{"as_of":"2026-08-16T21:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:add71efba89ee49d96e23eed7a12b22443bc8517532a015e3f89ad8dc994e573","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T11:20:18.713089Z","state":"measured"},{"denominator":43,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":43,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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":"2026-08-10T05:09:26.622417Z","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":"2026-06-30T12:34:38.843639Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.15800","snapshot_observed_at":"2026-08-06T23:48:06.816069Z","title":"Finder: Financial dataset for question answer- ing and evaluating retrieval-augmented generation,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.16037","last_updated":"2025-06-19T05:22:18Z","snapshot_observed_at":"2026-08-16T13:17:49.257160Z","submitted_at":"2025-06-19T05:22:18Z","title":"Enhancing Document-Level Question Answering via Multi-Hop Retrieval-Augmented Generation with LLaMA 3","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T23:48:06.816069Z"},"links":{"cited_paper":"/paper/2504.15800","citing_paper":"/paper/2506.16037"},"observation_digest":"sha256:53a2ba8aa7ad3ae9ccf199880b3f0816f8f917757023e5fae7608423f763958e","observation_id":"e451ad7e-2013-4184-80b5-b883dbdf1cc0","resolution":{"observed_at":"2026-08-06T23:48:06.816069Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"cited_work":{"arxiv_id":"2504.15800","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.15800","snapshot_observed_at":"2026-06-30T12:34:38.843639Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","venue":null,"work_id":"4aef505c-084a-4059-8de6-b07c3d3fd24b","year":2025},"citing_paper":{"arxiv_id":"2603.16877","last_updated":"2026-04-28T06:59:31Z","snapshot_observed_at":"2026-08-16T01:08:55.101278Z","submitted_at":"2026-02-18T20:54:23Z","title":"Enhancing Financial Report Question-Answering: A Retrieval-Augmented Generation System with Reranking Analysis","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-15T20:58:11.734614Z"},"links":{"cited_paper":"/paper/2504.15800","citing_paper":"/paper/2603.16877"},"observation_digest":"sha256:ce55d8c3aa808de094a10b61b889619c2c1f0210782eff4ada1b6aff3fb4616c","observation_id":"5800c5b8-edca-4b41-a098-af7feb51f840","resolution":{"observed_at":"2026-05-15T21:00:17.956208Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"cited_work":{"arxiv_id":"2504.15800","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.15800","snapshot_observed_at":"2026-06-30T12:34:38.843639Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","venue":null,"work_id":"4aef505c-084a-4059-8de6-b07c3d3fd24b","year":2025},"citing_paper":{"arxiv_id":"2603.26815","last_updated":"2026-06-26T23:03:46Z","snapshot_observed_at":"2026-08-15T03:55:26.956817Z","submitted_at":"2026-03-26T18:05:38Z","title":"Sustainable Hybrid Document-Routed Retrieval for Financial RAG: Resolving the Robustness-Precision Trade-off","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-15T00:20:51.066532Z"},"links":{"cited_paper":"/paper/2504.15800","citing_paper":"/paper/2603.26815"},"observation_digest":"sha256:a708f8f724acd4e9e0625db8fc194f39fdea859448a801e7c95562442d5262ff","observation_id":"d10298d5-5452-4735-99b9-2e14778171a0","resolution":{"observed_at":"2026-05-15T00:23:22.802806Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"cited_work":{"arxiv_id":"2504.15800","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.15800","snapshot_observed_at":"2026-06-30T12:34:38.843639Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","venue":null,"work_id":"4aef505c-084a-4059-8de6-b07c3d3fd24b","year":2025},"citing_paper":{"arxiv_id":"2604.14488","last_updated":"2026-04-28T13:48:12Z","snapshot_observed_at":"2026-08-15T23:08:45.079344Z","submitted_at":"2026-04-15T23:56:35Z","title":"Controlling Authority Retrieval: A Missing Retrieval Objective for Authority-Governed