{"as_of":"2026-08-12T12:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:18818288825821538b7fceee6896808a32ec8261eec401517171e87393134879","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":"2026-08-11T19:55:14.151821Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2412.06272/citation-record","integrity":"/paper/2412.06272/integrity","json":"/paper/2412.06272/citation-record.json","paper":"/paper/2412.06272"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-11T19:55:13.877911Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:13.877911Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:46abccbb257d989bc9c9b917008e08e0adefbc60d3287416a2b36b05c8b58101","observation_id":"ad00ddbf-4081-42a4-95b8-309750578c19","resolution":{"observed_at":"2026-08-11T19:55:13.877911Z","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":"2026-08-11T19:55:13.885150Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:13.885150Z"},"links":{"citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:551daae91e7ad5897fc76188c49551a76d921d17def41119f19909616cd703ab","observation_id":"25bad4a2-cf7c-4847-8378-63541e9c2185","resolution":{"observed_at":"2026-08-11T19:55:13.885150Z","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":"2026-08-11T19:55:15.430302Z","title":null,"venue":null,"work_id":"2259936b-6920-4d06-b0a7-02f7c415f444","year":1982},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:13.891078Z"},"links":{"citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:0a18147bbd43a5b0fede3123480a5568caafb298b142e574ec2097a85e103e77","observation_id":"84986135-cd0d-4a6d-a918-63fcd4f7e71d","resolution":{"observed_at":"2026-08-11T19:55:15.444772Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2311.09693","last_updated":"2024-10-17T15:03:11Z","snapshot_observed_at":"2026-08-09T09:40:52.587987Z","submitted_at":"2023-11-16T09:09:22Z","title":"BLT: Can Large Language Models Handle Basic Legal Text?","version":3},"cited_work":{"arxiv_id":"2311.09693","doi":null,"metadata_source":"pith","pith_arxiv_id":"2311.09693","snapshot_observed_at":"2026-08-11T19:55:15.075561Z","title":"BLT: Can Large Language Models Handle Basic Legal Text?","venue":"cs.CL","work_id":"dcbd1fb2-c853-469e-b871-c62c0a72445d","year":2023},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:13.910332Z"},"links":{"cited_paper":"/paper/2311.09693","citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:d412ea16d41b0f11abbe72022dc17c222ba5620dbd2df4625b46003b495d2a36","observation_id":"8477b87d-23f8-44fb-9635-00f7f8cd820f","resolution":{"observed_at":"2026-08-11T19:55:15.081906Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:55:13.917269Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:13.917269Z"},"links":{"citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:8fd2267de9452dd4761da265eb13a53dd8c404c60593ccc7a3bb8f29e01377da","observation_id":"b84dc9b6-f0d3-4bdd-9889-9b4b698ff63e","resolution":{"observed_at":"2026-08-11T19:55:13.917269Z","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":"2026-08-11T19:55:13.924362Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:13.924362Z"},"links":{"citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:852d48f0d74a0f9493909b5f8984ddf5f3f86b8f447b5e900ce587b58c382106","observation_id":"27bbb9ac-3d54-47c6-a102-641e8864e58d","resolution":{"observed_at":"2026-08-11T19:55:13.924362Z","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":"2026-08-11T19:55:15.395561Z","title":null,"venue":null,"work_id":"fc45873c-c1d3-420c-8671-31b050a4e57e","year":2024},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:13.930884Z"},"links":{"citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:5970df23089bfd620ac2b88fbdcf2b32497c2b245733736d98f71517604cb80c","observation_id":"0609b96e-2a31-4c1a-8375-25604064a651","resolution":{"observed_at":"2026-08-11T19:55:15.404242Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T19:55:15.371350Z","title":null,"venue":null,"work_id":"e93b97ff-e921-4398-933c-0d6dbb9ebb15","year":2024},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:13.938041Z"},"links":{"citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:7e59bc5136f4dd2662c60fb7dcfb7f40be258fa0c9755e3047ab9d40ddcb8729","observation_id":"c71d5021-d4d5-4ae9-826c-8c3a0534d8b2","resolution":{"observed_at":"2026-08-11T19:55:15.378483Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.03883","last_updated":"2024-03-07T06:39:32Z","snapshot_observed_at":"2026-08-08T14:59:42.793324Z","submitted_at":"2024-03-06T17:42:16Z","title":"SaulLM-7B: