{"as_of":"2026-08-08T11:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:cbcd96f8f72d1330dd048b7c6a133ac0b895c8b28152c5eb9c39ef623cbd3b74","coverage":[{"denominator":13,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":13,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:50:40.121894Z","state":"measured"},{"denominator":14,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":14,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T23:34:29.678007Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-04T23:34:32.227476Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.20871","last_updated":"2025-05-27T08:21:21Z","snapshot_observed_at":"2026-08-07T13:43:19.612915Z","submitted_at":"2025-05-27T08:21:21Z","title":"Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of RAG","version":1},"cited_work":{"arxiv_id":"2505.20871","doi":null,"metadata_source":"pith","pith_arxiv_id":"2505.20871","snapshot_observed_at":"2026-08-04T23:34:32.227476Z","title":"Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of RAG","venue":"cs.CL","work_id":"bd1f8891-5112-4207-84c6-9da148caa07a","year":2025},"citing_paper":{"arxiv_id":"2509.06472","last_updated":"2025-09-09T08:54:11Z","snapshot_observed_at":"2026-08-08T03:57:15.165103Z","submitted_at":"2025-09-08T09:37:20Z","title":"Rethinking LLM Parametric Knowledge as Post-retrieval Confidence for Dynamic Retrieval and Reranking","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T23:34:29.678007Z"},"links":{"cited_paper":"/paper/2505.20871","citing_paper":"/paper/2509.06472"},"observation_digest":"sha256:35d67d2a43f37a7f3b849cd5b8e126d4bf9abee16aaf2c79c7e02c654d842557","observation_id":"dc78e5a1-3c5d-4d0c-be8b-c55dbb02d6ca","resolution":{"observed_at":"2026-08-04T23:34:32.231695Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2505.20871/citation-record","integrity":"/paper/2505.20871/integrity","json":"/paper/2505.20871/citation-record.json","paper":"/paper/2505.20871"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T13:50:40.794482Z","title":null,"venue":null,"work_id":"eaff3356-b366-45b6-b878-da34ca1a995c","year":null},"citing_paper":{"arxiv_id":"2505.20871","last_updated":"2025-05-27T08:21:21Z","snapshot_observed_at":"2026-08-07T13:43:19.612915Z","submitted_at":"2025-05-27T08:21:21Z","title":"Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of RAG","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T13:50:39.857149Z"},"links":{"citing_paper":"/paper/2505.20871"},"observation_digest":"sha256:db32c900ef9ced6f3343716b867eabc9c32f1637041f35a7a9b7ef58b9390203","observation_id":"cd172284-c2e5-4d44-b8eb-456688986a1a","resolution":{"observed_at":"2026-08-07T13:50:40.885039Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:50:40.626148Z","title":null,"venue":null,"work_id":"0f6f6afc-103d-4f24-8f9b-33646fba600d","year":null},"citing_paper":{"arxiv_id":"2505.20871","last_updated":"2025-05-27T08:21:21Z","snapshot_observed_at":"2026-08-07T13:43:19.612915Z","submitted_at":"2025-05-27T08:21:21Z","title":"Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of RAG","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T13:50:39.953559Z"},"links":{"citing_paper":"/paper/2505.20871"},"observation_digest":"sha256:9561fb1d3f64c2948fe67d94ec2efc4067bf6c9e79e75a8fd7131a547d754568","observation_id":"6a7c0d8c-8164-4e3d-93af-c5d67d1567c8","resolution":{"observed_at":"2026-08-07T13:50:40.731990Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:50:40.467920Z","title":null,"venue":null,"work_id":"7bb733b8-ad00-4a0b-b801-0c9cc9ac9438","year":null},"citing_paper":{"arxiv_id":"2505.20871","last_updated":"2025-05-27T08:21:21Z","snapshot_observed_at":"2026-08-07T13:43:19.612915Z","submitted_at":"2025-05-27T08:21:21Z","title":"Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of RAG","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T13:50:40.029217Z"},"links":{"citing_paper":"/paper/2505.20871"},"observation_digest":"sha256:9e7f4e67272a43f7c72f0d648db3b6ddcf1b060c156bb954d9c891b5845dbb9e","observation_id":"b85a5c8f-c8e4-4b3e-97e4-fceedb79638c","resolution":{"observed_at":"2026-08-07T13:50:40.535948Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.10236","last_updated":"2025-01-05T06:15:04Z","snapshot_observed_at":"2026-07-06T15:56:03.875184Z","submitted_at":"2023-07-16T08:28:04Z","title":"Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.10236","snapshot_observed_at":"2026-08-07T13:50:39.165790Z","title":"arXiv preprint arXiv:2307.10236","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.20871","last_updated":"2025-05-27T08:21:21Z","snapshot_observed_at":"2026-08-07T13:43:19.612915Z","submitted_at":"2025-05-27T08:21:21Z","title":"Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of