{"as_of":"2026-08-09T23:06:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a67fe80a93c50321aa5eff5f4c90cd8e02b562d9f89c4c3db6e81ff9f57b6851","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":26,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":26,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":26,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":26,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T21:51:00.494082Z","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":"2026-07-04T14:19:54.995576Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2006.08097","last_updated":"2020-07-09T02:50:04Z","snapshot_observed_at":"2026-08-07T23:26:50.033424Z","submitted_at":"2020-06-15T02:51:06Z","title":"FinBERT: A Pretrained Language Model for Financial Communications","version":2},"cited_work":{"arxiv_id":"2006.08097","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.08097","snapshot_observed_at":"2026-07-04T14:19:54.995576Z","title":"Finbert: A pretrained language model for financial communications","venue":null,"work_id":"22124ce1-7366-4710-8a84-626d77a1b4b0","year":2020},"citing_paper":{"arxiv_id":"2406.12009","last_updated":"2026-04-09T08:47:41Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-06-17T18:25:02Z","title":"FinTruthQA: A Benchmark for AI-Driven Financial Disclosure Quality Assessment in Investor -- Firm Interactions","version":5},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-23T23:55:14.855176Z"},"links":{"cited_paper":"/paper/2006.08097","citing_paper":"/paper/2406.12009"},"observation_digest":"sha256:33102a477414f364506983286e2b715add27fa265a2aae165768810a325110f9","observation_id":"e4b50948-6d36-42af-aa5d-9c3a8e736928","resolution":{"observed_at":"2026-05-23T23:55:53.560546Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.08097","last_updated":"2020-07-09T02:50:04Z","snapshot_observed_at":"2026-08-07T23:26:50.033424Z","submitted_at":"2020-06-15T02:51:06Z","title":"FinBERT: A Pretrained Language Model for Financial Communications","version":2},"cited_work":{"arxiv_id":"2006.08097","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.08097","snapshot_observed_at":"2026-07-04T14:19:54.995576Z","title":"Finbert: A pretrained language model for financial communications","venue":null,"work_id":"22124ce1-7366-4710-8a84-626d77a1b4b0","year":2020},"citing_paper":{"arxiv_id":"2411.10915","last_updated":"2026-05-01T02:07:46Z","snapshot_observed_at":"2026-07-06T19:51:28.494860Z","submitted_at":"2024-11-16T23:54:53Z","title":"Bias in Large Language Models: Origin, Evaluation, and Mitigation","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-05-23T17:08:09.267577Z"},"links":{"cited_paper":"/paper/2006.08097","citing_paper":"/paper/2411.10915"},"observation_digest":"sha256:ea23265d369e9943779db30954c601a9e0b8ffecc6c8ecf3180539a183c4004b","observation_id":"fa6159b8-0747-4562-b0b8-0e079c44a7cd","resolution":{"observed_at":"2026-05-23T17:08:12.312796Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.08097","last_updated":"2020-07-09T02:50:04Z","snapshot_observed_at":"2026-08-07T23:26:50.033424Z","submitted_at":"2020-06-15T02:51:06Z","title":"FinBERT: A Pretrained Language Model for Financial Communications","version":2},"cited_work":{"arxiv_id":"2006.08097","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.08097","snapshot_observed_at":"2026-07-04T14:19:54.995576Z","title":"Finbert: A pretrained language model for financial communications","venue":null,"work_id":"22124ce1-7366-4710-8a84-626d77a1b4b0","year":2020},"citing_paper":{"arxiv_id":"2503.22693","last_updated":"2026-05-20T06:35:51Z","snapshot_observed_at":"2026-08-02T19:43:11.453664Z","submitted_at":"2025-03-14T01:35:20Z","title":"Bridging Language Models and Financial