{"as_of":"2026-08-09T08:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9c6d0b1dda6b0a9af4f16dce41c75813bd281104298b2e501b87fc19c4e9e922","coverage":[{"denominator":38,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":38,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-28T10:56:24.138197Z","state":"measured"},{"denominator":38,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":38,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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/2606.03602/citation-record","integrity":"/paper/2606.03602/integrity","json":"/paper/2606.03602/citation-record.json","paper":"/paper/2606.03602"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T10:56:24.138197Z","title":"The MIT Press, Cambridge, MA, 2nd edition, 2000","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:47032a6f6244a1cea1113474684f4b1f957ead654137c9aeb2746c70ee6f0d34","observation_id":"05c02695-c23f-4a7f-871f-c989c9680f48","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"Cambridge University Press, 2009","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:1c9630e95b868c3b9fa6847adcf746927d7ed221831ec2b5c4db23c07191b95c","observation_id":"908dee43-247b-460e-87d1-37268f01cd68","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"Basic Books, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:db118ccb3ec3b8d8a04c30427ec0c7b04260c25cc6b032bad1b6a5639ae534b1","observation_id":"7f5fec6e-290a-45d2-9717-6500831a477f","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"A survey on causal inference.ACM Transactions on Knowledge Discovery from Data (TKDD), 15(5):1–46, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:6a944bc2898b96c8d7e52537ae2905a4c88bc2399d3b62b6b999f926520a52cd","observation_id":"c5980a11-cca6-46e4-af9d-63cb3037fd94","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"Causal inference in the presence of latent variables and selection bias","venue":null,"work_id":null,"year":1995},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:32cc50394bae0fdaaf274360b155b864214ef1a4a13de2b39b3ae95b7b3e6673","observation_id":"5d242b00-bf9f-4774-bc16-800b1e324727","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"Optimal structure identification with greedy search.Journal of machine learning research, 3:507–554, 2002","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:c63a083c378d37c18f5f74fee8bab4ebc9ac5888dbdfc55cc760d0149bdfb4dc","observation_id":"50a3ad46-c5c1-4139-9ef2-dd8c20d90288","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"Fast scalable and accurate discovery of dags using the best order score search and grow shrink trees","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:7280c6bd97045ccdbeae0661ac28a4fac29c74190ca295d7e512c4af10318061","observation_id":"9e680747-1687-44c2-8492-606572e12c4c","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"Greedyrelaxationsofthesparsestpermutation algorithm","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:ef642ae4df52ce19df50606a6977ba526f2bc28b9afb89cea4fbf173e6e494bf","observation_id":"f4188ccc-f4d3-4251-b1d5-cb6303257d20","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:9f67947c3efeb4adea1bc239acfe927304f10934f90101f288634b8a0a700d34","observation_id":"f55e9e48-a3a3-4844-958b-02cb16de70a7","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:4c065edcfefe947032491ce93b9bd12b8300d191180ac2e159133859b1d825a6","observation_id":"4dc719de-7001-4a1f-acaf-0cdd83ce89d3","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"Equivalence and synthesis of causal models","venue":null,"work_id":null,"year":1990},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:85bc16c71d26b4aab943703ca71903e9d0f7d9c59a08b608680d04bce0d94496","observation_id":"1e4991d1-ac8a-4928-9de2-3df49fddd9cc","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"Efficient causal graph discovery using large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:51d4638ae293cb6edb3b54acdde0753411ce4d380833df096cb43af2049b5918","observation_id":"2ecf9741-9e8e-4e33-b237-26e08148f315","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"Causal-llm: A unified one-shot framework for prompt-and data-driven causal graph discovery","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:69972efaae6769e580c9d82c81cec6b2d0579dba249b134b120729a3aa5de024","observation_id":"e1d979fd-3b4a-4c64-b8eb-ca0ad3a8d70f","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2025.356092","doi":"10.1109/tai.2025.3560927","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"D-Band Cascode Up conversion Mixer Utilizing Double Mixing Technique for En- hanced Linearity and Output Power in 130-nm SiGe Process","venue":"IEEE