{"as_of":"2026-08-17T21:27:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0df64d89e7a6e94db178c164247312146fbdecacac1c438efda4a055187b4a2e","coverage":[{"denominator":14,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":14,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T22:27:44.498284Z","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":"2026-08-17T06:30:58.91139+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/2505.07166/citation-record","integrity":"/paper/2505.07166/integrity","json":"/paper/2505.07166/citation-record.json","paper":"/paper/2505.07166"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T22:27:44.647442Z","title":"Pruning-based methods in deep neural networks: A review","venue":null,"work_id":"2e5b2559-81f0-4f35-85ba-869a3150a8d1","year":2021},"citing_paper":{"arxiv_id":"2505.07166","last_updated":"2025-05-12T01:24:00Z","snapshot_observed_at":"2026-08-17T15:09:01.700698Z","submitted_at":"2025-05-12T01:24:00Z","title":"Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T22:27:44.461623Z"},"links":{"citing_paper":"/paper/2505.07166"},"observation_digest":"sha256:eb97d74d2b597eb1c50fbc88dd5a3a82bbdbd0a57834eb499d83cc88e8ef12fc","observation_id":"d4b1a9a6-8510-4e03-8518-60333e58ba13","resolution":{"observed_at":"2026-08-15T22:27:44.651643Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:27:44.636268Z","title":"Contriever: A fully unsupervised dense retriever","venue":null,"work_id":"cba6d81e-35ef-45c9-b6b5-b2752d970514","year":2021},"citing_paper":{"arxiv_id":"2505.07166","last_updated":"2025-05-12T01:24:00Z","snapshot_observed_at":"2026-08-17T15:09:01.700698Z","submitted_at":"2025-05-12T01:24:00Z","title":"Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T22:27:44.465412Z"},"links":{"citing_paper":"/paper/2505.07166"},"observation_digest":"sha256:51b1ed8b61ea96cbd509cdb40faaa3177b628328a851178f38f68692ade887dc","observation_id":"3647cfc4-415d-4d20-aa56-98db8975b439","resolution":{"observed_at":"2026-08-15T22:27:44.640295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:27:44.623748Z","title":"Dense passage retrieval for open-domain question answering","venue":null,"work_id":"4a39f831-a880-45ce-986e-4cb7d5620267","year":2020},"citing_paper":{"arxiv_id":"2505.07166","last_updated":"2025-05-12T01:24:00Z","snapshot_observed_at":"2026-08-17T15:09:01.700698Z","submitted_at":"2025-05-12T01:24:00Z","title":"Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T22:27:44.469036Z"},"links":{"citing_paper":"/paper/2505.07166"},"observation_digest":"sha256:df82a3cbd9b5811020b2a17d1dc796fd133a34f9dc19094486406d784b868478","observation_id":"7d87be54-4e1b-413f-8b91-8c24168afe31","resolution":{"observed_at":"2026-08-15T22:27:44.628104Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:27:44.610997Z","title":"Natural questions: A benchmark for question answering","venue":null,"work_id":"67bf0b11-7b04-4d6d-83c5-4e0714ba6aaf","year":2019},"citing_paper":{"arxiv_id":"2505.07166","last_updated":"2025-05-12T01:24:00Z","snapshot_observed_at":"2026-08-17T15:09:01.700698Z","submitted_at":"2025-05-12T01:24:00Z","title":"Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T22:27:44.472738Z"},"links":{"citing_paper":"/paper/2505.07166"},"observation_digest":"sha256:2d2798ea4e1b8b09209c2682ff1236fbbb06f8c9cfdec398dea4da3e051c1062","observation_id":"97f27550-d18a-45fa-a296-f542990d89f8","resolution":{"observed_at":"2026-08-15T22:27:44.615202Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:27:44.573663Z","title":"Sentence-bert: Sentence embeddings using siamese bert-networks","venue":null,"work_id":"fe0822cc-4fa0-479f-b8ab-1ff4d3913a59","year":2019},"citing_paper":{"arxiv_id":"2505.07166","last_updated":"2025-05-12T01:24:00Z","snapshot_observed_at":"2026-08-17T15:09:01.700698Z","submitted_at":"2025-05-12T01:24:00Z","title":"Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T22:27:44.483036Z"},"links":{"citing_paper":"/paper/2505.07166"},"observation_digest":"sha256:b925ac43ea58172b9e7fe96bf30f3b593fcf4199d8fc8d8c447ca1053a81208c","observation_id":"c2e96770-9b10-454f-b61c-95cf2ae43097","resolution":{"observed_at":"2026-08-15T22:27:44.577608Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:27:44.559291Z","title":"Replama: A decoder-based dense retriever for open-domain question answering","venue":null,"work_id":"69e5a206-789a-4873-a64a-06a5c031f556","year":2021},"citing_paper":{"arxiv_id":"2505.07166","last_updated":"2025-05-12T01:24:00Z","snapshot_observed_at":"2026-08-17T15:09:01.700698Z","submitted_at":"2025-05-12T01:24:00Z","title":"Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T22:27:44.486765Z"},"links":{"citing_paper":"/paper/2505.07166"},"observation_digest":"sha256:c3444e539fe66f703c1893629872a671622d6f8d0ffdeb451f7ef303bbd8450e","observation_id":"f1dde236-418c-48f0-a08f-b115a982bfc5","resolution":{"observed_at":"2026-08-15T22:27:44.565492Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.17299","last_updated":"2024-11-26T10:47:35Z","snapshot_observed_at":"2026-08-12T12:13:20.568629Z","submitted_at":"2024-11-26T10:47:35Z","title":"2D Matryoshka Training for Information Retrieval","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.17299","snapshot_observed_at":"2026-08-15T22:27:44.494350Z","title":"2d matryoshka training for information retrieval","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.07166","last_updated":"2025-05-12T01:24:00Z","snapshot_observed_at":"2026-08-17T15:09:01.700698Z","submitted_at":"2025-05-12T01:24:00Z","title":"Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T22:27:44.494350Z"},"links":{"cited_paper":"/paper/2411.17299","citing_paper":"/paper/2505.07166"},"observation_digest":"sha256:c48b193e4f08dac48bf143c138c0b451afa9b034907a8503e1192c9b02c222da","observation_id":"59371d71-578b-40bc-afaa-f957761750c5","resolution":{"observed_at":"2026-08-15T22:27:44.494350Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.18424","last_updated":"2024-10-19T01:07:37Z","snapshot_observed_at":"2026-08-16T13:57:06.595915Z","submitted_at":"2024-04-29T04:51:30Z","title":"PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.18424","snapshot_observed_at":"2026-08-15T22:27:44.498284Z","title":"Prompt-based representations for enhanced dense retrieval","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.07166","last_updated":"2025-05-12T01:24:00Z","snapshot_observed_at":"2026-08-17T15:09:01.700698Z","submitted_at":"2025-05-12T01:24:00Z","title":"Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T22:27:44.498284Z"},"links":{"cited_paper":"/paper/2404.18424","citing_paper":"/paper/2505.07166"},"observation_digest":"sha256:54f11bd622b11334a56c420ae4dd50c0b2fd8d795fee29765a1a2441c519fbd7","observation_id":"6e5f0728-3215-4cc4-87d7-5e8de5b8e3d1","resolution":{"observed_at":"2026-08-15T22:27:44.498284Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.13443","last_updated":"2022-09-12T07:54:26Z","snapshot_observed_at":"2026-08-16T16:43:00.743490Z","submitted_at":"2022-07-27T10:43:27Z","title":"Lecture Notes on Neural Information Retrieval","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.13443","snapshot_observed_at":"2026-08-15T22:27:44.490401Z","title":"Lecture notes on neural information retrieval","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.07166","last_updated":"2025-05-12T01:24:00Z","snapshot_observed_at":"2026-08-17T15:09:01.700698Z","submitted_at":"2025-05-12T01:24:00Z","title":"Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition","version":1},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-15T22:27:44.490401Z"},"links":{"cited_paper":"/paper/2207.13443","citing_paper":"/paper/2505.07166"},"observation_digest":"sha256:e11d8a14e745b4192eb746c09e2a180a7215d2706e2d923782e16f9b5b8afb71","observation_id":"02a75ee3-f188-4a6f-92ef-2a64ed476709","resolution":{"observed_at":"2026-08-15T22:27:44.490401Z","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":"2026-08-15T22:27:44.597270Z","title":"Promptreps: Enhancing dense retrieval with prompt-based