{"as_of":"2026-08-20T19:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f45dffa21b81db43721a8c2345a6187f1e20454e98ef0338322bdd2cde5b5950","coverage":[{"denominator":24,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":24,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T23:11:05.881956Z","state":"measured"},{"denominator":29,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":29,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T00:42:09.502274Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-03T15:18:32.707023Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.02764","snapshot_observed_at":"2026-08-07T00:42:09.502274Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.13824","last_updated":"2025-06-15T13:02:59Z","snapshot_observed_at":"2026-08-13T03:06:57.104152Z","submitted_at":"2025-06-15T13:02:59Z","title":"MLDebugging: Towards Benchmarking Code Debugging Across Multi-Library Scenarios","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-07T00:42:09.502274Z"},"links":{"cited_paper":"/paper/2412.02764","citing_paper":"/paper/2506.13824"},"observation_digest":"sha256:1249509af14882f042f981aaa5673040af98d8bba433ac15e8a1bf0084eed706","observation_id":"44978140-8e11-45f5-8954-b165103bdd0c","resolution":{"observed_at":"2026-08-07T00:42:09.502274Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"cited_work":{"arxiv_id":"2412.02764","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.02764","snapshot_observed_at":"2026-07-03T15:18:32.707023Z","title":"9 VisPhyWorld: Probing Physical Reasoning via Code-Driven Video Reconstruction Garrido, Q., Ballas, N., Assran, M., Bardes, A., Najman, L., Rabbat, M., Dupoux, E., and LeCun, Y","venue":null,"work_id":"27521e4e-a8ff-4ac7-869c-5713e76f412e","year":null},"citing_paper":{"arxiv_id":"2602.13294","last_updated":"2026-05-21T07:15:56Z","snapshot_observed_at":"2026-08-17T14:05:09.950492Z","submitted_at":"2026-02-09T05:46:44Z","title":"VisPhyWorld: Probing Physical Reasoning via Code-Driven Video Reconstruction","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-22T11:06:38.102348Z"},"links":{"cited_paper":"/paper/2412.02764","citing_paper":"/paper/2602.13294"},"observation_digest":"sha256:ed6615bbc9f71e9ec7b12cbd239cf0d2e7b061b792fa1024e5d0f9c66ad94008","observation_id":"62edd80c-aa5f-41dc-ad03-6e0a71601121","resolution":{"observed_at":"2026-05-22T11:11:27.539239Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.02764","snapshot_observed_at":"2026-07-13T15:55:53.399860Z","title":"Galimzyanov, S","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.29139","last_updated":"2026-08-09T04:03:34Z","snapshot_observed_at":"2026-08-16T05:17:14.711870Z","submitted_at":"2026-03-31T01:41:28Z","title":"SciVisAgentBench: A Benchmark for Evaluating Scientific Data Analysis and Visualization Agents","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-13T15:55:53.399860Z"},"links":{"cited_paper":"/paper/2412.02764","citing_paper":"/paper/2603.29139"},"observation_digest":"sha256:136a8f473e1a545ca4fbe3cd68a5af08a6d971ea9e9ade39efa6a0c20960055b","observation_id":"cdfda063-5c8d-4404-8dbb-bc397fcba74a","resolution":{"observed_at":"2026-07-13T15:55:53.399860Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.02764","snapshot_observed_at":"2026-08-02T17:09:17.931502Z","title":"Galimzyanov, S","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2603.29139","last_updated":"2026-08-09T04:03:34Z","snapshot_observed_at":"2026-08-16T05:17:14.711870Z","submitted_at":"2026-03-31T01:41:28Z","title":"SciVisAgentBench: A Benchmark for Evaluating Scientific Data Analysis and Visualization Agents","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-02T17:09:17.931502Z"},"links":{"cited_paper":"/paper/2412.02764","citing_paper":"/paper/2603.29139"},"observation_digest":"sha256:d191c54a29cc229bccafa8d7ef843cf0611eb43e5e2e555b4427ed35740c92c3","observation_id":"7967d04d-d35c-4235-892b-d00caa6a0a85","resolution":{"observed_at":"2026-08-02T17:09:17.931502Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"cited_work":{"arxiv_id":"2412.02764","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.02764","snapshot_observed_at":"2026-07-03T15:18:32.707023Z","title":"9 