{"as_of":"2026-08-10T12:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:62c826f4228ca323d24cd4c992bef84be27cab356be27dbf6a7f460f8a3dcef8","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":11,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":11,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":11,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":11,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T11:21:51.700097Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":1,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2410.02338","last_updated":"2024-10-04T14:59:04Z","snapshot_observed_at":"2026-07-06T19:26:54.335667Z","submitted_at":"2024-10-03T09:48:09Z","title":"How Much Can RAG Help the Reasoning of LLM?","version":2},"cited_work":{"arxiv_id":"2410.02338","doi":"10.48550/arxiv.2410.02338","metadata_source":"arxiv_reference","pith_arxiv_id":"2410.02338","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"How much can RAG help the reasoning of llm?CoRR, abs/2410.02338","venue":"arXiv (Cornell University)","work_id":"996d68ad-d888-4f40-b752-a701db7dd6ca","year":2024},"citing_paper":{"arxiv_id":"2501.05366","last_updated":"2025-01-09T16:48:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-09T16:48:17Z","title":"Search-o1: Agentic Search-Enhanced Large Reasoning Models","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-13T17:36:27.515468Z"},"links":{"cited_paper":"/paper/2410.02338","citing_paper":"/paper/2501.05366"},"observation_digest":"sha256:eaf9fd642df7d1c59895084ef78a660a93a9c1f085456ea8da3a3d4f9b214349","observation_id":"620cfbf7-8a45-4eee-af99-677a67704333","resolution":{"observed_at":"2026-05-13T17:36:27.564269Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.02338","last_updated":"2024-10-04T14:59:04Z","snapshot_observed_at":"2026-07-06T19:26:54.335667Z","submitted_at":"2024-10-03T09:48:09Z","title":"How Much Can RAG Help the Reasoning of LLM?","version":2},"cited_work":{"arxiv_id":"2410.02338","doi":"10.48550/arxiv.2410.02338","metadata_source":"arxiv_reference","pith_arxiv_id":"2410.02338","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"How much can RAG help the reasoning of llm?CoRR, abs/2410.02338","venue":"arXiv (Cornell University)","work_id":"996d68ad-d888-4f40-b752-a701db7dd6ca","year":2024},"citing_paper":{"arxiv_id":"2504.21776","last_updated":"2025-10-13T12:40:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T16:25:25Z","title":"WebThinker: Empowering Large Reasoning Models with Deep Research Capability","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-16T19:14:25.283645Z"},"links":{"cited_paper":"/paper/2410.02338","citing_paper":"/paper/2504.21776"},"observation_digest":"sha256:fa11be1234c23bbc883b6d8846bf659a42a501816ccf885f6786503f13a7d6ff","observation_id":"0ba83370-ab54-4fcb-93ff-40c743543e19","resolution":{"observed_at":"2026-05-16T19:14:25.353941Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.02338","last_updated":"2024-10-04T14:59:04Z","snapshot_observed_at":"2026-07-06T19:26:54.335667Z","submitted_at":"2024-10-03T09:48:09Z","title":"How Much Can RAG Help the Reasoning of LLM?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.02338","snapshot_observed_at":"2026-08-07T11:21:51.700097Z","title":"In NAACL-HLT, pages 4228–4238","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.05386","last_updated":"2025-08-09T19:31:11Z","snapshot_observed_at":"2026-08-08T15:12:42.397056Z","submitted_at":"2025-06-03T12:59:52Z","title":"Leaps Beyond the Seen: Reinforced Reasoning Augmented Generation for Clinical Notes","version":3},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-07T11:21:51.700097Z"},"links":{"cited_paper":"/paper/2410.02338","citing_paper":"/paper/2506.05386"},"observation_digest":"sha256:0bf72b8cc7991256b87c1492d2d04f3421e4c1491abf053f0627771153b86c12","observation_id":"4f52b676-c5f4-4405-be4d-6affaf9de1f8","resolution":{"observed_at":"2026-08-07T11:21:51.700097Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.02338","last_updated":"2024-10-04T14:59:04Z","snapshot_observed_at":"2026-07-06T19:26:54.335667Z","submitted_at":"2024-10-03T09:48:09Z","title":"How Much Can RAG Help the Reasoning of LLM?