{"as_of":"2026-08-13T18:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:360c1a5746f237b77ea31401db4b8e74fe4b6660ab19e02200b8087d354eda54","coverage":[{"denominator":35,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T19:24:53.797281Z","state":"measured"},{"denominator":40,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":40,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T14:47:45.660904Z","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":"2026-07-03T03:37:35.708363Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.05714","snapshot_observed_at":"2026-08-06T14:47:45.660904Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.22927","last_updated":"2025-07-23T16:14:08Z","snapshot_observed_at":"2026-08-10T00:56:09.586936Z","submitted_at":"2025-07-23T16:14:08Z","title":"PRGB Benchmark: A Robust Placeholder-Assisted Algorithm for Benchmarking Retrieval-Augmented Generation","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-06T14:47:45.660904Z"},"links":{"cited_paper":"/paper/2507.05714","citing_paper":"/paper/2507.22927"},"observation_digest":"sha256:26791a62cd13aae002f1bdb5332aef45077ce964fe160367c0babb160618b2fe","observation_id":"5410abcd-323d-4eda-9299-0e1f976fb587","resolution":{"observed_at":"2026-08-06T14:47:45.660904Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.05714","snapshot_observed_at":"2026-08-05T13:36:37.428094Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.00449","last_updated":"2025-08-30T10:44:06Z","snapshot_observed_at":"2026-08-10T03:42:52.576423Z","submitted_at":"2025-08-30T10:44:06Z","title":"GOSU: Retrieval-Augmented Generation with Global-Level Optimized Semantic Unit-Centric Framework","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-05T13:36:37.428094Z"},"links":{"cited_paper":"/paper/2507.05714","citing_paper":"/paper/2509.00449"},"observation_digest":"sha256:bed0229b59d512acef48160d5111bd5dfb712a89f383636835664a987fe80bd1","observation_id":"03cc28d5-51a9-45a6-a2b9-70c56cd2a168","resolution":{"observed_at":"2026-08-05T13:36:37.428094Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"cited_work":{"arxiv_id":"2507.05714","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.05714","snapshot_observed_at":"2026-07-03T03:37:35.708363Z","title":"Hirag: Hierarchical-thought instruction-tuning retrieval-augmented generation","venue":null,"work_id":"1f2eb904-8b9d-4e0e-8ae9-00480ea4c489","year":2025},"citing_paper":{"arxiv_id":"2511.11653","last_updated":"2026-04-30T09:50:26Z","snapshot_observed_at":"2026-08-12T21:25:11.944026Z","submitted_at":"2025-11-10T15:25:31Z","title":"GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-17T23:41:47.052697Z"},"links":{"cited_paper":"/paper/2507.05714","citing_paper":"/paper/2511.11653"},"observation_digest":"sha256:ab097279da4f75049ccb29338cd1e9c22bea66fedb459e199dbfe2edf865f960","observation_id":"b134669c-86b4-4150-abd2-128c5497525b","resolution":{"observed_at":"2026-05-17T23:42:13.034968Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"cited_work":{"arxiv_id":"2507.05714","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.05714","snapshot_observed_at":"2026-07-03T03:37:35.708363Z","title":"Hirag: Hierarchical-thought instruction-tuning retrieval-augmented generation","venue":null,"work_id":"1f2eb904-8b9d-4e0e-8ae9-00480ea4c489","year":2025},"citing_paper":{"arxiv_id":"2606.08979","last_updated":"2026-06-08T03:25:20Z","snapshot_observed_at":"2026-07-06T23:48:26.076260Z","submitted_at":"2026-06-08T03:25:20Z","title":"EviProp: Seeded Relevance Diffusion on Chunk-Page Graphs for Long Multimodal Document Retrieval","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-06-27T15:04:30.757297Z"},"links":{"cited_paper":"/paper/2507.05714","citing_paper":"/paper/2606.08979"},"observation_digest":"sha256:ccc70eff7804c0423ebff649a59de629851f9931f3dae1052240cd39125aff22","observation_id":"6a3a2c9d-a5ce-4e5c-8e1b-876e0b73d67a","resolution":{"observed_at":"2026-07-03T03:37:35.709790Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"cited_work":{"arxiv_id":"2507.05714","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.05714","snapshot_observed_at":"2026-07-03T03:37:35.708363Z","title":"Hirag: Hierarchical-thought instruction-tuning retrieval-augmented generation","venue":null,"work_id":"1f2eb904-8b9d-4e0e-8ae9-00480ea4c489","year":2025},"citing_paper":{"arxiv_id":"2606.28447","last_updated":"2026-06-26T08:40:08Z","snapshot_observed_at":"2026-08-13T14:54:44.080618Z","submitted_at":"2026-06-26T08:40:08Z","title":"SemFlowRAG: Directed Semantic Flow from Abstraction to Evidence for Complex Reasoning","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-06-30T01:31:23.993255Z"},"links":{"cited_paper":"/paper/2507.05714","citing_paper":"/paper/2606.28447"},"observation_digest":"sha256:7edb57b4d726a314dd1e8105743490cbe4f1d5252b576f51941b0baeb1216d53","observation_id":"8fbe1941-3538-44bb-bd79-53e16ecdb974","resolution":{"observed_at":"2026-07-01T15:25:48.121831Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2507.05714/citation-record","integrity":"/paper/2507.05714/integrity","json":"/paper/2507.05714/citation-record.json","paper":"/paper/2507.05714"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T19:24:50.104228Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:50.104228Z"},"links":{"citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:e5d33f6dada7587fe90434cdbda1d06c4e859a41770883c36b1c057f2dea6d40","observation_id":"b285bd9e-ecb5-4cfd-ae79-8ed53d6b22c3","resolution":{"observed_at":"2026-08-06T19:24:50.104228Z","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":"2026-08-12T20:38:07.563701Z","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":"2026-08-06T19:24:50.175682Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:50.175682Z"},"links":{"cited_paper":"/paper/2310.11511","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:62a0172a519fd3ecba70c0f5d1a79c9072baf7f38258122e9647a0d09fd3a878","observation_id":"a5c7e7ee-67ee-40e0-9bf8-5bd9875c497c","resolution":{"observed_at":"2026-08-06T19:24:50.175682Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.00610","last_updated":"2024-03-31T08:58:54Z","snapshot_observed_at":"2026-08-13T00:40:40.288248Z","submitted_at":"2024-03-31T08:58:54Z","title":"RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.00610","snapshot_observed_at":"2026-08-06T19:24:50.280623Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:50.280623Z"},"links":{"cited_paper":"/paper/2404.00610","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:7db88c2e8cce60cc89a787e043442a496b7537ff2fa9aafc9ba70accd03af7fd","observation_id":"b1325591-250e-4530-b20e-607827cc177b","resolution":{"observed_at":"2026-08-06T19:24:50.280623Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.01431","last_updated":"2023-12-20T11:54:11Z","snapshot_observed_at":"2026-08-13T10:20:59.304484Z","submitted_at":"2023-09-04T08:28:44Z","title":"Benchmarking Large Language Models in Retrieval-Augmented Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.01431","snapshot_observed_at":"2026-08-06T19:24:50.354323Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:50.354323Z"},"links":{"cited_paper":"/paper/2309.01431","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:d8e7fe12199edd32201d6ff040e24e7f0d047585bd230b6c92e07b52e8e7ca56","observation_id":"b07cf664-b701-4a39-a7aa-72b453789ac8","resolution":{"observed_at":"2026-08-06T19:24:50.354323Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19893","last_updated":"2024-05-30T09:50:38Z","snapshot_observed_at":"2026-08-12T23:54:21.420186Z","submitted_at":"2024-05-30T09:50:38Z","title":"Similarity is Not All You Need: Endowing Retrieval Augmented Generation with Multi Layered