Analysis","version":2},"reference_index":114,"source":"pdf_text","source_observed_at":"2026-05-23T01:08:58.528533Z"},"links":{"cited_paper":"/paper/2006.08097","citing_paper":"/paper/2503.22693"},"observation_digest":"sha256:72390d62cdf994efce434fcbdbafcb2f605b94b99243b707701fc53b49882ca4","observation_id":"497e94cf-7160-49c1-acf8-43fedee7c3a0","resolution":{"observed_at":"2026-05-23T01:12:20.752378Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.08097","last_updated":"2020-07-09T02:50:04Z","snapshot_observed_at":"2026-08-07T23:26:50.033424Z","submitted_at":"2020-06-15T02:51:06Z","title":"FinBERT: A Pretrained Language Model for Financial Communications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.08097","snapshot_observed_at":"2026-08-06T21:51:00.494082Z","title":"arXiv Prepr","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.01991","last_updated":"2025-06-29T09:33:29Z","snapshot_observed_at":"2026-08-07T23:27:29.149767Z","submitted_at":"2025-06-29T09:33:29Z","title":"FinAI-BERT: A Transformer-Based Model for Sentence-Level Detection of AI Disclosures in Financial Reports","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T21:51:00.494082Z"},"links":{"cited_paper":"/paper/2006.08097","citing_paper":"/paper/2507.01991"},"observation_digest":"sha256:19c153915686c541fe462889623b7546ea9feb60551e7d0ae9914c1c25ffd007","observation_id":"94fe8fc2-98f3-4c10-ae27-6d71a598b146","resolution":{"observed_at":"2026-08-06T21:51:00.494082Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.08097","last_updated":"2020-07-09T02:50:04Z","snapshot_observed_at":"2026-08-07T23:26:50.033424Z","submitted_at":"2020-06-15T02:51:06Z","title":"FinBERT: A Pretrained Language Model for Financial Communications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.08097","snapshot_observed_at":"2026-08-06T19:29:10.393579Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.05617","last_updated":"2025-07-08T02:54:15Z","snapshot_observed_at":"2026-08-07T03:43:31.678279Z","submitted_at":"2025-07-08T02:54:15Z","title":"Flipping Knowledge Distillation: Leveraging Small Models' Expertise to Enhance LLMs in Text Matching","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-06T19:29:10.393579Z"},"links":{"cited_paper":"/paper/2006.08097","citing_paper":"/paper/2507.05617"},"observation_digest":"sha256:53344581840ade8310458ddb133a3e4f5ea9cc790bd953358cd7a1cd12567c61","observation_id":"e20102b8-adbb-4323-a828-5445422094b0","resolution":{"observed_at":"2026-08-06T19:29:10.393579Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.08097","last_updated":"2020-07-09T02:50:04Z","snapshot_observed_at":"2026-08-07T23:26:50.033424Z","submitted_at":"2020-06-15T02:51:06Z","title":"FinBERT: A Pretrained Language Model for Financial Communications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.08097","snapshot_observed_at":"2026-08-06T17:54:17.290286Z","title":"Finbert: A pretrained language model for financial communications","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.09739","last_updated":"2025-07-13T18:30:57Z","snapshot_observed_at":"2026-08-09T13:36:49.615440Z","submitted_at":"2025-07-13T18:30:57Z","title":"Enhancing Trading Performance Through Sentiment Analysis with Large Language Models: Evidence from the S&P 500","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-06T17:54:17.290286Z"},"links":{"cited_paper":"/paper/2006.08097","citing_paper":"/paper/2507.09739"},"observation_digest":"sha256:a49d29275b714a5af949cff2403fa58d9bb2b8f9cb5077ffe3291e5bd99144c4","observation_id":"6facb2b6-a10c-4ba7-a16c-50648d151a22","resolution":{"observed_at":"2026-08-06T17:54:17.290286Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.08097","last_updated":"2020-07-09T02:50:04Z","snapshot_observed_at":"2026-08-07T23:26:50.033424Z","submitted_at":"2020-06-15T02:51:06Z","title":"FinBERT: A Pretrained Language Model for Financial Communications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.08097","snapshot_observed_at":"2026-08-06T15:12:49.975955Z","title":"Finbert: A