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.02338","snapshot_observed_at":"2026-08-06T22:44:01.630597Z","title":"How much can rag help the reasoning of llm? arXiv preprint arXiv:2410.02338 , 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.20963","last_updated":"2025-07-04T01:31:36Z","snapshot_observed_at":"2026-08-09T23:24:21.009731Z","submitted_at":"2025-06-26T03:01:33Z","title":"EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corpora","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T22:44:01.630597Z"},"links":{"cited_paper":"/paper/2410.02338","citing_paper":"/paper/2506.20963"},"observation_digest":"sha256:d82ddc0be1e636e7985a8297d64b79b2171bdc936e0493bfa6cdff05ba10d13e","observation_id":"2b913be1-8284-46c4-859f-e4e1a9452d8a","resolution":{"observed_at":"2026-08-06T22:44:01.630597Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.02338","last_updated":"2024-10-04T14:59:04Z","snapshot_observed_at":"2026-07-06T19:26:54.335667Z","submitted_at":"2024-10-03T09:48:09Z","title":"How Much Can RAG Help the Reasoning of LLM?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.02338","snapshot_observed_at":"2026-08-06T21:36:38.681531Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.23762","last_updated":"2025-06-30T12:09:29Z","snapshot_observed_at":"2026-08-06T21:29:49.550137Z","submitted_at":"2025-06-30T12:09:29Z","title":"Software Engineering for Large Language Models: Research Status, Challenges and the Road Ahead","version":1},"reference_index":209,"source":"pdf_text","source_observed_at":"2026-08-06T21:36:38.681531Z"},"links":{"cited_paper":"/paper/2410.02338","citing_paper":"/paper/2506.23762"},"observation_digest":"sha256:8826ca9be08c6a9177821aaca8c0510595e25fb91cdc61f307cc288776f278a3","observation_id":"fa545ba3-ee82-440d-9f46-59ba167bee5f","resolution":{"observed_at":"2026-08-06T21:36:38.681531Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.02338","last_updated":"2024-10-04T14:59:04Z","snapshot_observed_at":"2026-07-06T19:26:54.335667Z","submitted_at":"2024-10-03T09:48:09Z","title":"How Much Can RAG Help the Reasoning of LLM?","version":2},"cited_work":{"arxiv_id":"2410.02338","doi":"10.48550/arxiv.2410.02338","metadata_source":"arxiv_reference","pith_arxiv_id":"2410.02338","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"How much can RAG help the reasoning of llm?CoRR, abs/2410.02338","venue":"arXiv (Cornell University)","work_id":"996d68ad-d888-4f40-b752-a701db7dd6ca","year":2024},"citing_paper":{"arxiv_id":"2604.05306","last_updated":"2026-05-13T22:28:35Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-07T01:20:29Z","title":"LLMs Should Express Uncertainty Explicitly","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-10T20:12:46.367760Z"},"links":{"cited_paper":"/paper/2410.02338","citing_paper":"/paper/2604.05306"},"observation_digest":"sha256:65768bedb3865093afa101d1c6869f196dc3f509e854ad8cb8d6f2459717b729","observation_id":"45453462-2a67-4761-b6f0-c51aa657ca71","resolution":{"observed_at":"2026-05-10T22:10:48.096603Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.02338","last_updated":"2024-10-04T14:59:04Z","snapshot_observed_at":"2026-07-06T19:26:54.335667Z","submitted_at":"2024-10-03T09:48:09Z","title":"How Much Can RAG Help the Reasoning of LLM?","version":2},"cited_work":{"arxiv_id":"2410.02338","doi":"10.48550/arxiv.2410.02338","metadata_source":"arxiv_reference","pith_arxiv_id":"2410.02338","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"How much can RAG help the reasoning of llm?CoRR, abs/2410.02338","venue":"arXiv (Cornell University)","work_id":"996d68ad-d888-4f40-b752-a701db7dd6ca","year":2024},"citing_paper":{"arxiv_id":"2604.05306","last_updated":"2026-05-13T22:28:35Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-04-07T01:20:29Z","title":"LLMs Should Express Uncertainty Explicitly","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-15T07:11:39.471878Z"},"links":{"cited_paper":"/paper/2410.02338","citing_paper":"/paper/2604.05306"},"observation_digest":"sha256:2d54ea7a6d1302951b00513036472f676b61d40046e76e83972bc2e5edd33a28","observation_id":"288a9a2d-b437-457d-99af-a3b6d5751521","resolution":{"observed_at":"2026-05-15T07:15:11.854038Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.02338","last_updated":"2024-10-04T14:59:04Z","snapshot_observed_at":"2026-07-06T19:26:54.335667Z","submitted_at":"2024-10-03T09:48:09Z","title":"How Much Can RAG Help the Reasoning of LLM?","version":2},"cited_work":{"arxiv_id":"2410.02338","doi":"10.48550/arxiv.2410.02338","metadata_source":"arxiv_reference","pith_arxiv_id":"2410.02338","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"How much can RAG help the reasoning of llm?CoRR, abs/2410.02338","venue":"arXiv (Cornell University)","work_id":"996d68ad-d888-4f40-b752-a701db7dd6ca","year":2024},"citing_paper":{"arxiv_id":"2604.21380","last_updated":"2026-04-27T16:24:25Z","snapshot_observed_at":"2026-08-07T00:17:45.322644Z","submitted_at":"2026-04-23T07:50:49Z","title":"Conjecture and Inquiry: Quantifying Software Performance Requirements via Interactive