A pioneering Large Language Model for Law","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.03883","snapshot_observed_at":"2026-08-11T19:55:13.944333Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:13.944333Z"},"links":{"cited_paper":"/paper/2403.03883","citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:1fa8b3d064c2205c3b8596f0f65d882a6f148ff017f968b06c8279ee24951f00","observation_id":"7fd9edda-af20-4558-89c2-4279a59918e2","resolution":{"observed_at":"2026-08-11T19:55:13.944333Z","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":"2026-08-11T19:55:15.341142Z","title":null,"venue":null,"work_id":"4d135210-7598-4888-be2c-99ccfa0aaff5","year":2024},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:13.950941Z"},"links":{"citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:9229b24e28415c5224a41e0ab1fd914e6920c3293c0b6e29e505fcc6d099ff55","observation_id":"eebd91a3-50a2-48a5-bdfc-49bac44e6e18","resolution":{"observed_at":"2026-08-11T19:55:15.350333Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-11T19:55:15.308044Z","title":null,"venue":null,"work_id":"c4f6aa42-fcea-4b9e-a5df-303562a3c0a8","year":2024},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:13.958722Z"},"links":{"citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:73a4e215d0cfa2b498fdf55da2ee0449c20f4e521a12fd9e2be076cff3a41dda","observation_id":"6db59eec-60b3-48b8-b0c2-46babcf0a1fe","resolution":{"observed_at":"2026-08-11T19:55:15.315279Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.16615","last_updated":"2025-04-23T12:18:56Z","snapshot_observed_at":"2026-08-11T12:51:50.255443Z","submitted_at":"2024-07-23T16:23:04Z","title":"Lawma: The Power of Specialization for Legal Annotation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.16615","snapshot_observed_at":"2026-08-11T19:55:13.964351Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:13.964351Z"},"links":{"cited_paper":"/paper/2407.16615","citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:45617cea730abff6b7e69b355d9ed22b60d7a3a6421cd6329c734e5e3f61e7c8","observation_id":"e3fadae9-39c8-4af9-9607-9aa681c7478f","resolution":{"observed_at":"2026-08-11T19:55:13.964351Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.16289","last_updated":"2023-09-28T09:35:59Z","snapshot_observed_at":"2026-08-11T15:36:40.479412Z","submitted_at":"2023-09-28T09:35:59Z","title":"LawBench: Benchmarking Legal Knowledge of Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.16289","snapshot_observed_at":"2026-08-11T19:55:13.971200Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:13.971200Z"},"links":{"cited_paper":"/paper/2309.16289","citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:7a5efdb81260a1667f17f931a5c739192dc096de12d1892ad0fc3eff90cac13c","observation_id":"7b6c4b9f-557f-413c-b500-44d1d9e1c34f","resolution":{"observed_at":"2026-08-11T19:55:13.971200Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-08-10T16:40:37.411115Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-08-11T19:55:13.976720Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:13.976720Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:84eade4d9419482eec01faa53a8a22ed3f3c1b3160072d906ad0c874cbc433e3","observation_id":"795e6d99-f7f4-4e7f-85f7-6d4604d8bf95","resolution":{"observed_at":"2026-08-11T19:55:13.976720Z","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":"2026-08-11T19:55:15.277389Z","title":null,"venue":null,"work_id":"a55b3e0e-5be2-45a7-96d0-72aca2a97f3b","year":2024},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:13.982293Z"},"links":{"citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:a3858b00dc66415ccc5066697b396d95db02fd4eb89c6c8aab10f8f035f8ca98","observation_id":"29a8150a-5cf8-441e-a434-7c6a216c65e5","resolution":{"observed_at":"2026-08-11T19:55:15.283544Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.06268","last_updated":"2021-11-08T21:23:22Z","snapshot_observed_at":"2026-08-10T18:46:16.434957Z","submitted_at":"2021-03-10T18:59:34Z","title":"CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.06268","snapshot_observed_at":"2026-08-11T19:55:13.987950Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:13.987950Z"},"links":{"cited_paper":"/paper/2103.06268","citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:ee4e75f561a02f7af3f909ddd28e26f9605831a24fec55370202e4099644fa95","observation_id":"8e0bbbf1-a94d-4cac-bc44-07f8ce159b17","resolution":{"observed_at":"2026-08-11T19:55:13.987950Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.05257","last_updated":"2020-08-12T16:08:43Z","snapshot_observed_at":"2026-08-11T19:14:34.971085Z","submitted_at":"2020-05-11T16:54:42Z","title":"A