VisPhyWorld: Probing Physical Reasoning via Code-Driven Video Reconstruction Garrido, Q., Ballas, N., Assran, M., Bardes, A., Najman, L., Rabbat, M., Dupoux, E., and LeCun, Y","venue":null,"work_id":"27521e4e-a8ff-4ac7-869c-5713e76f412e","year":null},"citing_paper":{"arxiv_id":"2607.01883","last_updated":"2026-07-02T08:36:02Z","snapshot_observed_at":"2026-08-14T14:57:11.512918Z","submitted_at":"2026-07-02T08:36:02Z","title":"PairCoder++: Pair Programming as a Universal Paradigm for Verified Code-Driven Multimodal and Structured-Artifact Generation","version":1},"reference_index":70,"source":"arxiv_source","source_observed_at":"2026-07-03T15:15:14.024594Z"},"links":{"cited_paper":"/paper/2412.02764","citing_paper":"/paper/2607.01883"},"observation_digest":"sha256:5cca087a20c477573df6d9aa143c5ce4a7805190bace75d5ea3b13d80c4afe28","observation_id":"2075e0c2-d607-4df1-94aa-2bc1814b3fcb","resolution":{"observed_at":"2026-07-03T15:18:32.709074Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.02764/citation-record","integrity":"/paper/2412.02764/integrity","json":"/paper/2412.02764/citation-record.json","paper":"/paper/2412.02764"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:11:06.233035Z","title":"Le veraging large language models for data analysis automation,","venue":null,"work_id":"c04f2c48-e52f-4988-b297-15bc6b4372db","year":2025},"citing_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T23:11:05.676473Z"},"links":{"citing_paper":"/paper/2412.02764"},"observation_digest":"sha256:b2434288e44266ce80080fd2f14b74ade1a8d2c06feaa574576cf4a19f266bc6","observation_id":"1296daf9-43bf-443a-b070-0873db5177eb","resolution":{"observed_at":"2026-08-11T23:11:06.237472Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T23:11:06.219023Z","title":"Pipe(line) dreams: Fully au tomated end- to-end analysis and visualization,","venue":null,"work_id":"a5e87ee6-fc0a-4599-bd59-1fd01e0899b6","year":2024},"citing_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T23:11:05.681412Z"},"links":{"citing_paper":"/paper/2412.02764"},"observation_digest":"sha256:2bfa054b8fd8ada85e27931efa3e6ab9359691d1504846d3f3a3c5435171f898","observation_id":"f805fbfb-e04f-4f06-aea0-0fb0557a9734","resolution":{"observed_at":"2026-08-11T23:11:06.224123Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T23:11:06.205359Z","title":"LLMs f or science: Usage for code generation and data analysis,","venue":null,"work_id":"87b43838-2a53-4f57-86a4-4d6d5a491a89","year":2023},"citing_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T23:11:05.685741Z"},"links":{"citing_paper":"/paper/2412.02764"},"observation_digest":"sha256:58edeb540cf1ea8bdf62febae4d12cdf16f87a046fee9667e7e82820ece55c91","observation_id":"2641da3e-e307-485c-a8b1-6119d29ebe2a","resolution":{"observed_at":"2026-08-11T23:11:06.209946Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.11453","last_updated":"2024-03-19T14:44:22Z","snapshot_observed_at":"2026-08-20T08:48:07.799431Z","submitted_at":"2024-02-18T04:28:28Z","title":"MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.11453","snapshot_observed_at":"2026-08-11T23:11:05.689917Z","title":"MatPlotAgent: Method and evaluation for LLM-based agentic scientiﬁc data visualization,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T23:11:05.689917Z"},"links":{"cited_paper":"/paper/2402.11453","citing_paper":"/paper/2412.02764"},"observation_digest":"sha256:64baf1a7ab77bc481e05861c532b62b92a554ebaaeb9e13684aac332459eba18","observation_id":"f38cbc8b-4cae-4158-a8b9-8ea39c86bacf","resolution":{"observed_at":"2026-08-11T23:11:05.689917Z","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":"2026-08-11T23:11:06.191896Z","title":"Expectat ion vs. experi- ence: Evaluating the usability of code generation tools pow ered by large language