Thoughts","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.19893","snapshot_observed_at":"2026-08-06T19:24:50.451748Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:50.451748Z"},"links":{"cited_paper":"/paper/2405.19893","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:cdca2b7d0500c657b2abc9c3d0771846c7f3865274b9e069d62ddaab3bfc3bd0","observation_id":"d6cebeaa-9a2e-4989-8c0d-e806c60cb19d","resolution":{"observed_at":"2026-08-06T19:24:50.451748Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-06T19:24:50.534280Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:50.534280Z"},"links":{"cited_paper":"/paper/2312.10997","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:787d8742888e704c59ec049e5975063102e7a7ad15fb00de85419b8f191d7399","observation_id":"d37c01da-dbcd-4b92-bc8f-ac116584881e","resolution":{"observed_at":"2026-08-06T19:24:50.534280Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.06300","last_updated":"2022-07-13T15:51:40Z","snapshot_observed_at":"2026-08-13T15:04:50.124618Z","submitted_at":"2022-07-13T15:51:40Z","title":"Re2G: Retrieve, Rerank, Generate","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.06300","snapshot_observed_at":"2026-08-06T19:24:50.608052Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:50.608052Z"},"links":{"cited_paper":"/paper/2207.06300","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:3581e58cb20c8ba7a1b38a705626ba91ea6beec8d03bb36cd750cc2d6a6e18f3","observation_id":"309fb8df-4ce8-48bf-b4e7-981d0b64a3f5","resolution":{"observed_at":"2026-08-06T19:24:50.608052Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-08-13T17:20:44.002518Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-08-06T19:24:50.687299Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:50.687299Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:f378a34dd70a21b6a8772b8f2a9dd3825646c4efadc5c25268c16fc07a8d367f","observation_id":"f4a896c2-f4d9-4344-88b9-9813e301bf22","resolution":{"observed_at":"2026-08-06T19:24:50.687299Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.08909","last_updated":"2020-02-10T18:40:59Z","snapshot_observed_at":"2026-08-02T17:52:27.326803Z","submitted_at":"2020-02-10T18:40:59Z","title":"REALM: Retrieval-Augmented Language Model Pre-Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.08909","snapshot_observed_at":"2026-08-06T19:24:50.762672Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:50.762672Z"},"links":{"cited_paper":"/paper/2002.08909","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:633a629e30d8ba3111f7c13ba5d9c6a45297c6fb1c6307f4b5b95be54d0a2f1c","observation_id":"ebfb0b45-304b-4ffd-a170-aba67ea014cc","resolution":{"observed_at":"2026-08-06T19:24:50.762672Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.09118","last_updated":"2022-08-29T12:17:32Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2021-12-16T18:57:37Z","title":"Unsupervised Dense Information Retrieval with Contrastive Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.09118","snapshot_observed_at":"2026-08-06T19:24:50.862128Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:50.862128Z"},"links":{"cited_paper":"/paper/2112.09118","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:66c5662b8be0842dd4a9bf172f524e4a1d96dfeacf7945809a2b9160ddd959ad","observation_id":"e84d8c5a-315c-4216-9660-6d8ee4430295","resolution":{"observed_at":"2026-08-06T19:24:50.862128Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.14403","last_updated":"2024-03-28T06:45:11Z","snapshot_observed_at":"2026-08-13T00:48:40.543570Z","submitted_at":"2024-03-21T13:52:30Z","title":"Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.14403","snapshot_observed_at":"2026-08-06T19:24:50.937376Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:50.937376Z"},"links":{"cited_paper":"/paper/2403.14403","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:76dd50d43633ffe912d4a05c42dd77da10d7558c275f9f2bd4880d54fedebe8e","observation_id":"1b074398-5056-4add-82ed-72cb467ee1e8","resolution":{"observed_at":"2026-08-06T19:24:50.937376Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.06146","last_updated":"2019-09-13T11:18:20Z","snapshot_observed_at":"2026-08-13T00:36:57.540362Z","submitted_at":"2019-09-13T11:18:20Z","title":"PubMedQA: A Dataset for Biomedical Research Question Answering","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.06146","snapshot_observed_at":"2026-08-06T19:24:51.044477Z","title":"Cohen, and Xinghua Lu","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:51.044477Z"},"links":{"cited_paper":"/paper/1909.06146","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:5017cd8494666fd69c3d0a7c585959ba718cdc8950d2d9e764cc07afceb015e3","observation_id":"75de11d2-0a5d-49cc-bb96-2c5cfa176d76","resolution":{"observed_at":"2026-08-06T19:24:51.044477Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.06180","last_updated":"2023-09-12T12:50:04Z","snapshot_observed_at":"2026-08-02T09:51:08.145755Z","submitted_at":"2023-09-12T12:50:04Z","title":"Efficient Memory Management for Large Language Model Serving with PagedAttention","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.06180","snapshot_observed_at":"2026-08-06T19:24:51.129296Z","title":"Gonzalez, Hao Zhang, and Ion Stoica","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:51.129296Z"},"links":{"cited_paper":"/paper/2309.06180","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:6d5e8b63b4c7a079131b1c5b9ea4a44f31b834efbe8aa0e257590f818ddf19b5","observation_id":"30025f82-6b1a-427e-b7ad-024588f0a936","resolution":{"observed_at":"2026-08-06T19:24:51.129296Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.11401","last_updated":"2021-04-12T15:42:18Z","snapshot_observed_at":"2026-08-07T05:44:30.677502Z","submitted_at":"2020-05-22T21:34:34Z","title":"Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.11401","snapshot_observed_at":"2026-08-06T19:24:51.212243Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:51.212243Z"},"links":{"cited_paper":"/paper/2005.11401","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:9c1220ef4192113fb326b92c302a859eac83ce8c49454259ff699092bf4939eb","observation_id":"220e272e-14df-468a-951d-2c453ad08e2f","resolution":{"observed_at":"2026-08-06T19:24:51.212243Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.09848","last_updated":"2023-10-23T19:11:38Z","snapshot_observed_at":"2026-08-13T11:59:15.193767Z","submitted_at":"2023-04-19T17:56:12Z","title":"Evaluating Verifiability in Generative Search Engines","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.09848","snapshot_observed_at":"2026-08-06T19:24:51.275802Z","title":"Liu, Tianyi Zhang, and Percy Liang","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:51.275802Z"},"links":{"cited_paper":"/paper/2304.09848","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:04fd141d90efc088fd3345baf1e15f92461773ece6da909dcd965a5580b16e88","observation_id":"5dfd4035-795f-4bb1-a325-145dd1ea584f","resolution":{"observed_at":"2026-08-06T19:24:51.275802Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.10225","last_updated":"2024-10-30T02:58:14Z","snapshot_observed_at":"2026-08-13T04:39:36.385944Z","submitted_at":"2024-01-18T18:59:11Z","title":"ChatQA: Surpassing GPT-4 on Conversational QA and RAG","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.10225","snapshot_observed_at":"2026-08-06T19:24:51.379621Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:51.379621Z"},"links":{"cited_paper":"/paper/2401.10225","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:cadca1bfa41446e3fb2179229512a82df046506489345c0339459d5c02553f5e","observation_id":"0e1fcf93-988f-465b-a8a3-acc6eb8bf42f","resolution":{"observed_at":"2026-08-06T19:24:51.379621Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.10511","last_updated":"2023-07-02T07:21:59Z","snapshot_observed_at":"2026-07-06T14:33:08.041820Z","submitted_at":"2022-12-20T18:30:15Z","title":"When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.10511","snapshot_observed_at":"2026-08-06T19:24:51.468480Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:51.468480Z"},"links":{"cited_paper":"/paper/2212.10511","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:67390cb85b6