pretrained language model for financial communications,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2507.16642","last_updated":"2025-07-22T14:39:54Z","snapshot_observed_at":"2026-08-07T20:46:18.623735Z","submitted_at":"2025-07-22T14:39:54Z","title":"Towards Automated Regulatory Compliance Verification in Financial Auditing with Large Language Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T15:12:49.975955Z"},"links":{"cited_paper":"/paper/2006.08097","citing_paper":"/paper/2507.16642"},"observation_digest":"sha256:521cfcabe1a4ded47f0e32436776c23c959d8504141d7ce3da68b6b54d9b5bd9","observation_id":"16b61866-9484-4657-9cc7-25191e18e662","resolution":{"observed_at":"2026-08-06T15:12:49.975955Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.08097","last_updated":"2020-07-09T02:50:04Z","snapshot_observed_at":"2026-08-07T23:26:50.033424Z","submitted_at":"2020-06-15T02:51:06Z","title":"FinBERT: A Pretrained Language Model for Financial Communications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.08097","snapshot_observed_at":"2026-08-06T14:59:41.111956Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.17186","last_updated":"2025-07-31T08:14:21Z","snapshot_observed_at":"2026-08-07T21:24:43.546217Z","submitted_at":"2025-07-23T04:19:16Z","title":"FinGAIA: A Chinese Benchmark for AI Agents in Real-World Financial Domain","version":2},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-06T14:59:41.111956Z"},"links":{"cited_paper":"/paper/2006.08097","citing_paper":"/paper/2507.17186"},"observation_digest":"sha256:12d7fb1b625c94d339220d0b6c752006d4097770080a2036a78d2620af05ea6e","observation_id":"e523452a-239c-4d2d-8769-3be0c3b5074c","resolution":{"observed_at":"2026-08-06T14:59:41.111956Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.08097","last_updated":"2020-07-09T02:50:04Z","snapshot_observed_at":"2026-08-07T23:26:50.033424Z","submitted_at":"2020-06-15T02:51:06Z","title":"FinBERT: A Pretrained Language Model for Financial Communications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.08097","snapshot_observed_at":"2026-08-05T15:02:43.764763Z","title":"FinBERT: A Pretrained Language Model for Financial Communications,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.20622","last_updated":"2025-08-28T10:13:33Z","snapshot_observed_at":"2026-08-06T19:35:04.527331Z","submitted_at":"2025-08-28T10:13:33Z","title":"Masked Autoencoders for Ultrasound Signals: Robust Representation Learning for Downstream Applications","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T15:02:43.764763Z"},"links":{"cited_paper":"/paper/2006.08097","citing_paper":"/paper/2508.20622"},"observation_digest":"sha256:b57cc071c2ec407cf1ee8064c2ada826085ac3d1493a22edf41ab5f0700222ba","observation_id":"6626a5d6-c595-4e04-8f98-978e604098a5","resolution":{"observed_at":"2026-08-05T15:02:43.764763Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.08097","last_updated":"2020-07-09T02:50:04Z","snapshot_observed_at":"2026-08-07T23:26:50.033424Z","submitted_at":"2020-06-15T02:51:06Z","title":"FinBERT: A Pretrained Language Model for Financial Communications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.08097","snapshot_observed_at":"2026-08-04T09:26:25.816555Z","title":null,"venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2510.15416","last_updated":"2026-06-03T14:44:26Z","snapshot_observed_at":"2026-08-07T05:47:14.662115Z","submitted_at":"2025-10-17T08:10:06Z","title":"Adaptive Minds: Empowering Agents with LoRA-as-Tools","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T09:26:25.816555Z"},"links":{"cited_paper":"/paper/2006.08097","citing_paper":"/paper/2510.15416"},"observation_digest":"sha256:c21064445cc496c40b1cf67be3201769e0924f237d40b752f0628c541f959f12","observation_id":"7a393b73-f89d-4d5c-b89e-cc8c139fcf28","resolution":{"observed_at":"2026-08-04T09:26:25.816555Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.08097","last_updated":"2020-07-09T02:50:04Z","snapshot_observed_at":"2026-08-07T23:26:50.033424Z","submitted_at":"2020-06-15T02:51:06Z","title":"FinBERT: A Pretrained Language Model for Financial Communications","version":2},"cited_work":{"arxiv_id":"2006.08097","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.08097","snapshot_observed_at":"2026-07-04T14:19:54.995576Z","title":"Finbert: A pretrained language model for financial communications","venue":null,"work_id":"22124ce1-7366-4710-8a84-626d77a1b4b0","year":2020},"citing_paper":{"arxiv_id":"2512.13040","last_updated":"2026-04-08T20:16:25Z","snapshot_observed_at":"2026-08-08T10:11:57.261099Z","submitted_at":"2025-12-15T07:09:11Z","title":"Understanding Structured Financial Data with LLMs: A Case Study on Fraud Detection","version":2},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-05-16T21:59:13.588901Z"},"links":{"cited_paper":"/paper/2006.08097","citing_paper":"/paper/2512.13040"},"observation_digest":"sha256:2377241f4d703aee7e75c3f5eb9ae31344b4b381b7edb4aff1f6f746448620be","observation_id":"2c0ca148-6d24-47f4-bb4f-dae5313fad0f","resolution":{"observed_at":"2026-05-16T22:01:17.940146Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.08097","last_updated":"2020-07-09T02:50:04Z","snapshot_observed_at":"2026-08-07T23:26:50.033424Z","submitted_at":"2020-06-15T02:51:06Z","title":"FinBERT: A Pretrained Language Model for Financial Communications","version":2},"cited_work":{"arxiv_id":"2006.08097","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.08097","snapshot_observed_at":"2026-07-04T14:19:54.995576Z","title":"Finbert: A pretrained language model for financial communications","venue":null,"work_id":"22124ce1-7366-4710-8a84-626d77a1b4b0","year":2020},"citing_paper":{"arxiv_id":"2604.08649","last_updated":"2026-04-09T18:00:00Z","snapshot_observed_at":"2026-07-06T22:57:40.773778Z","submitted_at":"2026-04-09T18:00:00Z","title":"PRAGMA: Revolut Foundation Model","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-10T17:37:04.381074Z"},"links":{"cited_paper":"/paper/2006.08097","citing_paper":"/paper/2604.08649"},"observation_digest":"sha256:d51bca3c5c38fece7cfebc37941f3e217cd4d20f76f750cca7e7560bfe508d8a","observation_id":"6e3969a6-2495-4ed8-899b-dc28b04204a2","resolution":{"observed_at":"2026-05-11T06:30:59.070198Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.08097","last_updated":"2020-07-09T02:50:04Z","snapshot_observed_at":"2026-08-07T23:26:50.033424Z","submitted_at":"2020-06-15T02:51:06Z","title":"FinBERT: A Pretrained Language Model for Financial Communications","version":2},"cited_work":{"arxiv_id":"2006.08097","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.08097","snapshot_observed_at":"2026-07-04T14:19:54.995576Z","title":"Finbert: A pretrained language model for financial communications","venue":null,"work_id":"22124ce1-7366-4710-8a84-626d77a1b4b0","year":2020},"citing_paper":{"arxiv_id":"2604.12047","last_updated":"2026-04-13T20:39:43Z","snapshot_observed_at":"2026-07-06T23:00:17.634307Z","submitted_at":"2026-04-13T20:39:43Z","title":"Empirical Evaluation of PDF Parsing and Chunking for Financial Question Answering with RAG","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-10T15:17:27.749792Z"},"links":{"cited_paper":"/paper/2006.08097","citing_paper":"/paper/2604.12047"},"observation_digest":"sha256:429219bb13996fc48e5452db5eb59771d4db1d2258a40b2254609d6bf92eb4a3","observation_id":"5c6a423f-a378-4b47-bf0f-58d4ace304e2","resolution":{"observed_at":"2026-05-11T10:51:03.902699Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.08097","last_updated":"2020-07-09T02:50:04Z","snapshot_observed_at":"2026-08-07T23:26:50.033424Z","submitted_at":"2020-06-15T02:51:06Z","title":"FinBERT: A Pretrained Language Model for Financial