models,","venue":null,"work_id":"e3d0a5ff-5927-4d9d-bc21-c606e15c8ba8","year":2022},"citing_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T23:11:05.694863Z"},"links":{"citing_paper":"/paper/2412.02764"},"observation_digest":"sha256:a047cfaf469f97cff8636033287f9d855ca1ad605b84f9b6384e6500f59755bf","observation_id":"8a23719f-2d9c-4cdc-a180-e040cfb01f1b","resolution":{"observed_at":"2026-08-11T23:11:06.196525Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T23:11:06.177452Z","title":"DS-1000: A natural and reliable benchmark for data science code generation,","venue":null,"work_id":"d393f83b-ea19-4e2a-b6f7-7616c9a8caef","year":2023},"citing_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T23:11:05.699187Z"},"links":{"citing_paper":"/paper/2412.02764"},"observation_digest":"sha256:4c6d6d5ad6e9267b51f4e4ee61c0d3af96513a81b8df3276a1a9100ae3ccd954","observation_id":"80be52c5-7409-4286-8b65-f914ad241e1d","resolution":{"observed_at":"2026-08-11T23:11:06.182000Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.07990","last_updated":"2024-05-13T17:59:22Z","snapshot_observed_at":"2026-08-16T13:53:04.020766Z","submitted_at":"2024-05-13T17:59:22Z","title":"Plot2Code: A Comprehensive Benchmark for Evaluating Multi-modal Large Language Models in Code Generation from Scientific Plots","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.07990","snapshot_observed_at":"2026-08-11T23:11:05.703705Z","title":"Plot2Code: A comprehensive benchmark for evaluating mult i-modal large language models in code generation from scientiﬁc plo ts,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T23:11:05.703705Z"},"links":{"cited_paper":"/paper/2405.07990","citing_paper":"/paper/2412.02764"},"observation_digest":"sha256:98d1c9c236b68626269508ed86555b9fe1e7fcce0d82c81a2b2aec3a397ac1e8","observation_id":"88f2442b-7fdc-45b1-97c7-fe377454450e","resolution":{"observed_at":"2026-08-11T23:11:05.703705Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.09961","last_updated":"2025-02-28T13:33:00Z","snapshot_observed_at":"2026-08-18T23:26:00.281764Z","submitted_at":"2024-06-14T12:10:51Z","title":"ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.09961","snapshot_observed_at":"2026-08-11T23:11:05.708253Z","title":"ChartMimic: Evaluating LMM’s cross- modal reasoning capability via chart-to-code generation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T23:11:05.708253Z"},"links":{"cited_paper":"/paper/2406.09961","citing_paper":"/paper/2412.02764"},"observation_digest":"sha256:9d9930080e28d0c2b406ad0a348f3183ce2322d6fb60b7e200238d0b7208a964","observation_id":"285ad928-ea9c-4a5a-8c1f-088447eb1363","resolution":{"observed_at":"2026-08-11T23:11:05.708253Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.12926","last_updated":"2021-12-24T03:33:20Z","snapshot_observed_at":"2026-08-16T17:32:11.729850Z","submitted_at":"2021-12-24T03:33:20Z","title":"nvBench: A Large-Scale Synthesized Dataset for Cross-Domain Natural Language to Visualization Task","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.12926","snapshot_observed_at":"2026-08-11T23:11:05.712912Z","title":"nvBench: A large-scale synthe sized dataset for cross-domain natural language to visualization task,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T23:11:05.712912Z"},"links":{"cited_paper":"/paper/2112.12926","citing_paper":"/paper/2412.02764"},"observation_digest":"sha256:ac7c648c2b8f33503c48783cc659d742b1f9d3b160b9c480d4337601cdf1ffac","observation_id":"64cdd59e-fa45-4c7a-949e-f8a9b4c239ed","resolution":{"observed_at":"2026-08-11T23:11:05.712912Z","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":"10.5281/zenodo.3","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T23:11:05.910919Z","title":"pandas-dev/pandas: Pan das,","venue":null,"work_id":"bfd77eae-f768-4a91-bfe8-25ad8a724229","year":2020},"citing_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T23:11:05.717457Z"},"links":{"citing_paper":"/paper/2412.02764"},"observation_digest":"sha256:5219240ee5c9fe2af020bc06051e67aaca2dd63b4d6e895f667235851bca091f","observation_id":"ea5cffa7-83e3-413c-afd7-e957e792f133","resolution":{"observed_at":"2026-08-11T23:11:05.916283Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T23:11:06.162892Z","title":"Hello