{"as_of":"2026-08-08T02:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7ea8bb7706c7b42663a14af4f078c30bc7b2a15dc20b5acfbacb13e9ff2d7b68","coverage":[{"denominator":45,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":45,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:03:09.738383Z","state":"measured"},{"denominator":52,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":52,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T00:42:58.195262Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-06-30T19:35:01.081417Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.08938","snapshot_observed_at":"2026-08-07T00:42:58.195262Z","title":"Faithfulrag: Fact-level conflict modeling for context-faithful retrieval-augmented generation","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.13178","last_updated":"2025-06-16T07:43:18Z","snapshot_observed_at":"2026-08-07T00:34:16.038100Z","submitted_at":"2025-06-16T07:43:18Z","title":"Enhancing Large Language Models with Reliable Knowledge Graphs","version":1},"reference_index":128,"source":"arxiv_source","source_observed_at":"2026-08-07T00:42:58.195262Z"},"links":{"cited_paper":"/paper/2506.08938","citing_paper":"/paper/2506.13178"},"observation_digest":"sha256:d2f26b64565a2d485ffa647daad6b4d6f517da719e1aef4470acef7638989573","observation_id":"e4821d02-5ef8-4c54-9b52-44e4ffca1e93","resolution":{"observed_at":"2026-08-07T00:42:58.195262Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"cited_work":{"arxiv_id":"2506.08938","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.08938","snapshot_observed_at":"2026-06-30T19:35:01.081417Z","title":"Faithfulrag: Fact-level conflict modeling for context-faithful retrieval-augmented generation","venue":null,"work_id":"682509da-c963-46ed-a016-f25b75f36516","year":2025},"citing_paper":{"arxiv_id":"2604.12138","last_updated":"2026-07-08T18:30:50Z","snapshot_observed_at":"2026-08-03T13:05:28.864307Z","submitted_at":"2026-04-13T23:39:39Z","title":"Retrieval-Augmented Generation Must Move Beyond Factual Grounding to Represent Diverse Opinions","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-10T15:00:05.173476Z"},"links":{"cited_paper":"/paper/2506.08938","citing_paper":"/paper/2604.12138"},"observation_digest":"sha256:9a2e458314d120510eab49d3d3ff37dedcb2788808193529faba40690e0b049d","observation_id":"3aad59c6-81d6-4804-98ec-36e3c1806bdc","resolution":{"observed_at":"2026-05-11T11:21:03.599779Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"cited_work":{"arxiv_id":"2506.08938","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.08938","snapshot_observed_at":"2026-06-30T19:35:01.081417Z","title":"Faithfulrag: Fact-level conflict modeling for context-faithful retrieval-augmented generation","venue":null,"work_id":"682509da-c963-46ed-a016-f25b75f36516","year":2025},"citing_paper":{"arxiv_id":"2605.17301","last_updated":"2026-06-08T13:42:42Z","snapshot_observed_at":"2026-07-06T23:28:21.395050Z","submitted_at":"2026-05-17T07:25:29Z","title":"ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-20T14:37:34.841972Z"},"links":{"cited_paper":"/paper/2506.08938","citing_paper":"/paper/2605.17301"},"observation_digest":"sha256:dc1ab31e705c1a6f4fe395d6c63f404123dfe09c2ef66de0a5751fc23d5c0a0e","observation_id":"23572414-8c59-4581-be18-576877032cda","resolution":{"observed_at":"2026-05-20T14:38:21.443840Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"cited_work":{"arxiv_id":"2506.08938","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.08938","snapshot_observed_at":"2026-06-30T19:35:01.081417Z","title":"Faithfulrag: Fact-level conflict modeling for context-faithful retrieval-augmented