Dataset for Statutory Reasoning in Tax Law Entailment and Question Answering","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.05257","snapshot_observed_at":"2026-08-11T19:55:13.995166Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:13.995166Z"},"links":{"cited_paper":"/paper/2005.05257","citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:70f289c6d9ef5020738eafcf1c650535d8d77b3ba680862b3593ed801dc2e6b6","observation_id":"3c5ff5a2-1119-413d-9d16-7f1b2524c21f","resolution":{"observed_at":"2026-08-11T19:55:13.995166Z","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":"2026-08-11T19:55:15.256250Z","title":null,"venue":null,"work_id":"8b80028c-8c09-4a2b-bd4f-a8fa150599c8","year":2024},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:14.009821Z"},"links":{"citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:a71ded50d3ff8900372a95df5121411716e922ae092f13e39c0cdd5b474e2bef","observation_id":"2d235d13-40d7-4f81-9988-8c320192b65f","resolution":{"observed_at":"2026-08-11T19:55:15.262407Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.17186","last_updated":"2024-06-27T15:55:57Z","snapshot_observed_at":"2026-07-06T18:36:30.295995Z","submitted_at":"2024-06-24T23:57:57Z","title":"CLERC: A Dataset for Legal Case Retrieval and Retrieval-Augmented Analysis Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.17186","snapshot_observed_at":"2026-08-11T19:55:14.020826Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:14.020826Z"},"links":{"cited_paper":"/paper/2406.17186","citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:6628d02b43496b83bd85963d8e15d7d63c697085efc510c82064f36d081c883f","observation_id":"ccad2d1d-d9a7-4753-b7eb-ad374ee5796d","resolution":{"observed_at":"2026-08-11T19:55:14.020826Z","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":"2026-08-11T19:55:14.030703Z","title":"Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen - Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:14.030703Z"},"links":{"citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:35f15bd1c79b1602dce1b9979fd1cd1a49b12d9884e57142aef4499b5c460001","observation_id":"0677367e-3c7b-4986-b064-5822be78bbaa","resolution":{"observed_at":"2026-08-11T19:55:14.030703Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.06521","last_updated":"2025-01-11T12:08:15Z","snapshot_observed_at":"2026-08-10T20:56:22.899656Z","submitted_at":"2025-01-11T12:08:15Z","title":"Fine-tuning Large Language Models for Improving Factuality in Legal Question Answering","version":1},"cited_work":{"arxiv_id":"2501.06521","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.06521","snapshot_observed_at":"2026-08-11T19:55:14.838201Z","title":"Fine-tuning Large Language Models for Improving Factuality in Legal Question Answering","venue":"cs.CL","work_id":"e0cc31b8-f016-4764-ac4e-ed92a4c5102e","year":2025},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:14.036760Z"},"links":{"cited_paper":"/paper/2501.06521","citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:3f18a895910a59bef2175d08a11cfba8cb206086f5ce579fd81f136bf4038357","observation_id":"af55a9b1-74ac-4c52-9607-945281bba414","resolution":{"observed_at":"2026-08-11T19:55:14.847761Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:55:14.043902Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:14.043902Z"},"links":{"citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:71c885295590e570e85cf1caf09901dc264e9ecfcb8ebf2e8e4c374bd89b19e1","observation_id":"e1874f82-5bfd-418b-8438-c54db42fe542","resolution":{"observed_at":"2026-08-11T19:55:14.043902Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.05399","last_updated":"2024-11-26T08:48:42Z","snapshot_observed_at":"2026-07-06T18:42:34.958119Z","submitted_at":"2024-07-07T14:55:04Z","title":"IL-TUR: Benchmark for Indian