Communications","version":2},"cited_work":{"arxiv_id":"2006.08097","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.08097","snapshot_observed_at":"2026-07-04T14:19:54.995576Z","title":"Finbert: A pretrained language model for financial communications","venue":null,"work_id":"22124ce1-7366-4710-8a84-626d77a1b4b0","year":2020},"citing_paper":{"arxiv_id":"2604.16411","last_updated":"2026-04-01T14:06:53Z","snapshot_observed_at":"2026-07-06T23:03:43.854612Z","submitted_at":"2026-04-01T14:06:53Z","title":"CGCMA: Conditionally-Gated Cross-Modal Attention for Event-Conditioned Asynchronous Fusion","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-13T22:36:26.629041Z"},"links":{"cited_paper":"/paper/2006.08097","citing_paper":"/paper/2604.16411"},"observation_digest":"sha256:81f95d68fc815749aba30599fc7e96d31448118a6a33a25c34913f4da03b2d92","observation_id":"13dd0285-b2be-4233-81ec-ba0cab00bb15","resolution":{"observed_at":"2026-05-13T22:38:22.158975Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.08097","last_updated":"2020-07-09T02:50:04Z","snapshot_observed_at":"2026-08-07T23:26:50.033424Z","submitted_at":"2020-06-15T02:51:06Z","title":"FinBERT: A Pretrained Language Model for Financial Communications","version":2},"cited_work":{"arxiv_id":"2006.08097","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.08097","snapshot_observed_at":"2026-07-04T14:19:54.995576Z","title":"Finbert: A pretrained language model for financial communications","venue":null,"work_id":"22124ce1-7366-4710-8a84-626d77a1b4b0","year":2020},"citing_paper":{"arxiv_id":"2605.01384","last_updated":"2026-05-02T11:16:01Z","snapshot_observed_at":"2026-07-06T23:14:38.379875Z","submitted_at":"2026-05-02T11:16:01Z","title":"SBCA: Cross-Modal BERT-driven Actor-Critic for Multi-Asset Portfolio Optimization","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-05-10T15:31:21.815468Z"},"links":{"cited_paper":"/paper/2006.08097","citing_paper":"/paper/2605.01384"},"observation_digest":"sha256:fa9df90c92834dfe003a1a544bc398b9fc73999e6f06f6017286b2eb163b0481","observation_id":"a67e08da-b78f-489b-81ac-1ffc293f3bc0","resolution":{"observed_at":"2026-05-11T10:21:04.416490Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.08097","last_updated":"2020-07-09T02:50:04Z","snapshot_observed_at":"2026-08-07T23:26:50.033424Z","submitted_at":"2020-06-15T02:51:06Z","title":"FinBERT: A Pretrained Language Model for Financial Communications","version":2},"cited_work":{"arxiv_id":"2006.08097","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.08097","snapshot_observed_at":"2026-07-04T14:19:54.995576Z","title":"Finbert: A pretrained language model for financial communications","venue":null,"work_id":"22124ce1-7366-4710-8a84-626d77a1b4b0","year":2020},"citing_paper":{"arxiv_id":"2605.05409","last_updated":"2026-07-05T14:58:21Z","snapshot_observed_at":"2026-08-04T08:24:08.386615Z","submitted_at":"2026-05-06T19:59:51Z","title":"Agentic Retrieval-Augmented Generation for Financial Document Question Answering","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-05-08T17:03:09.496490Z"},"links":{"cited_paper":"/paper/2006.08097","citing_paper":"/paper/2605.05409"},"observation_digest":"sha256:ff9ab84d644b48a98066eb1f45d0b23a7a6d447cca0d39f32ad1db078d611eb6","observation_id":"deea5d07-d548-41a8-9df4-cd6bba3d620a","resolution":{"observed_at":"2026-05-11T17:51:08.308810Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.08097","last_updated":"2020-07-09T02:50:04Z","snapshot_observed_at":"2026-08-07T23:26:50.033424Z","submitted_at":"2020-06-15T02:51:06Z","title":"FinBERT: A Pretrained Language Model for Financial Communications","version":2},"cited_work":{"arxiv_id":"2006.08097","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.08097","snapshot_observed_at":"2026-07-04T14:19:54.995576Z","title":"Finbert: A pretrained language model for financial