Knowledge","version":3},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-05-10T11:50:27.258770Z"},"links":{"cited_paper":"/paper/2504.15800","citing_paper":"/paper/2604.14488"},"observation_digest":"sha256:52e1a04c10a74f975216988e058d30375daab533087af9be0056f279207ebee2","observation_id":"975e076d-3eb7-4c78-ae44-f63b0ee085eb","resolution":{"observed_at":"2026-05-10T11:55:21.090307Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"cited_work":{"arxiv_id":"2504.15800","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2504.15800","snapshot_observed_at":"2026-06-30T12:34:38.843639Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","venue":null,"work_id":"4aef505c-084a-4059-8de6-b07c3d3fd24b","year":2025},"citing_paper":{"arxiv_id":"2605.25030","last_updated":"2026-05-24T12:15:27Z","snapshot_observed_at":"2026-08-15T13:55:39.040627Z","submitted_at":"2026-05-24T12:15:27Z","title":"MimirRAG: A Multi-Agent RAG Framework for Financial Data Retrieval with Metadata Integration","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-06-30T12:27:46.629948Z"},"links":{"cited_paper":"/paper/2504.15800","citing_paper":"/paper/2605.25030"},"observation_digest":"sha256:94ce00001d6c92114c67b735c604b13e5a9e579f62363c5fd7120b979f7178ed","observation_id":"cb4fa91b-333c-47c2-8b6b-93c71cb769e5","resolution":{"observed_at":"2026-06-30T12:34:38.845279Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.15800","snapshot_observed_at":"2026-08-01T16:10:06.344884Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.18102","last_updated":"2026-07-21T15:53:04Z","snapshot_observed_at":"2026-08-13T12:20:17.395514Z","submitted_at":"2026-07-20T16:03:15Z","title":"FinSAgent: Corpus-Aligned Multi-Agent RAG Framework for Evidence-Grounded SEC Filing Question Answering","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-01T16:10:06.344884Z"},"links":{"cited_paper":"/paper/2504.15800","citing_paper":"/paper/2607.18102"},"observation_digest":"sha256:e69f6d0a68dbd4c896aec1f762d86996290be7f89c8a75be6f52bd9119b81e64","observation_id":"cec0de70-3928-4b7d-9d83-c55e0a8abf24","resolution":{"observed_at":"2026-08-01T16:10:06.344884Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.15800","snapshot_observed_at":"2026-08-10T05:09:26.622417Z","title":"arXiv preprint arXiv:2504.15800 , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.07400","last_updated":"2026-08-07T16:45:39Z","snapshot_observed_at":"2026-08-15T23:17:09.183345Z","submitted_at":"2026-08-07T16:45:39Z","title":"FinRank: An Evidence-Grounded Benchmark for Financial Question Answering and Retrieval over SEC Filings","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-10T05:09:26.622417Z"},"links":{"cited_paper":"/paper/2504.15800","citing_paper":"/paper/2608.07400"},"observation_digest":"sha256:616b0d012e5256c04f89ab6d3cba86ca8c5cc174c8a4b22fbdd158d2b4769beb","observation_id":"74fe4548-4c39-4f2c-ab6c-fdec4fd0756d","resolution":{"observed_at":"2026-08-10T05:09:26.622417Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2504.15800/citation-record","integrity":"/paper/2504.15800/integrity","json":"/paper/2504.15800/citation-record.json","paper":"/paper/2504.15800"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-16T19:40:28.523700Z","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":"2026-08-16T11:20:18.578638Z","title":"Gpt-4 technical report","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.578638Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:ca1ae98e4608cab421f7c570a77dde2adb7fa40b8e483a47803f4b5394ff8040","observation_id":"f1ac02c0-5f22-41c1-b09f-a1738d4d231b","resolution":{"observed_at":"2026-08-16T11:20:18.578638Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.00122","last_updated":"2022-05-07T07:52:39Z","snapshot_observed_at":"2026-08-16T17:59:38.950031Z","submitted_at":"2021-09-01T00:08:14Z","title":"FinQA: A Dataset of Numerical Reasoning over Financial Data","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.00122","snapshot_observed_at":"2026-08-16T11:20:18.592308Z","title":"Finqa: A dataset of numerical reasoning over financial data","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.592308Z"},"links":{"cited_paper":"/paper/2109.00122","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:92fc2a6cb8697fd6549561ee911c6a7f9268da134e4f7d0f2ffaab903d3842f8","observation_id":"ba24aa6a-cf6b-4095-a8a1-b108785c8f91","resolution":{"observed_at":"2026-08-16T11:20:18.592308Z","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":"2026-08-16T11:20:19.134169Z","title":"Inf-ufg