generation","venue":null,"work_id":"682509da-c963-46ed-a016-f25b75f36516","year":2025},"citing_paper":{"arxiv_id":"2605.17301","last_updated":"2026-06-08T13:42:42Z","snapshot_observed_at":"2026-07-06T23:28:21.395050Z","submitted_at":"2026-05-17T07:25:29Z","title":"ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-30T19:32:49.894007Z"},"links":{"cited_paper":"/paper/2506.08938","citing_paper":"/paper/2605.17301"},"observation_digest":"sha256:2456223313289aa9c9230ada63ac409a5957a8993fd3ffa7d28bb5719ce259ef","observation_id":"89c1e9f5-4f0c-4add-bbc2-7935298e8471","resolution":{"observed_at":"2026-06-30T19:35:01.082898Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"cited_work":{"arxiv_id":"2506.08938","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.08938","snapshot_observed_at":"2026-06-30T19:35:01.081417Z","title":"Faithfulrag: Fact-level conflict modeling for context-faithful retrieval-augmented generation","venue":null,"work_id":"682509da-c963-46ed-a016-f25b75f36516","year":2025},"citing_paper":{"arxiv_id":"2605.18792","last_updated":"2026-05-11T05:44:27Z","snapshot_observed_at":"2026-08-01T20:16:08.484322Z","submitted_at":"2026-05-11T05:44:27Z","title":"Trust or Abstain? A Self-Aware RAG Approach","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-20T23:02:58.671115Z"},"links":{"cited_paper":"/paper/2506.08938","citing_paper":"/paper/2605.18792"},"observation_digest":"sha256:da6e91889b07fe98b4d24569c69856e334487279276af2533be57fc80c904953","observation_id":"adc400e4-b42b-45cd-8598-a82e5a65765e","resolution":{"observed_at":"2026-05-20T23:03:50.443216Z","resolver_source":"arxiv_id","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"cited_work":{"arxiv_id":"2506.08938","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.08938","snapshot_observed_at":"2026-06-30T19:35:01.081417Z","title":"Faithfulrag: Fact-level conflict modeling for context-faithful retrieval-augmented generation","venue":null,"work_id":"682509da-c963-46ed-a016-f25b75f36516","year":2025},"citing_paper":{"arxiv_id":"2606.00610","last_updated":"2026-05-30T08:18:53Z","snapshot_observed_at":"2026-08-05T23:59:52.456196Z","submitted_at":"2026-05-30T08:18:53Z","title":"MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-06-28T18:20:43.092561Z"},"links":{"cited_paper":"/paper/2506.08938","citing_paper":"/paper/2606.00610"},"observation_digest":"sha256:1ff75f7d6493b427cc16fbcd2b6fabbb82b3f811728f1c92e763472003930835","observation_id":"1eb1b92e-68bb-43b1-9054-31850e475690","resolution":{"observed_at":"2026-06-28T20:42:37.937848Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.08938","snapshot_observed_at":"2026-08-01T04:48:29.303043Z","title":"arXiv:2506.08938","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22448","last_updated":"2026-07-27T23:08:28Z","snapshot_observed_at":"2026-08-07T08:03:30.671396Z","submitted_at":"2026-07-24T16:10:21Z","title":"Where Facts Go Missing: A Layerwise Taxonomy and Per-Layer Attribution of Information Omission in Air-Gapped LLMAgent Pipelines","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-01T04:48:29.303043Z"},"links":{"cited_paper":"/paper/2506.08938","citing_paper":"/paper/2607.22448"},"observation_digest":"sha256:99dbfd663cb3f81b6e2e5732543631d76748ac78be873ffc6abf70da899eeaee","observation_id":"9d987d81-ae4f-4513-b2c1-b9af990ec56f","resolution":{"observed_at":"2026-08-01T04:48:29.303043Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2506.08938/citation-record","integrity":"/paper/2506.08938/integrity","json":"/paper/2506.08938/citation-record.json","paper":"/paper/2506.08938"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:03:06.124274Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:06.124274Z"},"links":{"citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:006e2c6c3ea644e9ba50df5b95fa16eef1cf36f54f4bf889b223d8c8432433f6","observation_id":"28fcee2d-53a9-46c7-82c5-4ab87a76b489","resolution":{"observed_at":"2026-08-07T05:03:06.124274Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.11511","last_updated":"2023-10-17T18:18:32Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-10-17T18:18:32Z","title":"Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.11511","snapshot_observed_at":"2026-08-07T05:03:06.215442Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:06.215442Z"},"links":{"cited_paper":"/paper/2310.11511","citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:6b59ae13a35606f5fade25990a6e7be4d3a3e53c56e932a2572555306bd862dc","observation_id":"ae9872cf-6a06-4926-ba7e-ac0b42f05f4f","resolution":{"observed_at":"2026-08-07T05:03:06.215442Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15280","last_updated":"2024-12-18T04:08:18Z","snapshot_observed_at":"2026-07-06T20:10:29.099784Z","submitted_at":"2024-12-18T04:08:18Z","title":"Context-DPO: Aligning Language Models for Context-Faithfulness","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.15280","snapshot_observed_at":"2026-08-07T05:03:06.306782Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:06.306782Z"},"links":{"cited_paper":"/paper/2412.15280","citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:8c3abefef5490e6c3d3e9eb06cc6197f6a0765c42d927a4b1089a5b16a570497","observation_id":"640f9d77-5fa3-455f-a03f-f65668e4ed23","resolution":{"observed_at":"2026-08-07T05:03:06.306782Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.00216","last_updated":"2024-10-04T03:30:24Z","snapshot_observed_at":"2026-07-06T17:53:19.196215Z","submitted_at":"2024-03-30T02:08:28Z","title":"Is Factuality Enhancement a Free Lunch For LLMs? Better Factuality Can Lead to Worse Context-Faithfulness","version":2},"cited_work":{"arxiv_id":"2404.00216","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.00216","snapshot_observed_at":"2026-08-07T05:03:10.473543Z","title":"Is Factuality Enhancement a Free Lunch For LLMs? Better Factuality Can Lead to Worse Context-Faithfulness","venue":"cs.CL","work_id":"07a419a1-5cd6-409a-8dcb-db156e8d60da","year":2024},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:06.374192Z"},"links":{"cited_paper":"/paper/2404.00216","citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:66bb60174cdbb45e3d8b68822ebd8eb50a859bd19211f967844ba7d556f32c76","observation_id":"89992a86-6766-4392-9f93-db6ce502dcd3","resolution":{"observed_at":"2026-08-07T05:03:10.547043Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.02376","last_updated":"2024-06-17T15:02:11Z","snapshot_observed_at":"2026-07-06T18:25:14.248267Z","submitted_at":"2024-06-04T14:53:24Z","title":"Retaining Key Information under High Compression Ratios: Query-Guided Compressor for LLMs","version":2},"cited_work":{"arxiv_id":"2406.02376","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.02376","snapshot_observed_at":"2026-08-07T05:03:10.302291Z","title":"Retaining Key Information under High Compression Ratios: Query-Guided Compressor for LLMs","venue":"cs.CL","work_id":"2413689b-5e44-4f3b-8a3e-c1b8852fb04e","year":2024},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:06.477944Z"},"links":{"cited_paper":"/paper/2406.02376","citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:3c09dca4e99c243706fed5060671c3eac374a15e5229273c524cf6421fc4289e","observation_id":"128cb500-4d01-4464-9dcf-78eb7459dd9c","resolution":{"observed_at":"2026-08-07T05:03:10.388698Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.16806","last_updated":"2024-05-28T07:50:18Z","snapshot_observed_at":"2026-08-01T05:15:20.689130Z","submitted_at":"2024-05-27T03:52:55Z","title":"Entity Alignment with Noisy Annotations from Large Language Models","version":2},"cited_work":{"arxiv_id":"2405.16806","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.16806","snapshot_observed_at":"2026-08-07T05:03:10.152648Z","title":"Entity Alignment with Noisy Annotations from Large Language Models","venue":"cs.CL","work_id":"6f6d73f6-59dd-4ac9-8a57-1846927e0e0b","year":2024},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:06.581507Z"},"links":{"cited_paper":"/paper/2405.16806","citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:0cb1de40aa8a0db45e66bacce391d2f0f8aedcd0f8e7dabc301a23b93e9361e5","observation_id":"950ad6ff-8c4e-4e8a-926e-883b7287335a","resolution":{"observed_at":"2026-08-07T05:03:10.206516Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:03:12.632385Z","title":null,"venue":null,"work_id":"a40f28b2-cdca-4563-b973-fe217139f9e3","year":2025},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:06.674417Z"},"links":{"citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:ecaa730a6897b474f0f434197cd09f9d3c71dc90bb4b747e6033fdc363238991","observation_id":"9524a518-9fb2-4064-b40b-71e2f214ead6","resolution":{"observed_at":"2026-08-07T05:03:12.712886Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.10997","last_updated":"2024-03-27T09:16:57Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-12-18T07:47:33Z","title":"Retrieval-Augmented