communications","venue":null,"work_id":"22124ce1-7366-4710-8a84-626d77a1b4b0","year":2020},"citing_paper":{"arxiv_id":"2605.15092","last_updated":"2026-05-14T17:12:22Z","snapshot_observed_at":"2026-08-03T01:01:17.589865Z","submitted_at":"2026-05-14T17:12:22Z","title":"Monetary Policy in the Media Spotlight: Sentiments, Signals, and Economic Impact","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-05-15T02:58:27.599800Z"},"links":{"cited_paper":"/paper/2006.08097","citing_paper":"/paper/2605.15092"},"observation_digest":"sha256:72f913c5324fc6e87367db9204ab92006fd35a0644be9c55291e69b83e51f9ee","observation_id":"4f3b4f12-f5d8-4252-afe0-1ea8eccc6bbd","resolution":{"observed_at":"2026-05-15T02:58:33.852649Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.08097","last_updated":"2020-07-09T02:50:04Z","snapshot_observed_at":"2026-08-07T23:26:50.033424Z","submitted_at":"2020-06-15T02:51:06Z","title":"FinBERT: A Pretrained Language Model for Financial Communications","version":2},"cited_work":{"arxiv_id":"2006.08097","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.08097","snapshot_observed_at":"2026-07-04T14:19:54.995576Z","title":"Finbert: A pretrained language model for financial communications","venue":null,"work_id":"22124ce1-7366-4710-8a84-626d77a1b4b0","year":2020},"citing_paper":{"arxiv_id":"2605.24910","last_updated":"2026-05-24T07:31:34Z","snapshot_observed_at":"2026-08-09T12:04:09.964528Z","submitted_at":"2026-05-24T07:31:34Z","title":"Noise-Robust Financial Numerical Entity Attribute Tagging","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-06-30T11:47:43.473337Z"},"links":{"cited_paper":"/paper/2006.08097","citing_paper":"/paper/2605.24910"},"observation_digest":"sha256:bf5c8fd0619c1a36c17df9d1272105dbc036d470eb7bc467d0e97a7a74787154","observation_id":"107dd4a1-ed25-45e4-bb2f-52221d01bb1b","resolution":{"observed_at":"2026-06-30T11:54:38.463120Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.08097","last_updated":"2020-07-09T02:50:04Z","snapshot_observed_at":"2026-08-07T23:26:50.033424Z","submitted_at":"2020-06-15T02:51:06Z","title":"FinBERT: A Pretrained Language Model for Financial Communications","version":2},"cited_work":{"arxiv_id":"2006.08097","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.08097","snapshot_observed_at":"2026-07-04T14:19:54.995576Z","title":"Finbert: A pretrained language model for financial communications","venue":null,"work_id":"22124ce1-7366-4710-8a84-626d77a1b4b0","year":2020},"citing_paper":{"arxiv_id":"2605.26074","last_updated":"2026-05-25T17:38:30Z","snapshot_observed_at":"2026-08-06T16:57:59.468909Z","submitted_at":"2026-05-25T17:38:30Z","title":"StakeBench: Evaluating Language Understanding Grounded in Market Commitment","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-29T21:43:54.536505Z"},"links":{"cited_paper":"/paper/2006.08097","citing_paper":"/paper/2605.26074"},"observation_digest":"sha256:d64c9f948b2098e7abe1d55fc9a30990ab4cfc44b33a492729ef5837b91b31af","observation_id":"69dfe1ae-5795-4e95-a048-77e2d41a0fce","resolution":{"observed_at":"2026-06-29T21:43:58.608572Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.08097","last_updated":"2020-07-09T02:50:04Z","snapshot_observed_at":"2026-08-07T23:26:50.033424Z","submitted_at":"2020-06-15T02:51:06Z","title":"FinBERT: A Pretrained Language Model for Financial Communications","version":2},"cited_work":{"arxiv_id":"2006.08097","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.08097","snapshot_observed_at":"2026-07-04T14:19:54.995576Z","title":"Finbert: A pretrained language model for financial communications","venue":null,"work_id":"22124ce1-7366-4710-8a84-626d77a1b4b0","year":2020},"citing_paper":{"arxiv_id":"2606.05868","last_updated":"2026-06-04T08:44:37Z","snapshot_observed_at":"2026-08-01T15:27:39.234203Z","submitted_at":"2026-06-04T08:44:37Z","title":"YouZhi: Towards High-Concurrency Financial LLMs via Adaptive GQA-to-MLA