at fiqa 2018 task 1: predicting sentiments and aspects on financial tweets and news headlines","venue":null,"work_id":"a22990e6-2f6f-4325-90a4-cda31c5af82d","year":2018},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.605141Z"},"links":{"citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:d8dc5c708a877faabf387774397b580bdef8ec52d3e35c3dc45752af8f003691","observation_id":"7a768319-0623-439c-9a57-361e7788f5be","resolution":{"observed_at":"2026-08-16T11:20:19.138983Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-16T11:20:19.122391Z","title":"Managing the complex- ity of processing financial data at scale-an experience report","venue":null,"work_id":"c57fd065-90e9-457e-914d-fc969973cad7","year":2019},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.616586Z"},"links":{"citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:20cef3d1dc1637ed787761f585fffe8e0e89ada0c0bbb6b2e54ec7ed4a9317fb","observation_id":"f4002270-f630-477e-837f-4f4f41f4b66f","resolution":{"observed_at":"2026-08-16T11:20:19.126877Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-08-15T12:33:55.451951Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-16T11:20:18.627720Z","title":"Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.627720Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:54490c63241c325062bda653bc2ba0ff36a7f7595b7eaec24a9cf36b198e9d68","observation_id":"fa1180f3-d566-4980-8faf-9c8061c55c03","resolution":{"observed_at":"2026-08-16T11:20:18.627720Z","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":"2026-08-16T11:20:18.634690Z","title":"A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.634690Z"},"links":{"cited_paper":"/paper/2311.05232","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:83c391321d2cd9ecf40b5dd3e56dfbcae16e6cc9fe138ab0855f37bb569f22d5","observation_id":"f8d2a76e-bf06-4c46-997e-ec2e4fa74676","resolution":{"observed_at":"2026-08-16T11:20:18.634690Z","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":"2026-08-16T11:20:19.111498Z","title":"Evaluating retrieval- augmented generation models for financial report question and answering","venue":null,"work_id":"76cd8771-fa49-4581-af17-6c56aab7d5d1","year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.638331Z"},"links":{"citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:8d7108dd6a0ed151365ff3ac8d954e43ce40b9ffab6909f468a50f61ebaca2a7","observation_id":"fab7eef6-12d7-4fcf-906d-8cf9760cadf9","resolution":{"observed_at":"2026-08-16T11:20:19.115211Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.11944","last_updated":"2023-11-20T17:28:02Z","snapshot_observed_at":"2026-08-13T18:35:52.271946Z","submitted_at":"2023-11-20T17:28:02Z","title":"FinanceBench: A New Benchmark for Financial Question Answering","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.11944","snapshot_observed_at":"2026-08-16T11:20:18.641630Z","title":"Financebench: A new benchmark for financial question answering","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.641630Z"},"links":{"cited_paper":"/paper/2311.11944","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:3d7533bb2fb8a85614b993e37ab72b4bddae6180ab6c182a0ae7d489b66bb565","observation_id":"cefcaa49-41f7-4043-a53e-8e8659bfcda9","resolution":{"observed_at":"2026-08-16T11:20:18.641630Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.16720","last_updated":"2026-04-30T02:46:40Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-12-21T18:04:31Z","title":"OpenAI o1 System Card","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.16720","snapshot_observed_at":"2026-08-16T11:20:18.645447Z","title":"Openai o1 system card","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.645447Z"},"links":{"cited_paper":"/paper/2412.16720","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:b34c1db21e7087720af2873a651666fb137fe493612c50357dc9f9786bf1d4a7","observation_id":"b80278d1-42fc-4c1e-8dfa-7ad17fb0a018","resolution":{"observed_at":"2026-08-16T11:20:18.645447Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.08890","last_updated":"2023-11-15T11:50:10Z","snapshot_observed_at":"2026-08-16T14:43:07.190515Z","submitted_at":"2023-11-15T11:50:10Z","title":"Large