Generation for Large Language Models: A Survey","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.10997","snapshot_observed_at":"2026-08-07T05:03:06.741662Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:06.741662Z"},"links":{"cited_paper":"/paper/2312.10997","citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:896a7833e5523891b39f718727d2ae6e39693afcaa16b63f2e57a749f9a68fa3","observation_id":"4139aac9-4005-4048-b99d-abb7bf643815","resolution":{"observed_at":"2026-08-07T05:03:06.741662Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.11517","last_updated":"2024-06-06T13:56:59Z","snapshot_observed_at":"2026-08-02T07:12:04.104343Z","submitted_at":"2024-02-18T09:10:04Z","title":"Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLM","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.11517","snapshot_observed_at":"2026-08-07T05:03:06.814929Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:06.814929Z"},"links":{"cited_paper":"/paper/2402.11517","citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:d413c39e9ff9a1b82e6c4f7dcdc241d91a4a607e1eca322d51e0cd7e59ff5668","observation_id":"1a9c42a5-3d5a-49ce-ab1e-e078c828db8b","resolution":{"observed_at":"2026-08-07T05:03:06.814929Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.16008","last_updated":"2024-07-03T17:40:00Z","snapshot_observed_at":"2026-08-06T05:58:48.181378Z","submitted_at":"2024-06-23T04:35:42Z","title":"Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.16008","snapshot_observed_at":"2026-08-07T05:03:06.878537Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:06.878537Z"},"links":{"cited_paper":"/paper/2406.16008","citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:80cb85170b193b8efcf409042c540395e6194e954cb50f6cab3a3d470b3895a0","observation_id":"eac51094-3220-4400-93b1-6146dc042c35","resolution":{"observed_at":"2026-08-07T05:03:06.878537Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.05232","last_updated":"2024-11-19T12:42:45Z","snapshot_observed_at":"2026-07-06T16:45:07.733095Z","submitted_at":"2023-11-09T09:25:37Z","title":"A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.05232","snapshot_observed_at":"2026-08-07T05:03:06.950820Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:06.950820Z"},"links":{"cited_paper":"/paper/2311.05232","citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:5984f250e4a8f5c4c98a5c4d5eee4ab9337006727322494f8cf6097dc675f741","observation_id":"a57e697d-89c9-4cc4-af42-1628a4f07b21","resolution":{"observed_at":"2026-08-07T05:03:06.950820Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:03:12.432128Z","title":null,"venue":null,"work_id":"47f30884-3c08-4d48-b7a8-61fd9c98f364","year":2025},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:07.045579Z"},"links":{"citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:c68202177758b0a5cb893d7103a84710919e4ae1b375050a6d339c03fdc3ca7c","observation_id":"683312f8-7df0-4350-85d7-8ebdd0a3c199","resolution":{"observed_at":"2026-08-07T05:03:12.538212Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:03:07.126030Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:07.126030Z"},"links":{"citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:ff8abf09f2918c43780986d0c127ba014233b5dced821909453aa63dc4e9d8c2","observation_id":"377c87e3-cd83-4f80-a301-1cba76eeef3b","resolution":{"observed_at":"2026-08-07T05:03:07.126030Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:03:07.218355Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:07.218355Z"},"links":{"citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:c423d2e81769aaa1af12139d63bfc898b7525ce4b278a3e9019d17cb00807375","observation_id":"57ef0a9f-7953-4f48-9fa5-1750ac4e751d","resolution":{"observed_at":"2026-08-07T05:03:07.218355Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:03:07.289656Z","title":"u