Transition","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-06-28T01:33:54.383507Z"},"links":{"cited_paper":"/paper/2006.08097","citing_paper":"/paper/2606.05868"},"observation_digest":"sha256:f9961b5279101986b394f5a91a87604b978dd6f7be7d9a00bd5eb86e8e27d8cd","observation_id":"6d9e0d3e-3dd7-42b3-a487-931ce112f6d4","resolution":{"observed_at":"2026-07-02T13:06:59.441669Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.08097","last_updated":"2020-07-09T02:50:04Z","snapshot_observed_at":"2026-08-07T23:26:50.033424Z","submitted_at":"2020-06-15T02:51:06Z","title":"FinBERT: A Pretrained Language Model for Financial Communications","version":2},"cited_work":{"arxiv_id":"2006.08097","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.08097","snapshot_observed_at":"2026-07-04T14:19:54.995576Z","title":"Finbert: A pretrained language model for financial communications","venue":null,"work_id":"22124ce1-7366-4710-8a84-626d77a1b4b0","year":2020},"citing_paper":{"arxiv_id":"2606.18875","last_updated":"2026-06-17T09:52:39Z","snapshot_observed_at":"2026-07-06T23:54:13.575009Z","submitted_at":"2026-06-17T09:52:39Z","title":"Efficient Financial Language Understanding via Distillation with Synthetic Data","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-26T21:01:00.910007Z"},"links":{"cited_paper":"/paper/2006.08097","citing_paper":"/paper/2606.18875"},"observation_digest":"sha256:0d7817fab2d584eb1758550e8c76d63c0a1ab0a20811c8d6e04031f2b4ec5246","observation_id":"33ebc1e7-02d0-486e-a76f-9b690d1c4c9d","resolution":{"observed_at":"2026-07-04T00:49:17.747423Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.08097","last_updated":"2020-07-09T02:50:04Z","snapshot_observed_at":"2026-08-07T23:26:50.033424Z","submitted_at":"2020-06-15T02:51:06Z","title":"FinBERT: A Pretrained Language Model for Financial Communications","version":2},"cited_work":{"arxiv_id":"2006.08097","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.08097","snapshot_observed_at":"2026-07-04T14:19:54.995576Z","title":"Finbert: A pretrained language model for financial communications","venue":null,"work_id":"22124ce1-7366-4710-8a84-626d77a1b4b0","year":2020},"citing_paper":{"arxiv_id":"2606.27316","last_updated":"2026-06-25T17:29:58Z","snapshot_observed_at":"2026-07-31T08:21:54.248070Z","submitted_at":"2026-06-25T17:29:58Z","title":"LLM-Based Examination of Eligibility Criteria from Securities Prospectuses at the German Central Bank","version":1},"reference_index":86,"source":"arxiv_source","source_observed_at":"2026-06-26T03:56:28.271760Z"},"links":{"cited_paper":"/paper/2006.08097","citing_paper":"/paper/2606.27316"},"observation_digest":"sha256:1fc6c35338c94344c3600b03f191e1749748cc6e5d7b59de1adbcd106820f93a","observation_id":"46dc2f76-cc4d-4334-bbf5-32e7b1d48753","resolution":{"observed_at":"2026-07-04T14:19:54.997035Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.08097","last_updated":"2020-07-09T02:50:04Z","snapshot_observed_at":"2026-08-07T23:26:50.033424Z","submitted_at":"2020-06-15T02:51:06Z","title":"FinBERT: A Pretrained Language Model for Financial Communications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.08097","snapshot_observed_at":"2026-08-01T11:45:22.570508Z","title":"FinBERT: A pretrained language model for financial communications","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.19794","last_updated":"2026-07-22T06:20:32Z","snapshot_observed_at":"2026-08-06T20:26:21.885501Z","submitted_at":"2026-07-22T06:20:32Z","title":"TriAgent: Divergence-Aware Multi-Agent Committees for Cost-Efficient Financial Sentiment Analysis","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-01T11:45:22.570508Z"},"links":{"cited_paper":"/paper/2006.08097","citing_paper":"/paper/2607.19794"},"observation_digest":"sha256:44689a749331af407d355ac366d13f244b811dcf712707c2603c215618dd63df","observation_id":"9d211644-a29c-44fc-87b2-d899f829e179","resolution":{"observed_at":"2026-08-01T11:45:22.570508Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.08097","last_updated":"2020-07-09T02:50:04Z","snapshot_observed_at":"2026-08-07T23:26:50.033424Z","submitted_at":"2020-06-15T02:51:06Z","title":"FinBERT: A Pretrained Language Model for Financial Communications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.08097","snapshot_observed_at":"2026-07-31T23:40:54.943203Z","title":"FinBERT: A pretrained language model for financial communications,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.23370","last_updated":"2026-07-25T21:25:12Z","snapshot_observed_at":"2026-08-07T23:27:08.198556Z","submitted_at":"2026-07-25T21:25:12Z","title":"Bitcoin Price Direction Prediction via Regime-Aware Multi-Modal Fusion of Social Sentiment and Technical Features","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-31T23:40:54.943203Z"},"links":{"cited_paper":"/paper/2006.08097","citing_paper":"/paper/2607.23370"},"observation_digest":"sha256:e84c51ee10e28d5d3e2d66ca42294cb032fd73b18a684078d0731daa7da00f82","observation_id":"201b8fd1-a107-44b3-b5c9-9815a51c19fb","resolution":{"observed_at":"2026-07-31T23:40:54.943203Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.08097","last_updated":"2020-07-09T02:50:04Z","snapshot_observed_at":"2026-08-07T23:26:50.033424Z","submitted_at":"2020-06-15T02:51:06Z","title":"FinBERT: A Pretrained Language Model for Financial Communications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.08097","snapshot_observed_at":"2026-07-31T23:08:14.935496Z","title":"Finbert: A pretrained language model for financial communications,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.24875","last_updated":"2026-07-27T07:05:22Z","snapshot_observed_at":"2026-08-08T04:05:18.034662Z","submitted_at":"2026-07-27T07:05:22Z","title":"FinAbstain: Uncertainty-Calibrated Multimodal RAG for Selective Financial Forecasting","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-31T23:08:14.935496Z"},"links":{"cited_paper":"/paper/2006.08097","citing_paper":"/paper/2607.24875"},"observation_digest":"sha256:243fce59b5f0e466c9ee0c144c40977600a7f0909f2bcff2f5cfd5724f9064fd","observation_id":"cbde3a70-2df9-4170-9ac2-82cbc8f69e8f","resolution":{"observed_at":"2026-07-31T23:08:14.935496Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.08097","last_updated":"2020-07-09T02:50:04Z","snapshot_observed_at":"2026-08-07T23:26:50.033424Z","submitted_at":"2020-06-15T02:51:06Z","title":"FinBERT: A Pretrained Language Model for Financial Communications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.08097","snapshot_observed_at":"2026-08-01T04:44:50.264038Z","title":"arXiv preprint arXiv:2006.08097","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.27611","last_updated":"2026-07-30T02:58:46Z","snapshot_observed_at":"2026-08-07T23:27:08.722258Z","submitted_at":"2026-07-30T02:58:46Z","title":"AWARE-FX: An Auditable Knowledge-Guided AI System for Measuring Corporate Foreign-Exchange Hedging Disclosure","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-01T04:44:50.264038Z"},"links":{"cited_paper":"/paper/2006.08097","citing_paper":"/paper/2607.27611"},"observation_digest":"sha256:46554437748c28d75199290438638d45165c76f7faef4d3ac77c6a1e543bc2ed","observation_id":"cd1d3c7e-172f-4cec-8f1e-4a285553d12b","resolution":{"observed_at":"2026-08-01T04:44:50.264038Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2006.08097/citation-record","integrity":"/paper/2006.08097/integrity","json":"/paper/2006.08097/citation-record.json","paper":"/paper/2006.08097"},"outbound":[],"paper":{"arxiv_id":"2006.08097","last_updated":"2020-07-09T02:50:04Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-07T23:26:50.033424Z","submitted_at":"2020-06-15T02:51:06Z","title":"FinBERT: A Pretrained Language Model for Financial Communications"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 26 inbound Pith citation observations for arXiv:2006.08097."}