b35f27089ccf46880074d1433cf925859bb7054062b6d98624211","observation_id":"71886768-6fbb-4795-894c-9096ba29b245","resolution":{"observed_at":"2026-08-06T19:24:51.468480Z","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":"2026-08-06T19:24:51.530008Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:51.530008Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:5740734122e5f393c6bdbf8a63a02c4a0d53094559f3aad9e6cf67f4e332e13b","observation_id":"0ee91bf1-5067-4c31-b46f-9db94a0af3f4","resolution":{"observed_at":"2026-08-06T19:24:51.530008Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.00083","last_updated":"2023-08-01T12:10:15Z","snapshot_observed_at":"2026-08-13T12:53:56.601584Z","submitted_at":"2023-01-31T20:26:16Z","title":"In-Context Retrieval-Augmented Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.00083","snapshot_observed_at":"2026-08-06T19:24:51.610954Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:51.610954Z"},"links":{"cited_paper":"/paper/2302.00083","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:c5b6ff2ab8089d57c02f8579515f9acbd6768967a3e6de79acef94ce36f8e372","observation_id":"4481cc7b-1372-4743-ba5a-8e0fe63fab75","resolution":{"observed_at":"2026-08-06T19:24:51.610954Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.08277","last_updated":"2024-06-03T16:48:59Z","snapshot_observed_at":"2026-08-13T04:20:01.822302Z","submitted_at":"2024-02-13T08:12:48Z","title":"Towards Faithful and Robust LLM Specialists for Evidence-Based Question-Answering","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.08277","snapshot_observed_at":"2026-08-06T19:24:51.809937Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:51.809937Z"},"links":{"cited_paper":"/paper/2402.08277","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:571432c18e0e87145cf2b91eb15fdd2b1a8a0bdaa5b93a93de0da2dd1c3a7a0c","observation_id":"50a07832-9a34-4150-8f29-d289a2840f83","resolution":{"observed_at":"2026-08-06T19:24:51.809937Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.00093","last_updated":"2023-06-06T08:36:20Z","snapshot_observed_at":"2026-08-13T12:53:56.066493Z","submitted_at":"2023-01-31T20:48:57Z","title":"Large Language Models Can Be Easily Distracted by Irrelevant Context","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.00093","snapshot_observed_at":"2026-08-06T19:24:51.991510Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:51.991510Z"},"links":{"cited_paper":"/paper/2302.00093","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:569c9e558cd0baed2ac44d86f5b1b41769f59b0c80871319d41b6187bbedf91d","observation_id":"069f6937-a358-4d54-9bac-7bd76e8d40a7","resolution":{"observed_at":"2026-08-06T19:24:51.991510Z","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":"2026-08-06T19:24:52.129546Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:52.129546Z"},"links":{"citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:d78fb6a5a6460c8cf69eafe302427bcae43d9e85cfce61fe4ef3e33560f6edc5","observation_id":"214e8902-9a7e-4c43-a26c-c344848ba4a4","resolution":{"observed_at":"2026-08-06T19:24:52.129546Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-07T12:56:43.323460Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-08-06T19:24:52.238510Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:52.238510Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:5eb67ba8fe7be5067047f46ef46ce4dea4183cd1221689ea6ea91086e72a5c6d","observation_id":"b3224de9-dd24-4770-a883-bd42f3eedfef","resolution":{"observed_at":"2026-08-06T19:24:52.238510Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.00573","last_updated":"2022-05-05T05:50:50Z","snapshot_observed_at":"2026-08-13T18:35:36.592732Z","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":"2026-08-06T19:24:52.375707Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:52.375707Z"},"links":{"cited_paper":"/paper/2108.00573","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:c87e54bdc14cc8cbc52c98e154ec66b451f1299c26c0d4133e