ttler, Mike Lewis, Wen-tau Yih, Tim Rockt\\","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:07.289656Z"},"links":{"citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:c6064a43c021b505b8724e85a7ac438d2eacfa2f10913f4508e88fc0254b5685","observation_id":"2855405b-62ff-44d3-8239-29cf385606a2","resolution":{"observed_at":"2026-08-07T05:03:07.289656Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:03:07.359848Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:07.359848Z"},"links":{"citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:5707bf93c0865f6b44581e1492263f12e2b9cefa4902b1eabac9876f7571c9c3","observation_id":"2a4b54ba-05ef-4123-967e-60b5ae480a64","resolution":{"observed_at":"2026-08-07T05:03:07.359848Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.19437","last_updated":"2025-02-18T17:26:38Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-27T04:03:16Z","title":"DeepSeek-V3 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.19437","snapshot_observed_at":"2026-08-07T05:03:07.446274Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:07.446274Z"},"links":{"cited_paper":"/paper/2412.19437","citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:c9cbf7293d025e5e3904ba894ba24aa6f476e5cea5f45c3776bd5aa2b2566556","observation_id":"3d130e15-3c34-4bc9-aeb9-2ef207e6eef8","resolution":{"observed_at":"2026-08-07T05:03:07.446274Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.01714","last_updated":"2024-03-03T06:12:44Z","snapshot_observed_at":"2026-08-01T10:11:20.453560Z","submitted_at":"2023-12-04T08:07:21Z","title":"Retrieval-augmented Multi-modal Chain-of-Thoughts Reasoning for Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.01714","snapshot_observed_at":"2026-08-07T05:03:07.538573Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:07.538573Z"},"links":{"cited_paper":"/paper/2312.01714","citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:a7fd68e3940ea9b5ceef562229a7f52ec0bcd23bc2424fd5438cdcd178b92b21","observation_id":"e72f84bc-cb93-4ca5-b5db-ee7504516b8c","resolution":{"observed_at":"2026-08-07T05:03:07.538573Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:03:07.629003Z","title":"Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:07.629003Z"},"links":{"citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:b08bec839e97cc9f1d93bdf5b4e131f0ce3a67d3fb9b03b95da4ec7f62f9fbbb","observation_id":"d9eb7df5-524a-45a3-b0ff-828080ec8f0d","resolution":{"observed_at":"2026-08-07T05:03:07.629003Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:03:12.221574Z","title":null,"venue":null,"work_id":"eaad7587-818e-4d01-8672-9a8aa7d964f4","year":2024},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:07.707328Z"},"links":{"citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:608e12cdfc46626c4ecbe4e344f393e3719b70cfa6ab718b6bf437c889a5490c","observation_id":"4366bbf8-96be-495f-abaf-cb46befca159","resolution":{"observed_at":"2026-08-07T05:03:12.315428Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:03:07.799701Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:07.799701Z"},"links":{"citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:6ab77beb7ab5d9ec12de9d7163f4cc0da867e700be46d283d14f7f0a8ea40aa5","observation_id":"dc19ea4f-79e2-47d9-8768-3a16e30abccc","resolution":{"observed_at":"2026-08-07T05:03:07.799701Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:03:11.998064Z","title":null,"venue":null,"work_id":"05d4ead7-ec0f-4042-9145-2e65d1649dba","year":2025},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:07.879316Z"},"links":{"citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:6b765d8d78cb069779ab1087abecece80488526374608f5b9b4a48a81c23144e","observation_id":"34eabee0-a6b9-4b61-a5c6-3245b734a0a6","resolution":{"observed_at":"2026-08-07T05:03:12.127691Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.09916","last_updated":"2024-09-16T01:08:18Z","snapshot_observed_at":"2026-08-06T17:52:04.103266Z","submitted_at":"2024-09-16T01:08:18Z","title":"SFR-RAG: Towards Contextually Faithful LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.09916","snapshot_observed_at":"2026-08-07T05:03:07.952032Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:07.952032Z"},"links":{"cited_paper":"/paper/2409.09916","citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:ae1dcad69f3516010b4595f29bf8d6877616f9d10e04480925c0fc023ae7e951","observation_id":"7a741862-3141-49d5-b3f4-6ca5d282b460","resolution":{"observed_at":"2026-08-07T05:03:07.952032Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-07T05:03:08.050693Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:08.050693Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:6a3424f44ab8c820c9ddf9e563089e2c9c98f09fc417335027d6542d8053e55c","observation_id":"fd2bdd55-8b3c-4b6a-a247-b5987ab928fe","resolution":{"observed_at":"2026-08-07T05:03:08.050693Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1606.05250","last_updated":"2016-10-11T02:42:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2016-06-16T16:36:00Z","title":"SQuAD: 100,000+ Questions for Machine Comprehension of Text","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.05250","snapshot_observed_at":"2026-08-07T05:03:08.121580Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:08.121580Z"},"links":{"cited_paper":"/paper/1606.05250","citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:7486f71bda64327d91bcf97673c558935f1f483ce8573160c52ee9918ce131d2","observation_id":"4f7c59b1-a0a2-4dfa-a06e-e51a34ccc30b","resolution":{"observed_at":"2026-08-07T05:03:08.121580Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.14739","last_updated":"2023-05-24T05:19:15Z","snapshot_observed_at":"2026-07-06T15:32:14.701994Z","submitted_at":"2023-05-24T05:19:15Z","title":"Trusting Your Evidence: Hallucinate Less with Context-aware Decoding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.14739","snapshot_observed_at":"2026-08-07T05:03:08.197207Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:08.197207Z"},"links":{"cited_paper":"/paper/2305.14739","citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:5c2e4d52e0772ba9c4ba4fb6ce87bb1bb1842fd02e8e8e726242195666072a23","observation_id":"72c0a2f0-c055-4162-a72a-5480986b88fd","resolution":{"observed_at":"2026-08-07T05:03:08.197207Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:03:11.846829Z","title":null,"venue":null,"work_id":"74e6cf51-174d-4972-88ce-747146d6e126","year":2024},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:08.289858Z"},"links":{"citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:d19ff85b715bb5ac20e77822ff570c7157ad17ab77f0c873f6db50381a6672fa","observation_id":"b841e0ae-8f27-4252-af98-a001d1215817","resolution":{"observed_at":"2026-08-07T05:03:11.910512Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.00573","last_updated":"2022-05-05T05:50:50Z","snapshot_observed_at":"2026-08-05T14:23:49.315145Z","submitted_at":"2021-08-02T00:33:27Z","title":"MuSiQue: Multihop Questions via Single-hop Question Composition","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.00573","snapshot_observed_at":"2026-08-07T05:03:08.367162Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:08.367162Z"},"links":{"cited_paper":"/paper/2108.00573","citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:8a9ca70ffd0f785c17f63c4a5cfeeb5cbe97096946b751851499f9010f43c983","observation_id":"e82eceef-a9e9-41a1-8d12-048c20271949","resolution":{"observed_at":"2026-08-07T05:03:08.367162Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:03:11.646979Z","title":null,"venue":null,"work_id":"f7e0fb4f-1049-4b55-8f7c-e690c44a8186","year":2024},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:08.432444Z"},"links":{"citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:21b8a355e1cb9c24f