Language Models are legal but they are not: Making the case for a powerful LegalLLM","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.08890","snapshot_observed_at":"2026-08-16T11:20:18.648926Z","title":"Large language models are legal but they are not: Making the case for a powerful legalllm","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.648926Z"},"links":{"cited_paper":"/paper/2311.08890","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:2a4ff6ce5e985fe85cb2914898d5df52c97bc06a7261bf650fc3d39edc959abe","observation_id":"c703932a-d8e6-4011-b182-2bf4831c01d5","resolution":{"observed_at":"2026-08-16T11:20:18.648926Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.06983","last_updated":"2023-10-22T00:11:13Z","snapshot_observed_at":"2026-08-16T15:33:47.314609Z","submitted_at":"2023-05-11T17:13:40Z","title":"Active Retrieval Augmented Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.06983","snapshot_observed_at":"2026-08-16T11:20:18.652674Z","title":"Active retrieval augmented generation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.652674Z"},"links":{"cited_paper":"/paper/2305.06983","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:92dfe3a86db4e18c43eb7d47d03c4bd75253c91a79f9ce1e7254c906dbce4ea3","observation_id":"43f82cea-3c7d-4769-a07d-c35ebf0e8e90","resolution":{"observed_at":"2026-08-16T11:20:18.652674Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.07391","last_updated":"2025-01-13T15:07:55Z","snapshot_observed_at":"2026-08-14T21:45:02.458602Z","submitted_at":"2025-01-13T15:07:55Z","title":"Enhancing Retrieval-Augmented Generation: A Study of Best Practices","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.07391","snapshot_observed_at":"2026-08-16T11:20:18.656300Z","title":"Enhancing retrieval-augmented generation: A study of best practices","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.656300Z"},"links":{"cited_paper":"/paper/2501.07391","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:82f7120220444747800361ee8bca4d9482fdf34979d2b440c8eb5c5a924893c9","observation_id":"32efdc52-34d9-40e1-981f-d9bd2d16553a","resolution":{"observed_at":"2026-08-16T11:20:18.656300Z","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":"2026-08-16T11:20:19.100899Z","title":"Retrieval aug- mented generation or long-context llms? a comprehensive study and hybrid approach","venue":null,"work_id":"53047224-1ab8-445d-8d0c-d5ac2014a0df","year":2024},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.663842Z"},"links":{"citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:28c773d7a106b2e5488440d92809019f39af6cad230b96c39f674d893b6d98c1","observation_id":"02dc0b92-711a-4b72-9a75-7393ffd0255f","resolution":{"observed_at":"2026-08-16T11:20:19.104630Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1904.08375","last_updated":"2019-09-25T00:40:54Z","snapshot_observed_at":"2026-08-14T16:44:52.763460Z","submitted_at":"2019-04-17T17:20:14Z","title":"Document Expansion by Query Prediction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.08375","snapshot_observed_at":"2026-08-16T11:20:18.667574Z","title":"Document expansion by query prediction","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.667574Z"},"links":{"cited_paper":"/paper/1904.08375","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:e873c1badad768ef4938db1e989cfb674051f8a41f12aaf8e7e3c1a3a7febaba","observation_id":"3b1bb269-a17d-4885-aa09-a613bb771d1c","resolution":{"observed_at":"2026-08-16T11:20:18.667574Z","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":"2026-08-16T11:20:18.675433Z","title":"Docfinqa: A long-context financial reasoning dataset","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.675433Z"},"links":{"citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:fe3b136a1f4db38417558fd05c7b187d217c4c65b8667f47034b4d667dc577b7","observation_id":"f17f0c91-2073-4583-b0dc-821c209ced94","resolution":{"observed_at":"2026-08-16T11:20:18.675433Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.07221","last_updated":"2024-08-01T03:02:44Z","snapshot_observed_at":"2026-08-16T14:07:11.135175Z","submitted_at":"2024-03-23T00:49:40Z","title":"Improving