RAG","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T13:50:39.165790Z"},"links":{"cited_paper":"/paper/2307.10236","citing_paper":"/paper/2505.20871"},"observation_digest":"sha256:37c22306ba84ef2ee6c6a1c38b02dc367b77ddd5b44dbee268eb8caf44798ecb","observation_id":"912e6fa5-4b63-4d10-a79b-15e3f1ec704c","resolution":{"observed_at":"2026-08-07T13:50:39.165790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.11457","last_updated":"2024-06-11T08:08:47Z","snapshot_observed_at":"2026-07-06T17:31:41.687799Z","submitted_at":"2024-02-18T04:57:19Z","title":"When Do LLMs Need Retrieval Augmentation? Mitigating LLMs' Overconfidence Helps Retrieval Augmentation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.11457","snapshot_observed_at":"2026-08-07T13:50:39.311498Z","title":"arXiv preprint arXiv:2402.11457","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.20871","last_updated":"2025-05-27T08:21:21Z","snapshot_observed_at":"2026-08-07T13:43:19.612915Z","submitted_at":"2025-05-27T08:21:21Z","title":"Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of RAG","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T13:50:39.311498Z"},"links":{"cited_paper":"/paper/2402.11457","citing_paper":"/paper/2505.20871"},"observation_digest":"sha256:105e4dabf115375fa3d14455a73d5774f6d353733ad81de3998f3a8031526816","observation_id":"2a6ede6c-8427-4e3e-87ec-11f4bad444d8","resolution":{"observed_at":"2026-08-07T13:50:39.311498Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.11242","last_updated":"2025-04-24T14:58:40Z","snapshot_observed_at":"2026-08-06T10:43:07.875232Z","submitted_at":"2024-09-17T14:47:33Z","title":"Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refuse","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.11242","snapshot_observed_at":"2026-08-07T13:50:39.380085Z","title":"Advances in Neu- ral Information Processing Systems, 36","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20871","last_updated":"2025-05-27T08:21:21Z","snapshot_observed_at":"2026-08-07T13:43:19.612915Z","submitted_at":"2025-05-27T08:21:21Z","title":"Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of RAG","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T13:50:39.380085Z"},"links":{"cited_paper":"/paper/2409.11242","citing_paper":"/paper/2505.20871"},"observation_digest":"sha256:b935dfea6393b23322a32c844d485d30895189d274fcbc2b5c6bd2b3cf416f7c","observation_id":"abe08b7d-1a36-45c2-8168-9e80c480a814","resolution":{"observed_at":"2026-08-07T13:50:39.380085Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.21028","last_updated":"2024-07-03T12:49:23Z","snapshot_observed_at":"2026-08-06T06:24:20.841100Z","submitted_at":"2024-05-31T17:16:38Z","title":"LACIE: Listener-Aware Finetuning for Confidence Calibration in Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.21028","snapshot_observed_at":"2026-08-07T13:50:39.500130Z","title":"arXiv preprint arXiv:2405.21028","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20871","last_updated":"2025-05-27T08:21:21Z","snapshot_observed_at":"2026-08-07T13:43:19.612915Z","submitted_at":"2025-05-27T08:21:21Z","title":"Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of RAG","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T13:50:39.500130Z"},"links":{"cited_paper":"/paper/2405.21028","citing_paper":"/paper/2505.20871"},"observation_digest":"sha256:406cd40da8435c9a384e3fcc05fb47d93ba9d203c8954b995551e4ed1c33d53e","observation_id":"a309b4b0-284d-4569-b6ec-8c11a7103bd0","resolution":{"observed_at":"2026-08-07T13:50:39.500130Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-07T12:56:43.323460Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-08-07T13:50:39.649777Z","title":"knowing when you don’t know","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20871","last_updated":"2025-05-27T08:21:21Z","snapshot_observed_at":"2026-08-07T13:43:19.612915Z","submitted_at":"2025-05-27T08:21:21Z","title":"Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of RAG","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T13:50:39.649777Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2505.20871"},"observation_digest":"sha256:1311dbbbb4100ba0e6d2d356121e116b53470f7191368abebe676692b16e48ac","observation_id":"c5307181-38bc-4327-bf64-c055a695585f","resolution":{"observed_at":"2026-08-07T13:50:39.649777Z","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":"2026-08-07T13:50:41.032614Z","title":"lost in the middle","venue":null,"work_id":"97754289-0118-44d2-b522-c1333f25c547","year":2024},"citing_paper":{"arxiv_id":"2505.20871","last_updated":"2025-05-27T08:21:21Z","snapshot_observed_at":"2026-08-07T13:43:19.612915Z","submitted_at":"2025-05-27T08:21:21Z","title":"Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of