25f1a591a3902b","observation_id":"e0703914-dbb0-46e3-9d1e-d1cd8873fdb2","resolution":{"observed_at":"2026-08-06T19:24:52.375707Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.07713","last_updated":"2024-05-29T04:15:39Z","snapshot_observed_at":"2026-08-13T13:01:05.804566Z","submitted_at":"2023-10-11T17:59:05Z","title":"InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.07713","snapshot_observed_at":"2026-08-06T19:24:52.540163Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:52.540163Z"},"links":{"cited_paper":"/paper/2310.07713","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:f25aaea6ca08f0d7683aaceb291d75627595254b9cacafd112ec5bff3a651d88","observation_id":"4a63383f-ac01-4249-9d2f-f90711f38d47","resolution":{"observed_at":"2026-08-06T19:24:52.540163Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.11903","last_updated":"2023-01-10T23:07:57Z","snapshot_observed_at":"2026-08-13T07:04:41.220509Z","submitted_at":"2022-01-28T02:33:07Z","title":"Chain-of-Thought Prompting Elicits Reasoning in Large Language Models","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.11903","snapshot_observed_at":"2026-08-06T19:24:52.704493Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:52.704493Z"},"links":{"cited_paper":"/paper/2201.11903","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:6d305872b7f8d4386bc2e4e20d45e0e045afb39dfd773d8937c739a13009a724","observation_id":"e892e0d7-a639-43bc-968c-65bff2afdc1d","resolution":{"observed_at":"2026-08-06T19:24:52.704493Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.13629","last_updated":"2025-03-02T00:46:31Z","snapshot_observed_at":"2026-08-12T23:39:07.244890Z","submitted_at":"2024-06-19T15:25:29Z","title":"InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.13629","snapshot_observed_at":"2026-08-06T19:24:52.819484Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:52.819484Z"},"links":{"cited_paper":"/paper/2406.13629","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:7b334577291d542e57b7e6144dcd0474e1c3e737ad97dbe84c20d2e44bdfa39f","observation_id":"85ec07dc-4f2b-421f-b90f-b4ccef465ea1","resolution":{"observed_at":"2026-08-06T19:24:52.819484Z","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":"2026-08-12T23:18:29.132355Z","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":"2026-08-06T19:24:52.935031Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:52.935031Z"},"links":{"cited_paper":"/paper/2407.14482","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:d0fb0c3edc1cb6998377e4dc233d7e891c2b2c4aca1156c06ad5d014769117da","observation_id":"8e300ee3-8d5c-43b1-aa30-ee6dd9a01cc2","resolution":{"observed_at":"2026-08-06T19:24:52.935031Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1809.09600","last_updated":"2018-09-25T17:28:20Z","snapshot_observed_at":"2026-08-11T10:35:06.989614Z","submitted_at":"2018-09-25T17:28:20Z","title":"HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1809.09600","snapshot_observed_at":"2026-08-06T19:24:53.055003Z","title":"Cohen, Ruslan Salakhutdinov, and Christopher D","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:53.055003Z"},"links":{"cited_paper":"/paper/1809.09600","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:ae5e0b4bac54789e3cc665e3f9e26180bce7c498ddee692d6026415671392ced","observation_id":"082d7d84-4e74-4104-accf-a745477d0036","resolution":{"observed_at":"2026-08-06T19:24:53.055003Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.02485","last_updated":"2024-07-02T17:59:17Z","snapshot_observed_at":"2026-08-12T23:30:11.293443Z","submitted_at":"2024-07-02T17:59:17Z","title":"RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.02485","snapshot_observed_at":"2026-08-06T19:24:53.214443Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:53.214443Z"},"links":{"cited_paper":"/paper/2407.02485","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:8df826a474c27f11d8369e4ac8b8cde853c5214cc7625f44567c27a4da19252e","observation_id":"8186e39a-1615-4be8-8e25-e1a19f271971","resolution":{"observed_at":"2026-08-06T19:24:53.214443Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.10131","last_updated":"2024-06-05T17:27:51Z","snapshot_observed_at":"2026-08-13T00:53:38.526176Z","submitted_at":"2024-03-15T09:26:02Z","title":"RAFT: Adapting Language Model to Domain Specific RAG","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.10131","snapshot_observed_at":"2026-08-06T19:24:53.356483Z","title":"Patil, Naman Jain, Sheng Shen, Matei Zaharia, Ion Stoica, and Joseph E","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:53.356483Z"},"links":{"cited_paper":"/paper/2403.10131","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:bfa10e5a53d368a03ad79b07c3b106a3eb274d391bb231820a4e3f18a71520f7","observation_id":"1005dd11-28af-48d4-9469-ea35a027c9ef","resolution":{"observed_at":"2026-08-06T19:24:53.356483Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.01219","last_updated":"2025-09-14T09:34:46Z","snapshot_observed_at":"2026-07-06T16:13:46.112815Z","submitted_at":"2023-09-03T16:56:48Z","title":"Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.01219","snapshot_observed_at":"2026-08-06T19:24:53.481397Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:53.481397Z"},"links":{"cited_paper":"/paper/2309.01219","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:b619d02dadb51282fa49ad4f36560ac03f86579bf731ed68f68ad80f69d25463","observation_id":"130ef53c-4e31-4e0d-9f89-e9ece8e44098","resolution":{"observed_at":"2026-08-06T19:24:53.481397Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.15569","last_updated":"2024-08-30T14:52:24Z","snapshot_observed_at":"2026-08-12T23:17:22.251897Z","submitted_at":"2024-07-22T11:55:14Z","title":"An Empirical Study of Retrieval Augmented Generation with Chain-of-Thought","version":2},"cited_work":{"arxiv_id":"2407.15569","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.15569","snapshot_observed_at":"2026-08-06T19:24:53.962393Z","title":"An Empirical Study of Retrieval Augmented Generation with Chain-of-Thought","venue":"cs.CL","work_id":"a72512c3-3ca7-46f6-9228-78363053d3a7","year":2024},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:53.596252Z"},"links":{"cited_paper":"/paper/2407.15569","citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:e79e2f04c12bc7aa9aacc1760985d0d700735c00e24a8c10f39db3102a2c4628","observation_id":"2d76cc8f-01e4-4f5c-b565-8a8e0e7ec754","resolution":{"observed_at":"2026-08-06T19:24:54.036798Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-06T19:24:53.694085Z","title":"online\" 'onlinestring :=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:53.694085Z"},"links":{"citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:980f86b717e544cf6abd8b3ff314a081729c41d700dc0dd751ba10c419cd3c17","observation_id":"169d601d-b35c-4499-acf3-a29ab54ef980","resolution":{"observed_at":"2026-08-06T19:24:53.694085Z","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":"2026-08-06T19:24:53.797281Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation","version":3},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-06T19:24:53.797281Z"},"links":{"citing_paper":"/paper/2507.05714"},"observation_digest":"sha256:b595fab69daa5567f04469ab011d119c20b440491e2a1fe1c8d2f67756a481d2","observation_id":"06d75e68-9222-459a-95b1-411ac94e89fa","resolution":{"observed_at":"2026-08-06T19:24:53.797281Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.05714","last_updated":"2025-09-10T03:00:18Z","latest_version":3,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-10T02:04:53.026532Z","submitted_at":"2025-07-08T06:53:28Z","title":"HIRAG: Hierarchical-Thought Instruction-Tuning Retrieval-Augmented Generation"},"reference_resolution":{"displayed":35,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":34,"verified_exact":1,"verified_fuzzy":0},"total_outbound_references":35},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 5 inbound Pith citation observations for arXiv:2507.05714."}