Retrieval for RAG based Question Answering Models on Financial Documents","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.07221","snapshot_observed_at":"2026-08-16T11:20:18.679130Z","title":"Improving retrieval for rag based question answering models on financial documents","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.679130Z"},"links":{"cited_paper":"/paper/2404.07221","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:b6556f87b7b5649b36697bb280b9ca17c1f0c6bb6f6679ded8f846c39693c007","observation_id":"ff204101-20ee-4d92-99f0-6620784c160d","resolution":{"observed_at":"2026-08-16T11:20:18.679130Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.19572","last_updated":"2025-04-23T06:29:39Z","snapshot_observed_at":"2026-08-16T13:05:56.590359Z","submitted_at":"2024-10-25T14:07:53Z","title":"ChunkRAG: Novel LLM-Chunk Filtering Method for RAG Systems","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.19572","snapshot_observed_at":"2026-08-16T11:20:18.683176Z","title":"Chunkrag: Novel llm-chunk filtering method for rag systems","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.683176Z"},"links":{"cited_paper":"/paper/2410.19572","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:f7dd35e50fcd26339d24873fa9612922f07befd7dedc89811feb191cc9088052","observation_id":"f3c1489f-de28-4e48-bb9f-3476e4f91174","resolution":{"observed_at":"2026-08-16T11:20:18.683176Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.10168","last_updated":"2024-04-03T00:25:39Z","snapshot_observed_at":"2026-08-16T15:07:15.936180Z","submitted_at":"2023-08-20T05:31:03Z","title":"Head-to-Tail: How Knowledgeable are Large Language Models (LLMs)? A.K.A. Will LLMs Replace Knowledge Graphs?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.10168","snapshot_observed_at":"2026-08-16T11:20:18.687246Z","title":"Head-to-tail: How knowl- edgeable are large language models (llm)? aka will llms replace knowledge graphs? arXiv preprint arXiv:2308.10168,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.687246Z"},"links":{"cited_paper":"/paper/2308.10168","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:02ec57616917ca44abfc9dae8cd5ddde6ddc664a39fb21f984e19c42e299124f","observation_id":"589f43ba-c264-415b-acbe-0c43287d08ff","resolution":{"observed_at":"2026-08-16T11:20:18.687246Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.20266","last_updated":"2024-10-26T20:35:14Z","snapshot_observed_at":"2026-08-16T13:05:43.274592Z","submitted_at":"2024-10-26T20:35:14Z","title":"Limitations of the LLM-as-a-Judge Approach for Evaluating LLM Outputs in Expert Knowledge Tasks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.20266","snapshot_observed_at":"2026-08-16T11:20:18.691133Z","title":"Limitations of the llm-as-a-judge approach for evaluating llm outputs in expert knowledge tasks","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.691133Z"},"links":{"cited_paper":"/paper/2410.20266","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:046888028b33cefe976b6984dff249d40dd43cdc735f814f559cf7188aeaec55","observation_id":"3df3244e-cc25-4dc1-85c0-2ddd7b96b165","resolution":{"observed_at":"2026-08-16T11:20:18.691133Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.00368","last_updated":"2024-05-31T07:22:01Z","snapshot_observed_at":"2026-08-16T14:30:41.754354Z","submitted_at":"2023-12-31T02:13:18Z","title":"Improving Text Embeddings with Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.00368","snapshot_observed_at":"2026-08-16T11:20:18.694928Z","title":"Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, and Furu Wei","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.694928Z"},"links":{"cited_paper":"/paper/2401.00368","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:d75a564e2bff03d90e6c4596087263c3ff67615f4cd9edbf461e2a8565aef477","observation_id":"7950ffcb-8f3a-4b5c-8f4c-720e014da5fd","resolution":{"observed_at":"2026-08-16T11:20:18.694928Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.05131","last_updated":"2024-03-16T09:08:26Z","snapshot_observed_at":"2026-08-16T14:21:13.577227Z","submitted_at":"2024-02-05T22:35:42Z","title":"Financial