792468e45a861805896f2dcda741a99a1277196ad773ca9","observation_id":"54f736ab-1f71-44a7-b984-7cbe67008369","resolution":{"observed_at":"2026-08-07T05:03:11.758721Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:03:11.463930Z","title":null,"venue":null,"work_id":"ce70c42a-b0dd-49de-acdc-154bd621b4cf","year":2024},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:08.474834Z"},"links":{"citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:ed59965ff1866de3ed02b2ceb615284d5c4eb9593fc201156684502b2af60bc6","observation_id":"f5925ea4-4b92-444d-a719-997497dbe089","resolution":{"observed_at":"2026-08-07T05:03:11.579532Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:03:08.550152Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:08.550152Z"},"links":{"citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:f2a77eea5ea316997173374c6079ffd1470c3262d3c51445e8ac10badd51ddc2","observation_id":"2a24c81e-1fb3-4aca-b2e0-13484d36e405","resolution":{"observed_at":"2026-08-07T05:03:08.550152Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.14482","last_updated":"2025-02-14T20:58:41Z","snapshot_observed_at":"2026-07-06T18:49:10.964379Z","submitted_at":"2024-07-19T17:35:47Z","title":"ChatQA 2: Bridging the Gap to Proprietary LLMs in Long Context and RAG Capabilities","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.14482","snapshot_observed_at":"2026-08-07T05:03:08.617273Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:08.617273Z"},"links":{"cited_paper":"/paper/2407.14482","citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:215cee82f36a9220a6257122904709680f493de5fa41c90bb74feba9b6fe93ec","observation_id":"8428222d-5ac8-4473-88d2-be6670b00e9e","resolution":{"observed_at":"2026-08-07T05:03:08.617273Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:03:08.686106Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:08.686106Z"},"links":{"citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:3720ed497155c0b69ce3d45771a1d4ae78b7b8dea4a5337b7ece84d49615e9ae","observation_id":"339649b8-e04d-49b8-a532-33080382f805","resolution":{"observed_at":"2026-08-07T05:03:08.686106Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:03:08.760568Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:08.760568Z"},"links":{"citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:0bc1a90210ad3271e83f1149d18792d6cf0bf4cd6619b2fb9ba7f7e8bdc85487","observation_id":"f8879c64-4742-46dd-bd60-23893f605165","resolution":{"observed_at":"2026-08-07T05:03:08.760568Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:03:11.277495Z","title":null,"venue":null,"work_id":"5c579d07-a636-4666-b9a0-e508b72107c1","year":2023},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:08.831117Z"},"links":{"citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:35e79f2c1e084b9956c2f6fe4688ceb15b2ff63b756b8a25f18b88d79b7adc1c","observation_id":"436aa401-a8ae-4d44-b7f3-47f7c1496a59","resolution":{"observed_at":"2026-08-07T05:03:11.362221Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:03:08.836374Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:08.836374Z"},"links":{"citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:ca9895206d3a415fd575c139a84c98fb42870160746f0ed3f63ee2782566d8c2","observation_id":"2e0eecf2-052e-4c59-a685-42ad8b00d1f1","resolution":{"observed_at":"2026-08-07T05:03:08.836374Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.12911","last_updated":"2026-04-22T11:59:20Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-02-18T14:53:45Z","title":"Knapsack Optimization-based Schema Linking for LLM-based