GPT-4o,","venue":null,"work_id":"54e5d5a4-3e79-4f7a-a22a-f4b00175d2a6","year":2024},"citing_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T23:11:05.721596Z"},"links":{"citing_paper":"/paper/2412.02764"},"observation_digest":"sha256:bca81f995d73e5f1ea132b61d4d4c2555d43f482d80f7c1a0a721de62e706a2b","observation_id":"afea46eb-8104-4a19-923a-0d21c3029e9b","resolution":{"observed_at":"2026-08-11T23:11:06.168292Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T23:11:06.149425Z","title":"The Claude 3 model family: Opus, Sonnet, Ha iku,","venue":null,"work_id":"a705f125-c1e5-451d-9dca-76666ee40e55","year":2024},"citing_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T23:11:05.725703Z"},"links":{"citing_paper":"/paper/2412.02764"},"observation_digest":"sha256:e23154e6737e662f1f5906616e097a70476bfbd6384b5d444dc0ac377963b2d5","observation_id":"0be27900-62e7-49e5-bbb4-8c82b56b9e1f","resolution":{"observed_at":"2026-08-11T23:11:06.153961Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.05530","last_updated":"2024-12-16T17:39:39Z","snapshot_observed_at":"2026-08-14T18:15:53.516440Z","submitted_at":"2024-03-08T18:54:20Z","title":"Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.05530","snapshot_observed_at":"2026-08-11T23:11:05.834413Z","title":"Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T23:11:05.834413Z"},"links":{"cited_paper":"/paper/2403.05530","citing_paper":"/paper/2412.02764"},"observation_digest":"sha256:017060392c579f4321081159df33c015f6ccb4712cb1505240f1c02c4fca918e","observation_id":"0c463be8-7560-49c2-9990-bbd0599dfad4","resolution":{"observed_at":"2026-08-11T23:11:05.834413Z","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":"2026-08-13T17:20:44.002518Z","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":"2026-08-11T23:11:05.840249Z","title":"The Llama 3 herd of models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T23:11:05.840249Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2412.02764"},"observation_digest":"sha256:21fe878775e01988edd85bd3bb1debee90d97dd1988e50b005c46a12b1d1e815","observation_id":"b3a9c9a4-9d28-44f1-8ea0-06a5c36d2422","resolution":{"observed_at":"2026-08-11T23:11:05.840249Z","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":"2026-08-11T23:11:06.134848Z","title":"Matplotlib: A 2D graphics environment,","venue":null,"work_id":"76d68e77-2333-40e1-9ab6-fcb5654c1f1e","year":2007},"citing_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T23:11:05.844633Z"},"links":{"citing_paper":"/paper/2412.02764"},"observation_digest":"sha256:27617924870b6811d7d275a70be7c8afe108468989bbd22e65bd1c7df8f1f54c","observation_id":"16f84e66-a811-4db4-a582-6bfb983e1836","resolution":{"observed_at":"2026-08-11T23:11:06.139185Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T23:11:06.121674Z","title":"Seaborn: statistical data visualizatio n,","venue":null,"work_id":"ddf2b0c1-1be4-43ef-b3c3-cee7f08e8540","year":2021},"citing_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T23:11:05.848846Z"},"links":{"citing_paper":"/paper/2412.02764"},"observation_digest":"sha256:528e706e41bb0a61d24bd5fa64283acb514afc758a471dc3a69af700e455324f","observation_id":"dd5b4ecf-02e1-420b-9f32-e18687703228","resolution":{"observed_at":"2026-08-11T23:11:06.126000Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T23:11:06.106682Z","title":"Collaborative data scienc e,","venue":null,"work_id":"f7573d68-b155-45b8-97fd-5b71e862ad5b","year":2015},"citing_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T23:11:05.852967Z"},"links":{"citing_paper":"/paper/2412.02764"},"observation_digest":"sha256:7d6c8d0fe02bbe0d8289430e63d26b54edb1070df2bd7d4da4f0f1f99d834b57","observation_id":"42e82cb7-6a71-42f0-bc49-db8f253b1514","resolution":{"observed_at":"2026-08-11T23:11:06.111677Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T23:11:06.091035Z","title":"HuggingFace Page with PandasPlotBench,","venue":null,"work_id":"882cb986-4476-450e-bdc6-0aebab2ce5a3","year":2024},"citing_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T23:11:05.856987Z"},"links":{"citing_paper":"/paper/2412.02764"},"observation_digest":"sha256:c1c357b323dd754172ddd20fa4ff44aadd327a99c93bd4bea19b075aa42957cd","observation_id":"bce5986a-7059-4043-824a-3a4db4378bd4","resolution":{"observed_at":"2026-08-11T23:11:06.096229Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T23:11:06.077216Z","title":"Code