RAG","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T13:50:39.724853Z"},"links":{"citing_paper":"/paper/2505.20871"},"observation_digest":"sha256:5e5ddeb6efb33a3fd7eba3771ce5696ade133011d1b08c408029a7458c4e2b12","observation_id":"397acd04-661d-4658-b6d1-7155db837ff3","resolution":{"observed_at":"2026-08-07T13:50:41.102367Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T13:50:40.315522Z","title":"score\": 0 or 1,","venue":null,"work_id":"2bd909b1-43db-4161-a6f9-c0d3c660beee","year":2022},"citing_paper":{"arxiv_id":"2505.20871","last_updated":"2025-05-27T08:21:21Z","snapshot_observed_at":"2026-08-07T13:43:19.612915Z","submitted_at":"2025-05-27T08:21:21Z","title":"Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of RAG","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T13:50:40.121894Z"},"links":{"citing_paper":"/paper/2505.20871"},"observation_digest":"sha256:ce7de7fdf11ebd1ccf02bb4b629dbe6b0d2c8a1306c92db6b9f8ed62b1fe9b9c","observation_id":"f8e076bd-7cc8-44b9-ba65-17f6f8b4dd7b","resolution":{"observed_at":"2026-08-07T13:50:40.381397Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.12712","last_updated":"2023-04-13T20:41:31Z","snapshot_observed_at":"2026-08-03T04:49:15.195814Z","submitted_at":"2023-03-22T16:51:28Z","title":"Sparks of Artificial General Intelligence: Early experiments with GPT-4","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.12712","snapshot_observed_at":"2026-08-07T13:50:38.707666Z","title":"In International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Bal- timore, Maryland, USA, volume 162 of Proceedings of Machine Learning Research , pages 2206–2240","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.20871","last_updated":"2025-05-27T08:21:21Z","snapshot_observed_at":"2026-08-07T13:43:19.612915Z","submitted_at":"2025-05-27T08:21:21Z","title":"Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of RAG","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-07T13:50:38.707666Z"},"links":{"cited_paper":"/paper/2303.12712","citing_paper":"/paper/2505.20871"},"observation_digest":"sha256:c3730bacc6ef1bd477812e980f3d40cfa20e5237c7a30c7349d8ce69038a3f31","observation_id":"6e89a56a-acac-4c60-95d4-884de0b4e1f5","resolution":{"observed_at":"2026-08-07T13:50:38.707666Z","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":"2026-08-07T13:50:41.179021Z","title":"In Proceedings of the 17th Conference of the European Chapter of the Association for Com- putational Linguistics, pages 1059–1075, Dubrovnik, Croatia","venue":null,"work_id":"1f1cedb5-cb94-4267-88d8-8d27ec393fd5","year":2017},"citing_paper":{"arxiv_id":"2505.20871","last_updated":"2025-05-27T08:21:21Z","snapshot_observed_at":"2026-08-07T13:43:19.612915Z","submitted_at":"2025-05-27T08:21:21Z","title":"Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of RAG","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-07T13:50:39.005682Z"},"links":{"citing_paper":"/paper/2505.20871"},"observation_digest":"sha256:4b98713cda63247ac12b144c532d9a125e9f6ce23bc86157d76bfede71308763","observation_id":"6764c75b-7039-413e-8eb1-100cd5394dea","resolution":{"observed_at":"2026-08-07T13:50:41.297514Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.20978","last_updated":"2024-05-31T16:24:53Z","snapshot_observed_at":"2026-07-06T18:23:23.565502Z","submitted_at":"2024-05-31T16:24:53Z","title":"Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.20978","snapshot_observed_at":"2026-08-07T13:50:38.827183Z","title":"In Proceedings of the 47th International ACM SIGIR Conference on Research and Develop- ment in Information Retrieval, pages 719–729","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.20871","last_updated":"2025-05-27T08:21:21Z","snapshot_observed_at":"2026-08-07T13:43:19.612915Z","submitted_at":"2025-05-27T08:21:21Z","title":"Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of RAG","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-07T13:50:38.827183Z"},"links":{"cited_paper":"/paper/2405.20978","citing_paper":"/paper/2505.20871"},"observation_digest":"sha256:46c6dd3bddc5a693f05c3804a6a0249bebf6c72d5925aa326e11517521bc46a8","observation_id":"3ee76715-134a-44dd-b1b8-0eb36fa38234","resolution":{"observed_at":"2026-08-07T13:50:38.827183Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.20871","last_updated":"2025-05-27T08:21:21Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-07T13:43:19.612915Z","submitted_at":"2025-05-27T08:21:21Z","title":"Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of RAG"},"reference_resolution":{"displayed":13,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":0,"verified_fuzzy":2},"total_outbound_references":13},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 1 inbound Pith citation observation for arXiv:2505.20871."}