Legal Text Understanding and Reasoning","version":2},"cited_work":{"arxiv_id":"2407.05399","doi":null,"metadata_source":"pith","pith_arxiv_id":"2407.05399","snapshot_observed_at":"2026-08-11T19:55:14.711603Z","title":"IL-TUR: Benchmark for Indian Legal Text Understanding and Reasoning","venue":"cs.CL","work_id":"bad809ce-100d-45ff-a08f-21828f605e0b","year":2024},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:14.050387Z"},"links":{"cited_paper":"/paper/2407.05399","citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:9e52cf112517fd012683c8b700c41456c36b15816394c2b2356a886d57887542","observation_id":"16bf2114-f5e8-4e5a-8e98-6186fae4784a","resolution":{"observed_at":"2026-08-11T19:55:14.720040Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.17259","last_updated":"2024-12-23T04:02:46Z","snapshot_observed_at":"2026-08-11T15:38:35.028144Z","submitted_at":"2024-12-23T04:02:46Z","title":"LegalAgentBench: Evaluating LLM Agents in Legal Domain","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.17259","snapshot_observed_at":"2026-08-11T19:55:14.057214Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:14.057214Z"},"links":{"cited_paper":"/paper/2412.17259","citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:37dac59f76f109bad03714a3b1d94107c69dec2cf0c335055799083597d212a0","observation_id":"ca4500ef-cab4-4921-879b-208520ab91a9","resolution":{"observed_at":"2026-08-11T19:55:14.057214Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.20288","last_updated":"2024-11-26T15:35:49Z","snapshot_observed_at":"2026-08-11T15:38:07.519535Z","submitted_at":"2024-09-30T13:44:00Z","title":"LexEval: A Comprehensive Chinese Legal Benchmark for Evaluating Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.20288","snapshot_observed_at":"2026-08-11T19:55:14.065633Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:14.065633Z"},"links":{"cited_paper":"/paper/2409.20288","citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:88c9874129f670c10560f4f0cdc8d586229e44c29858c31af7032f9d80ae0004","observation_id":"2a77e2aa-7058-4fc2-b486-45db849e81c4","resolution":{"observed_at":"2026-08-11T19:55:14.065633Z","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":"2026-08-11T19:55:14.072967Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:14.072967Z"},"links":{"citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:c6330d07fd0ce642fd9351c773f948699a5333be6a1660cce4ab7f3d791f7302","observation_id":"0faa0c98-400c-4f92-949d-5b13f0a585ea","resolution":{"observed_at":"2026-08-11T19:55:14.072967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.20362","last_updated":"2024-05-30T17:56:05Z","snapshot_observed_at":"2026-08-05T11:35:24.583000Z","submitted_at":"2024-05-30T17:56:05Z","title":"Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.20362","snapshot_observed_at":"2026-08-11T19:55:14.082869Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:14.082869Z"},"links":{"cited_paper":"/paper/2405.20362","citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:809ad0678e37ea5dce076ec3803aa721940fa0c2b79fbc30fff94cfe7ac5aa5f","observation_id":"8fe9db50-33bc-45e4-b6c5-650e2efcd91e","resolution":{"observed_at":"2026-08-11T19:55:14.082869Z","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":"2026-08-11T19:55:14.091292Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:14.091292Z"},"links":{"citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:0aa8e6606269ec05ef1edf4f8d3fe964d9d7687685c8b10746a27d9eeb6a7543","observation_id":"e19084b3-af25-40f7-b273-ebabc9c8d899","resolution":{"observed_at":"2026-08-11T19:55:14.091292Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.11195","last_updated":"2024-10-15T02:18:01Z","snapshot_observed_at":"2026-07-06T19:33:34.819837Z","submitted_at":"2024-10-15T02:18:01Z","title":"Athena: Retrieval-augmented Legal Judgment Prediction with Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.11195","snapshot_observed_at":"2026-08-11T19:55:14.097181Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:14.097181Z"},"links":{"cited_paper":"/paper/2410.11195","citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:0f768fe73eab8e863f50f5ff2a3cde27730b829c3d9684b04068d27530a38d07","observation_id":"32bbf20c-4f30-44ec-b552-0afdc4a976b8","resolution":{"observed_at":"2026-08-11T19:55:14.097181Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.10343","last_updated":"2024-08-19T18:30:18Z","snapshot_observed_at":"2026-08-11T15:37:47.886344Z","submitted_at":"2024-08-19T18:30:18Z","title":"LegalBench-RAG: A Benchmark for Retrieval-Augmented Generation in the Legal Domain","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.10343","snapshot_observed_at":"2026-08-11T19:55:14.103552Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:14.103552Z"},"links":{"cited_paper":"/paper/2408.10343","citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:b8807b3c98af37fd21ec9ddffa7e0c4b0260d46e6be524f6a82c9b664973303e","observation_id":"8aa07e33-cd1b-4ed6-87fa-723b1166639e","resolution":{"observed_at":"2026-08-11T19:55:14.103552Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.04416","last_updated":"2023-08-04T16:59:48Z","snapshot_observed_at":"2026-08-09T19:48:36.958334Z","submitted_at":"2023-08-04T16:59:48Z","title":"Legal Summarisation through LLMs: The PRODIGIT Project","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.04416","snapshot_observed_at":"2026-08-11T19:55:14.110159Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:14.110159Z"},"links":{"cited_paper":"/paper/2308.04416","citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:99f7f78d5d92d946d1a2aa2089381d93eaa6f302cecb3471e3f3948cf937d157","observation_id":"b737d79b-35fc-4efe-a0ba-7db34052b28f","resolution":{"observed_at":"2026-08-11T19:55:14.110159Z","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":"2026-08-11T19:55:15.165519Z","title":null,"venue":null,"work_id":"5567df20-48d1-432c-a319-996113a5ced4","year":1987},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:14.117465Z"},"links":{"citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:f6add8cec377f6e3dc4ff8e379912e43ac4f521e2ba022196352c8bc6ca32b3f","observation_id":"5d6d56de-ffb3-403e-a188-a4b64a5ce441","resolution":{"observed_at":"2026-08-11T19:55:15.177004Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T19:55:14.123165Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:14.123165Z"},"links":{"citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:69a0f3d707a4cac210d590223993cdad83f39d0b2e46e65f167428b4f22e5085","observation_id":"dd27ec46-d08c-4494-8dee-b35a9830d3ac","resolution":{"observed_at":"2026-08-11T19:55:14.123165Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.00876","last_updated":"2023-11-24T14:24:01Z","snapshot_observed_at":"2026-08-10T02:24:15.686124Z","submitted_at":"2023-01-02T21:08:27Z","title":"MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.00876","snapshot_observed_at":"2026-08-11T19:55:14.129045Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:14.129045Z"},"links":{"cited_paper":"/paper/2301.00876","citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:2b91c9d503f6fb502f0a73bfc28b4fd6e7d235b6fa0c32f65593e60c8289b214","observation_id":"16793eed-f4fc-415e-8e9e-e79f3e0eb4e4","resolution":{"observed_at":"2026-08-11T19:55:14.129045Z","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":"2026-08-11T19:55:14.142191Z","title":"online\" 'onlinestring :=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:14.142191Z"},"links":{"citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:1a70167a0d7fdd1ad45ea5e83de06accab0121631253d5be2b3e3aad75d89e42","observation_id":"99f1039c-8b79-41e5-b9d0-f80c1f572444","resolution":{"observed_at":"2026-08-11T19:55:14.142191Z","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":"2026-08-11T19:55:14.151821Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-11T19:55:14.151821Z"},"links":{"citing_paper":"/paper/2412.06272"},"observation_digest":"sha256:493701ed1ee3597bb1e93e666fce84832ebd43f36b9c4dfbbdaa3c155c498916","observation_id":"e15e7976-081c-42be-81c6-6d94934f3c05","resolution":{"observed_at":"2026-08-11T19:55:14.151821Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.06272","last_updated":"2025-05-22T03:52:00Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-11T19:48:33.213206Z","submitted_at":"2024-12-09T07:46:14Z","title":"Evaluating LLM-based Approaches to Legal Citation Prediction: Domain-specific Pre-training, Fine-tuning, or RAG? A Benchmark and an Australian Law Case Study"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":33,"verified_exact":3,"verified_fuzzy":0},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2412.06272."}