representations","venue":null,"work_id":"f3dae4fa-f34e-4b96-a037-6602c44cbb0c","year":2021},"citing_paper":{"arxiv_id":"2505.07166","last_updated":"2025-05-12T01:24:00Z","snapshot_observed_at":"2026-08-17T15:09:01.700698Z","submitted_at":"2025-05-12T01:24:00Z","title":"Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-15T22:27:44.476034Z"},"links":{"citing_paper":"/paper/2505.07166"},"observation_digest":"sha256:aef66ced44c28c444863548e84ee94835e8e6b8a990e3d1a8123a75b645080ef","observation_id":"00da0993-dc05-4649-a176-023210b775d6","resolution":{"observed_at":"2026-08-15T22:27:44.601548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:27:44.680312Z","title":"Knowledge neurons in pre-trained transformers","venue":null,"work_id":"2b201081-bf1f-40d1-b79d-78c840220660","year":2022},"citing_paper":{"arxiv_id":"2505.07166","last_updated":"2025-05-12T01:24:00Z","snapshot_observed_at":"2026-08-17T15:09:01.700698Z","submitted_at":"2025-05-12T01:24:00Z","title":"Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-15T22:27:44.449289Z"},"links":{"citing_paper":"/paper/2505.07166"},"observation_digest":"sha256:bc3d84d3e489fffb448d69804233e753397f56437e6e7149246482a0ae4a34a7","observation_id":"ecbff32f-d97b-496f-b93d-b16fe64ca71f","resolution":{"observed_at":"2026-08-15T22:27:44.684098Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:27:44.658590Z","title":"Transformer feed-forward layers are key-value memories","venue":null,"work_id":"ea6e5933-cf74-4943-a30e-5c450f95ab02","year":2021},"citing_paper":{"arxiv_id":"2505.07166","last_updated":"2025-05-12T01:24:00Z","snapshot_observed_at":"2026-08-17T15:09:01.700698Z","submitted_at":"2025-05-12T01:24:00Z","title":"Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-15T22:27:44.457425Z"},"links":{"citing_paper":"/paper/2505.07166"},"observation_digest":"sha256:08389d0352b0ba9ea301c8c28023572600ef8e9ff6e6c2e4ce1d41b8bfac7973","observation_id":"4a50d862-45da-4184-9dd8-66ac78b0fca9","resolution":{"observed_at":"2026-08-15T22:27:44.662509Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:27:44.669519Z","title":"Simcse: Simple contrastive learning of sentence embeddings","venue":null,"work_id":"d3004e2b-dbe0-40a7-8531-51e8f63b4238","year":2021},"citing_paper":{"arxiv_id":"2505.07166","last_updated":"2025-05-12T01:24:00Z","snapshot_observed_at":"2026-08-17T15:09:01.700698Z","submitted_at":"2025-05-12T01:24:00Z","title":"Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-15T22:27:44.453562Z"},"links":{"citing_paper":"/paper/2505.07166"},"observation_digest":"sha256:0b0d452e67bd5e67a0eeb1343c88d02751f8aad8e170e0a4e42f70fc9ab06d18","observation_id":"06a1c6d7-edbe-48b8-8c20-0b3a7b867693","resolution":{"observed_at":"2026-08-15T22:27:44.673294Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T22:27:44.585116Z","title":"Pre-training for ad-hoc retrieval: hyperlink is also you need","venue":null,"work_id":"37eb2144-d0df-40ff-8c8f-d7b2d27c385c","year":2016},"citing_paper":{"arxiv_id":"2505.07166","last_updated":"2025-05-12T01:24:00Z","snapshot_observed_at":"2026-08-17T15:09:01.700698Z","submitted_at":"2025-05-12T01:24:00Z","title":"Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-15T22:27:44.479234Z"},"links":{"citing_paper":"/paper/2505.07166"},"observation_digest":"sha256:0aa161e0ffaaa636da5fad8f587ce9fd679dce20db5cac31421cda0a8f0c9c6a","observation_id":"c984636a-7384-48bb-b2e3-6e561ee9d263","resolution":{"observed_at":"2026-08-15T22:27:44.589345Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.07166","last_updated":"2025-05-12T01:24:00Z","latest_version":1,"primary_category":"cs.IR","snapshot_observed_at":"2026-08-17T15:09:01.700698Z","submitted_at":"2025-05-12T01:24:00Z","title":"Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition"},"reference_resolution":{"displayed":14,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":3,"verified_exact":0,"verified_fuzzy":11},"total_outbound_references":14},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2505.07166."}