Transactions on Artificial Intelligence","work_id":"1492c11a-ad38-4154-a041-66cc5cdb7351","year":2025},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:4d954412b3372d9a797403c81d46c7cd3d84ed73f312e26c649a1dddb298f904","observation_id":"7dccaab3-e366-4837-8f91-b32775c7790c","resolution":{"observed_at":"2026-06-28T11:02:01.291735Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T10:56:24.138197Z","title":"Causal modelling agents: Causal graph discovery through synergising metadata-and data-driven reasoning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:e3601d9e9177180d1643ce77adff7586527a047abcc8af9c141d5d6905fabbb2","observation_id":"6999ffb1-2f8f-4bec-a23b-44988f31995c","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2601.13614","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-02T02:26:26.633134Z","title":"Causcientist: Teaching llms to respect data for causal discovery.arXiv preprint arXiv:2601.13614, 2026","venue":null,"work_id":"5694e700-640d-4ea2-9f2d-1dc4fb52c43a","year":2026},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:9129cb4a864ca7e75e29cf4b28cb6a77561682a5b3f1dd14f8c8a74751714dcc","observation_id":"0e4f6e6c-9125-41eb-a996-677f6224bc53","resolution":{"observed_at":"2026-07-02T02:26:26.634801Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T10:56:24.138197Z","title":"Balasubramanian, and Amit Sharma","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:bb1f1c5f07353358f1588c2dd1605158ba32d62f7992d642aede6b5cab26a989","observation_id":"d672fa5b-f49d-40b9-a250-a4d788567740","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"Integrating large language models in causal discovery: A statistical causal approach.Transactions on Machine Learning Research, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:3eac1627e9221ece8e521f4383c6e5a32cf1f84d4781b1910db1de6c47ecb672","observation_id":"10cac259-31ef-42dd-8575-c65cd7371c10","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"Causal discovery with language models as imperfect experts","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:e5f3215433b6ce270cfeb7d4570b23ae314bf562f312fb271238f889e16b32ab","observation_id":"7043d7cf-f741-46ec-a3ca-cf99abcafe3f","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"Causal discovery via mml","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:8be65067d55410d13ad26b255d519ce0aeaec8959e638f96fa9e8c69b12bb7ab","observation_id":"31105bdd-528c-4262-a385-c49427fd29ea","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"Can LLMs express their uncertainty? an empirical evaluation of confidence elicitation in LLMs","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:e7c97ddc1f3d67dedd0e7c1a74138e8f7f8a192a0a47c0ae8613acf594172e9c","observation_id":"69e2eeb2-e996-494b-965c-a00a6e0c894c","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"Deep structural causal models for tractable counterfactual inference.Advances in neural information processing systems, 33: 857–869, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:9ddf92c9649e87d7fb85a7f8ebbdf52f2318225d9fe84b5a6d2279eeffa2b168","observation_id":"8172dc94-a55b-4343-ba90-0a922f7594c9","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"Learning bayesian networks with the bnlearn r package.Journal of statistical software, 35:1–22, 2010","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:081d8cc81d0f4da36e45997c1a61ec528be45b35f58ee094bc4c26e47b5bddcc","observation_id":"9a2a131e-7f16-49a0-aa22-5ccbd2df75b9","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"CRC Press, 2nd edition, 2010","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:093cc2e10b0c12810136cd31c6acacc9ca03a2f80d358afe6ec1122a07ca3404","observation_id":"814a649c-1018-4c6b-9c2f-3136c9377386","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"Adaptive probabilistic networks with hidden variables.Machine Learning, 29(2):213–244, 1997","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:8795690556d73c14da2d6ae345aa7b45cc2dbae0a9b059eb0c5c1fbd13206cee","observation_id":"7bfbd8b8-c44f-4861-8054-99f15994a433","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"An expert system for control of waste water treatment—a pilot project","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:43a94b33cfbb346c48f70d8052b4e95fe954ad718be6c4016dc94da2751972ab","observation_id":"c81423bd-85c6-4d1e-a895-f37d9115b803","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"The alarm monitoring system: A case study with two probabilistic inference techniques for belief