Retrieval-Augmented Preference Elicitation","version":2},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-05-09T21:20:30.481494Z"},"links":{"cited_paper":"/paper/2410.02338","citing_paper":"/paper/2604.21380"},"observation_digest":"sha256:ad3f20eb967fcf20c4026c12ad3662a16b83d8a80587e2d9bdd10b798bea4ad1","observation_id":"cea842fd-24a0-4129-8272-4b9ac249e735","resolution":{"observed_at":"2026-05-09T21:23:25.877751Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.02338","last_updated":"2024-10-04T14:59:04Z","snapshot_observed_at":"2026-07-06T19:26:54.335667Z","submitted_at":"2024-10-03T09:48:09Z","title":"How Much Can RAG Help the Reasoning of LLM?","version":2},"cited_work":{"arxiv_id":"2410.02338","doi":"10.48550/arxiv.2410.02338","metadata_source":"arxiv_reference","pith_arxiv_id":"2410.02338","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"How much can RAG help the reasoning of llm?CoRR, abs/2410.02338","venue":"arXiv (Cornell University)","work_id":"996d68ad-d888-4f40-b752-a701db7dd6ca","year":2024},"citing_paper":{"arxiv_id":"2605.03344","last_updated":"2026-06-08T22:01:42Z","snapshot_observed_at":"2026-07-06T23:16:15.509235Z","submitted_at":"2026-05-05T04:03:28Z","title":"RAG over Thinking Traces Can Improve Reasoning Tasks","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-05-07T14:37:48.165452Z"},"links":{"cited_paper":"/paper/2410.02338","citing_paper":"/paper/2605.03344"},"observation_digest":"sha256:c3b6d4a89ac60297b94ae07a5e06575e8d35f18412c19d02b3f1b88cc3564d82","observation_id":"9336ae2d-0141-4df7-b6e1-2cb8e078fd0b","resolution":{"observed_at":"2026-05-12T00:41:25.391636Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.02338","last_updated":"2024-10-04T14:59:04Z","snapshot_observed_at":"2026-07-06T19:26:54.335667Z","submitted_at":"2024-10-03T09:48:09Z","title":"How Much Can RAG Help the Reasoning of LLM?","version":2},"cited_work":{"arxiv_id":"2410.02338","doi":"10.48550/arxiv.2410.02338","metadata_source":"arxiv_reference","pith_arxiv_id":"2410.02338","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"How much can RAG help the reasoning of llm?CoRR, abs/2410.02338","venue":"arXiv (Cornell University)","work_id":"996d68ad-d888-4f40-b752-a701db7dd6ca","year":2024},"citing_paper":{"arxiv_id":"2606.13680","last_updated":"2026-06-11T17:59:52Z","snapshot_observed_at":"2026-07-06T23:52:23.981568Z","submitted_at":"2026-06-11T17:59:52Z","title":"Learning to Reason by Analogy via Retrieval-Augmented Reinforcement Fine-Tuning","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-06-27T06:30:55.592334Z"},"links":{"cited_paper":"/paper/2410.02338","citing_paper":"/paper/2606.13680"},"observation_digest":"sha256:a6701bb4f5e0b80d56e148437585674a7165009bc6ab525f3f6169006f7d5008","observation_id":"77557933-b217-49ef-a0f4-29bf33f91999","resolution":{"observed_at":"2026-07-03T15:28:33.910054Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.02338","last_updated":"2024-10-04T14:59:04Z","snapshot_observed_at":"2026-07-06T19:26:54.335667Z","submitted_at":"2024-10-03T09:48:09Z","title":"How Much Can RAG Help the Reasoning of LLM?","version":2},"cited_work":{"arxiv_id":"2410.02338","doi":"10.48550/arxiv.2410.02338","metadata_source":"arxiv_reference","pith_arxiv_id":"2410.02338","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"How much can RAG help the reasoning of llm?CoRR, abs/2410.02338","venue":"arXiv (Cornell University)","work_id":"996d68ad-d888-4f40-b752-a701db7dd6ca","year":2024},"citing_paper":{"arxiv_id":"2607.01511","last_updated":"2026-07-01T22:17:39Z","snapshot_observed_at":"2026-08-02T17:13:50.983394Z","submitted_at":"2026-07-01T22:17:39Z","title":"Revisiting Chain-of-Thought Reasoning under Limited Supervision: Semi-supervised Chain-of-Thought Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-03T20:04:19.110148Z"},"links":{"cited_paper":"/paper/2410.02338","citing_paper":"/paper/2607.01511"},"observation_digest":"sha256:3ffe5d2c7050e9a0ba30b5c65983e20bfadf97c0289294d64f188641c89752c8","observation_id":"d0071f79-82ea-448f-9f5b-7b8a105eb24b","resolution":{"observed_at":"2026-07-03T20:08:54.901845Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2410.02338/citation-record","integrity":"/paper/2410.02338/integrity","json":"/paper/2410.02338/citation-record.json","paper":"/paper/2410.02338"},"outbound":[],"paper":{"arxiv_id":"2410.02338","last_updated":"2024-10-04T14:59:04Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-07-06T19:26:54.335667Z","submitted_at":"2024-10-03T09:48:09Z","title":"How Much Can RAG Help the Reasoning of LLM?"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2410.02338."}