Report Chunking for Effective Retrieval Augmented Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.05131","snapshot_observed_at":"2026-08-16T11:20:18.702161Z","title":"Boyu Zhang, Hongyang Yang, Tianyu Zhou, Muhammad Ali Babar, and Xiao-Yang Liu","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.702161Z"},"links":{"cited_paper":"/paper/2402.05131","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:747b9a098c5b7c07ce8892c75c27239ef286b46a00953a79a34e12d29d115a68","observation_id":"ccd4e20c-636d-482b-adfb-d59a43cfa441","resolution":{"observed_at":"2026-08-16T11:20:18.702161Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.01347","last_updated":"2022-06-03T00:24:35Z","snapshot_observed_at":"2026-08-16T16:55:37.431823Z","submitted_at":"2022-06-03T00:24:35Z","title":"MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.01347","snapshot_observed_at":"2026-08-16T11:20:18.705716Z","title":"Multihiertt: Numerical reasoning over multi hierarchical tabular and textual data","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.705716Z"},"links":{"cited_paper":"/paper/2206.01347","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:b3799ba975cba3351f696b8b3adf7627f4b2558b85cd3281745c70b3610242b7","observation_id":"3fd29a2c-81b3-435b-b702-1d93b87e34d0","resolution":{"observed_at":"2026-08-16T11:20:18.705716Z","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":"2026-08-16T11:20:19.087962Z","title":"Optimizing llm based retrieval augmented generation pipelines in the financial domain","venue":null,"work_id":"4a5f87a8-1380-4a2f-b420-5ed0c0d4d7c0","year":2024},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.709187Z"},"links":{"citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:12169d2766505847b636d9a6c06bbc09f814acdc87dcc37236b201bfc0fd7a7f","observation_id":"2bcbd48e-3e92-4270-96cc-73527256e78d","resolution":{"observed_at":"2026-08-16T11:20:19.093296Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.07624","last_updated":"2021-06-01T05:38:50Z","snapshot_observed_at":"2026-08-16T18:24:20.042360Z","submitted_at":"2021-05-17T06:12:06Z","title":"TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.07624","snapshot_observed_at":"2026-08-16T11:20:18.713089Z","title":"Tat-qa: A question answering benchmark on a hybrid of tabular and textual content in finance","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.713089Z"},"links":{"cited_paper":"/paper/2105.07624","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:b91d6d1083115f3d94ccc0ea77a2360ed957413f30233665a882075b9427dd04","observation_id":"5a17b970-b196-475e-a66b-129b2274e600","resolution":{"observed_at":"2026-08-16T11:20:18.713089Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.15217","last_updated":"2025-04-28T05:09:12Z","snapshot_observed_at":"2026-08-13T16:22:14.977210Z","submitted_at":"2023-09-26T19:23:54Z","title":"Ragas: Automated Evaluation of Retrieval Augmented Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.15217","snapshot_observed_at":"2026-08-16T11:20:18.608974Z","title":"Ragas: Automated evaluation of retrieval augmented generation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":1996,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.608974Z"},"links":{"cited_paper":"/paper/2309.15217","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:c78fc72eff8b692892614fdcf7278f3696ba7c7a81bc32145a1dfa401329e46b","observation_id":"8cffec56-d2a2-45bf-b55e-4d6a8a467962","resolution":{"observed_at":"2026-08-16T11:20:18.608974Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02244","last_updated":"2024-05-29T13:38:25Z","snapshot_observed_at":"2026-08-16T14:21:49.943376Z","submitted_at":"2024-02-03T19:20:02Z","title":"Beyond the Limits: A Survey of Techniques to Extend the Context Length in Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02244","snapshot_observed_at":"2026-08-16T11:20:18.698507Z","title":"Beyond the limits: A survey of techniques