networks","venue":null,"work_id":null,"year":1989},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:dea07a1134bdb3b262fea691e9f236b92ad6207b4f0041d70df82a0f55e7addc","observation_id":"ad1a45e7-54a5-4073-abfc-0f0cd5840549","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":null,"venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:c6e3bcd6d09f85af42c88a134d896488546c0157217278cfa2bffe309c76eefc","observation_id":"a88f2c93-11f1-49f6-936e-40adbec8d012","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"Decision-theoretic troubleshooting","venue":null,"work_id":null,"year":1995},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:ad708a0eb25125479ee4473f2696209e69bd765df19db7f5ad521fc6eee78ef6","observation_id":"ad5a1464-8ad8-4e5b-968d-b4de94eb4e42","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"Structural intervention distance for evaluating causal graphs","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:f9de9f668524009f06b717b7039ff3292eef716b346104cfcd2a29663c5f2b43","observation_id":"73d550ee-b0d6-4231-8ff3-42272cabe15b","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"Learning sparse nonparametric dags","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:2b4f120d387e7230de1c3090f65db22b1cf18185d3d0a0838e6ba63697b60a22","observation_id":"436c5dd9-b1fc-4da2-80cc-792eb1295e44","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"Sample, estimate, aggregate: A recipe for causal discovery foundation models.Transactions on Machine Learning Research,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:94e3f324f5059c33020c74d9762eb680c4a1ff625b8f7e618af75be0bff80f6e","observation_id":"1fcda739-031d-465c-800c-73b13b87ded6","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"URLhttps://openreview.net/forum?id=h434zx5SX0","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:26cb9e4598a90e66e3cfc6bc98ccaf86842b051651c38a7d6fbb030983028525","observation_id":"db43d7e2-17e0-42bf-a40b-dd064874b0be","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"Causcale: Neural causal discovery at scale","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:b68ec585b0ff6259d0c3e07a56f630da9b651ca4715f6c172945dff72e94dc83","observation_id":"068ac6e5-87e6-4864-820c-e8fd808693e9","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"pgmpy: A python toolkit for bayesian networks.Journal of Machine Learning Research, 25(265):1–8, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:fb1e2c30297b88f5312e6b6d951f39ef3e0d16f4f965df7a93d17c55ea5d2f43","observation_id":"9ded7335-ba34-4e13-8a93-74e88d31a593","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"Pedregosa, G","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:ab83dfb242558ab140b99521e072005825ff7cdf444f7a89c9f70ef958754b95","observation_id":"55aa6dda-f7d4-4baa-ac79-14a2bee98ed5","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","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":"2026-06-28T10:56:24.138197Z","title":"Causal-learn: Causal discovery in python.Journal of Machine Learning Research, 25(60):1–8, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:133c98b9e3d8f312dd65ad5a38da0bee74e57b2aa99b013602ee85629a8b0fac","observation_id":"5ad9780a-b270-4d99-96ec-6940a909f166","resolution":{"observed_at":"2026-06-28T10:56:24.138197Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.15155","last_updated":"2021-11-30T06:27:40Z","snapshot_observed_at":"2026-07-06T12:13:31.730614Z","submitted_at":"2021-11-30T06:27:40Z","title":"gCastle: A Python Toolbox for Causal Discovery","version":1},"cited_work":{"arxiv_id":"2111.15155","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2111.15155","snapshot_observed_at":"2026-07-02T02:26:26.630431Z","title":"gcastle: A python toolbox for causal discovery","venue":null,"work_id":"87f903b4-89da-4d7d-b619-06532a5615ac","year":2021},"citing_paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-06-28T10:56:24.138197Z"},"links":{"cited_paper":"/paper/2111.15155","citing_paper":"/paper/2606.03602"},"observation_digest":"sha256:0bf60d67a3a5e9b8705971726c08d3d4aa5a70f4c5c6bf2a7571618c90e8a4eb","observation_id":"9f5117a8-60c9-4360-8bb8-ee453b471161","resolution":{"observed_at":"2026-07-02T02:26:26.631954Z","resolver_source":"arxiv_id","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2606.03602","last_updated":"2026-06-02T13:07:43Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T23:43:50.943530Z","submitted_at":"2026-06-02T13:07:43Z","title":"CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery"},"reference_resolution":{"displayed":38,"state_counts":{"malformed_identifier":1,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":35,"verified_exact":1,"verified_fuzzy":0},"total_outbound_references":38},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2606.03602."}