Text-to-SQL Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.12911","snapshot_observed_at":"2026-08-07T05:03:08.869985Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:08.869985Z"},"links":{"cited_paper":"/paper/2502.12911","citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:d2ccb969b54d81f7ed899fb265e825fba45bade311ead1db5439dba568d3c801","observation_id":"b7eb3014-948f-4ddd-b5ab-b9c781854b9e","resolution":{"observed_at":"2026-08-07T05:03:08.869985Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:03:08.996494Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:08.996494Z"},"links":{"citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:7da8de67f070fd696034cba2badd3a5f8f5bdf794037455566ca349e0d2dcf71","observation_id":"a73de233-cf92-47ce-944c-84d93bfc078f","resolution":{"observed_at":"2026-08-07T05:03:08.996494Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:03:11.099704Z","title":null,"venue":null,"work_id":"5486cd55-12a7-4c99-a56a-809906bc7eb6","year":2024},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:09.153631Z"},"links":{"citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:a69c19343606705c908696079d1fbe22bc8861ce0ab1799b266204c62d6cfdcc","observation_id":"706647b4-9bed-40a1-a78f-5203aab4ef20","resolution":{"observed_at":"2026-08-07T05:03:11.202720Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:03:09.260708Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:09.260708Z"},"links":{"citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:06664d0bb79479a1b7f9841024aa2a9f3d95f80cbdcb4bcbe4ae4f1f9c884603","observation_id":"a49709df-dae7-4989-a5dc-57537f9b643e","resolution":{"observed_at":"2026-08-07T05:03:09.260708Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:03:10.877017Z","title":null,"venue":null,"work_id":"4eb50f4d-0135-469f-890d-fb678f3904bb","year":2025},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:09.419046Z"},"links":{"citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:817a799ca50ed23db5d943c967d1365188e68d30302978026f246d541134a83d","observation_id":"f53d92bd-f912-44d2-81a7-6db7f2786381","resolution":{"observed_at":"2026-08-07T05:03:10.948970Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:03:10.669757Z","title":null,"venue":null,"work_id":"4f45e89d-466a-43dc-8694-90cdc096eac9","year":2024},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:09.515952Z"},"links":{"citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:c8c44b9f950f4848b028c5221467a53d6353307076fbfa911dcc5880d319cc93","observation_id":"18becbcf-8edd-410e-8712-8bd48e830b5c","resolution":{"observed_at":"2026-08-07T05:03:10.762882Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:03:09.599040Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:09.599040Z"},"links":{"citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:5aa870d2020f33aef469fa55efce25251bbc49e6939cc9e6cf466770df20b0e5","observation_id":"ac6a868f-c9db-458f-a3d4-f9947478c650","resolution":{"observed_at":"2026-08-07T05:03:09.599040Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:03:09.679172Z","title":"online\" 'onlinestring :=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:09.679172Z"},"links":{"citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:016605dca361b2bd67777e161b2d1fb9b0371563476857761ad4fe155419a91e","observation_id":"b995c809-ba3a-4615-b995-5b69dee0f53f","resolution":{"observed_at":"2026-08-07T05:03:09.679172Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:03:09.738383Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation","version":2},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-07T05:03:09.738383Z"},"links":{"citing_paper":"/paper/2506.08938"},"observation_digest":"sha256:f879b1d2d56042bed2a3c1cb8bd36ba632f049ceadd93dac005d00d7323bafb5","observation_id":"8565dc86-26ae-4b4a-aaec-2b54c64699f2","resolution":{"observed_at":"2026-08-07T05:03:09.738383Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.08938","last_updated":"2025-07-08T08:59:27Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-07T04:56:22.646316Z","submitted_at":"2025-06-10T16:02:54Z","title":"FaithfulRAG: Fact-Level Conflict Modeling for Context-Faithful Retrieval-Augmented Generation"},"reference_resolution":{"displayed":45,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":42,"verified_exact":3,"verified_fuzzy":0},"total_outbound_references":45},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 7 inbound Pith citation observations for arXiv:2506.08938."}