for running the benchmark,","venue":null,"work_id":"340f0b0a-43a0-48c0-b46e-6e5feffae265","year":2024},"citing_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T23:11:05.861210Z"},"links":{"citing_paper":"/paper/2412.02764"},"observation_digest":"sha256:af82ec44e4fa474d53ebf34a68377c49e9696966eee56d49883c46f7ab73dd39","observation_id":"03bdaba8-08a6-4907-9d4d-cf77ba6c765a","resolution":{"observed_at":"2026-08-11T23:11:06.081942Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T23:11:06.063092Z","title":"Supplementary materials,","venue":null,"work_id":"98fabb4b-33de-46e9-bfc0-f4ffe56af0d5","year":2024},"citing_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T23:11:05.865295Z"},"links":{"citing_paper":"/paper/2412.02764"},"observation_digest":"sha256:2d037ae23606067ab709cf83a6ac84e5ae978ada1150189050832f33f4736557","observation_id":"98e80da5-cb13-4b0a-9fa8-95c1c474fb69","resolution":{"observed_at":"2026-08-11T23:11:06.067355Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-11T23:11:06.049703Z","title":"MatPlotLib Gallery,","venue":null,"work_id":"7366b216-6494-411c-a0fe-5305a9f22be0","year":2025},"citing_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T23:11:05.869621Z"},"links":{"citing_paper":"/paper/2412.02764"},"observation_digest":"sha256:6b263db59bfb38a11647002317b0f1f64efa4d31f78dd379bce4c1149e4f44a8","observation_id":"4c155640-331a-40c7-90d4-91840aaf6440","resolution":{"observed_at":"2026-08-11T23:11:06.053973Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-17T09:58:46.058102Z","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":"2026-08-11T23:11:05.873704Z","title":"GPT-4 technical report,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T23:11:05.873704Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2412.02764"},"observation_digest":"sha256:c36f07e76b00274f6d5c8af4d46b281b6b05fccea34d78de83024ae68d24854f","observation_id":"bb151c14-0d12-4129-9fb0-3efcb53b472f","resolution":{"observed_at":"2026-08-11T23:11:05.873704Z","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":"2026-08-11T23:11:06.034327Z","title":"GPT-4V(ision) System Card,","venue":null,"work_id":"958feb7c-362a-40d9-b885-76b5a84ef205","year":2023},"citing_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T23:11:05.877858Z"},"links":{"citing_paper":"/paper/2412.02764"},"observation_digest":"sha256:402d9e9f6a9dedca0701cc1c2e4eb0567df98d54d19da9f517489030e6fa3484","observation_id":"2ad7261f-4ab2-436b-8bf1-f5bfb3363170","resolution":{"observed_at":"2026-08-11T23:11:06.039487Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2002.08155","last_updated":"2020-09-18T15:38:12Z","snapshot_observed_at":"2026-08-20T10:40:40.110220Z","submitted_at":"2020-02-19T13:09:07Z","title":"CodeBERT: A Pre-Trained Model for Programming and Natural Languages","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.08155","snapshot_observed_at":"2026-08-11T23:11:05.881956Z","title":"CodeBERT: A pre-trained model for programming and natural languages,","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T23:11:05.881956Z"},"links":{"cited_paper":"/paper/2002.08155","citing_paper":"/paper/2412.02764"},"observation_digest":"sha256:74519650431706636b3ca7ec224da6e520b81bba900d6f1d74dd228780638ad2","observation_id":"9188646f-6a84-4200-99b0-a6bd27450897","resolution":{"observed_at":"2026-08-11T23:11:05.881956Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.02764","last_updated":"2025-02-26T16:52:21Z","latest_version":2,"primary_category":"cs.SE","snapshot_observed_at":"2026-08-17T07:38:36.841847Z","submitted_at":"2024-12-03T19:05:37Z","title":"Drawing Pandas: A Benchmark for LLMs in Generating Plotting Code"},"reference_resolution":{"displayed":24,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":8,"verified_exact":1,"verified_fuzzy":15},"total_outbound_references":24},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 5 inbound Pith citation observations for arXiv:2412.02764."}