to extend the context length in large language models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":2011,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.698507Z"},"links":{"cited_paper":"/paper/2402.02244","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:056b34e997bf98d8474dd386082936a59edd3468c8d85a7ec22345b00a44ce5f","observation_id":"9bee7ab7-7974-48da-b573-9bd709a0e22d","resolution":{"observed_at":"2026-08-16T11:20:18.698507Z","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":"2026-08-16T14:04:53.635805Z","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":"2026-08-16T11:20:18.583316Z","title":"Rq-rag: Learning to refine queries for retrieval augmented generation","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":2012,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.583316Z"},"links":{"cited_paper":"/paper/2404.00610","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:f1f3ee3dd39d6b0f640727313151b7621490224550513de8793495862bcc8de5","observation_id":"95ec30d8-46bd-4dee-ac0e-56d99cda7b2e","resolution":{"observed_at":"2026-08-16T11:20:18.583316Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.15594","last_updated":"2025-10-19T10:32:43Z","snapshot_observed_at":"2026-08-13T06:43:22.011336Z","submitted_at":"2024-11-23T16:03:35Z","title":"A Survey on LLM-as-a-Judge","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.15594","snapshot_observed_at":"2026-08-16T11:20:18.624131Z","title":"A survey on llm-as-a-judge","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.624131Z"},"links":{"cited_paper":"/paper/2411.15594","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:6d7037e57713aeb42c3c8927c6ea3403809206fbc0146aedbfe483e0aca3ee1a","observation_id":"b8a9d0c1-8e2f-4a1c-8491-887147a95685","resolution":{"observed_at":"2026-08-16T11:20:18.624131Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.02839","last_updated":"2025-05-30T12:01:03Z","snapshot_observed_at":"2026-08-16T14:12:43.451038Z","submitted_at":"2024-03-05T10:20:52Z","title":"An Empirical Study of LLM-as-a-Judge for LLM Evaluation: Fine-tuned Judge Model is not a General Substitute for GPT-4","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.02839","snapshot_observed_at":"2026-08-16T11:20:18.631186Z","title":"An empirical study of llm-as- a-judge for llm evaluation: Fine-tuned judge models are task-specific classifiers","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.631186Z"},"links":{"cited_paper":"/paper/2403.02839","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:dc6b626110e3704b2f89bd40d746a903d29d6bb74f204aba7be5b60996d8bd50","observation_id":"cf976ba1-edc4-46d4-bc82-8340d2bdc577","resolution":{"observed_at":"2026-08-16T11:20:18.631186Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.05922","last_updated":"2023-09-12T02:34:06Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-09-12T02:34:06Z","title":"A Survey of Hallucination in Large Foundation Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.05922","snapshot_observed_at":"2026-08-16T11:20:18.671658Z","title":"A survey of hallucination in large foundation models","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.671658Z"},"links":{"cited_paper":"/paper/2309.05922","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:58a5c85ad22e3548dc08d9cddd656f8ad5c70f57d8b52c07a19b2186dab9ea13","observation_id":"82885303-b89b-424a-bee3-b9d74d18c63b","resolution":{"observed_at":"2026-08-16T11:20:18.671658Z","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":"2026-08-16T11:20:18.620320Z","title":"Retrieval-augmented generation for large language models: A survey","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.620320Z"},"links":{"cited_paper":"/paper/2312.10997","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:2028b9a14fa20bf01106e6b77ba78192da0d2ff1fe5fe7d29c47f98c37d1f78c","observation_id":"e46bd1bc-5445-42ab-8004-500472c9b65b","resolution":{"observed_at":"2026-08-16T11:20:18.620320Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.03849","last_updated":"2022-10-07T23:48:50Z","snapshot_observed_at":"2026-08-16T16:25:58.896620Z","submitted_at":"2022-10-07T23:48:50Z","title":"ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.03849","snapshot_observed_at":"2026-08-16T11:20:18.596463Z","title":"Convfinqa: Exploring the chain of numerical reasoning in conversational finance question an- swering","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.596463Z"},"links":{"cited_paper":"/paper/2210.03849","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:04dc186fb0737ec576c73fcc5d947f60f604e9650af8c45db59d2bc8a31fb62a","observation_id":"4fdd40fe-8f13-4cef-9c5c-c4a89099a0f5","resolution":{"observed_at":"2026-08-16T11:20:18.596463Z","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":"2026-08-16T11:20:19.146855Z","title":"Enhancing financial risk analysis using rag-based large language models","venue":null,"work_id":"781663e7-dd72-4906-8358-63b8bc4db0ea","year":2024},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.601128Z"},"links":{"citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:e30e9bacbb7003c1ee105f7fbbc811e444224e6e169c9963a7435b09f99cfb69","observation_id":"1b7b5962-6ccd-4250-a6b4-f17ed9379d8f","resolution":{"observed_at":"2026-08-16T11:20:19.150567Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.07883","last_updated":"2024-01-15T18:25:18Z","snapshot_observed_at":"2026-08-16T14:27:21.836794Z","submitted_at":"2024-01-15T18:25:18Z","title":"The Chronicles of RAG: The Retriever, the Chunk and the Generator","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.07883","snapshot_observed_at":"2026-08-16T11:20:18.613013Z","title":"The chronicles of rag: The retriever, the chunk and the genera- tor","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.613013Z"},"links":{"cited_paper":"/paper/2401.07883","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:8d13bf97e5695222757aad862b0ab9dd3bc9ecb92fa3d820e49f8dcb04a3154d","observation_id":"3e0b9bae-d628-4821-b912-a496162c0ea9","resolution":{"observed_at":"2026-08-16T11:20:18.613013Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.09980","last_updated":"2024-05-16T10:53:31Z","snapshot_observed_at":"2026-08-16T13:52:13.314260Z","submitted_at":"2024-05-16T10:53:31Z","title":"FinTextQA: A Dataset for Long-form Financial Question Answering","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.09980","snapshot_observed_at":"2026-08-16T11:20:18.588007Z","title":"Fintextqa: A dataset for long-form financial question answering","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.588007Z"},"links":{"cited_paper":"/paper/2405.09980","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:205e7b1c6e982b5cec7dc82f600bef8169896b392490cdb91838a6cfeeff5128","observation_id":"e7e66e24-5574-496c-a9a9-7f725a4ba9a3","resolution":{"observed_at":"2026-08-16T11:20:18.588007Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.01880","last_updated":"2024-12-27T14:34:37Z","snapshot_observed_at":"2026-08-15T17:50:28.000214Z","submitted_at":"2024-12-27T14:34:37Z","title":"Long Context vs. RAG for LLMs: An Evaluation and Revisits","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.01880","snapshot_observed_at":"2026-08-16T11:20:18.660243Z","title":"Long context vs","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation","version":3},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-16T11:20:18.660243Z"},"links":{"cited_paper":"/paper/2501.01880","citing_paper":"/paper/2504.15800"},"observation_digest":"sha256:86e956df8fa534293e379681b45b504cd599799e17be4e62a144768b6a5de10b","observation_id":"33c68203-7d39-4d3c-925f-141eb80dd726","resolution":{"observed_at":"2026-08-16T11:20:18.660243Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2504.15800","last_updated":"2025-09-03T07:11:43Z","latest_version":3,"primary_category":"cs.IR","snapshot_observed_at":"2026-08-16T13:16:56.997474Z","submitted_at":"2025-04-22T11:30:13Z","title":"FinDER: Financial Dataset for Question Answering and Evaluating Retrieval-Augmented Generation"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":30,"verified_exact":0,"verified_fuzzy":6},"total_outbound_references":36},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 7 inbound Pith citation observations for arXiv:2504.15800."}