{"as_of":"2026-08-10T08:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f5ef58feaff6cb9042f470a0fd5083cc39bd46ddd5bf4448272971c8df14b605","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":27,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":27,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":27,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":27,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T16:21:13.507624Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":20,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2106.13314","last_updated":"2021-06-24T21:00:28Z","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T21:00:28Z","title":"Promises and Pitfalls of Black-Box Concept Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.13314","snapshot_observed_at":"2026-08-09T16:21:13.507624Z","title":"Promises and pitfalls of black-box concept learning models","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.01191","last_updated":"2025-02-03T09:29:39Z","snapshot_observed_at":"2026-08-10T05:18:25.182251Z","submitted_at":"2025-02-03T09:29:39Z","title":"Towards Robust and Reliable Concept Representations: Reliability-Enhanced Concept Embedding Model","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-09T16:21:13.507624Z"},"links":{"cited_paper":"/paper/2106.13314","citing_paper":"/paper/2502.01191"},"observation_digest":"sha256:8f8c56f0c7f951e04727d1fc9a5e4abc1a1761de0dd5e136ad02b47762fdb002","observation_id":"959d1b59-4b21-4332-a6c2-48abd5d0615f","resolution":{"observed_at":"2026-08-09T16:21:13.507624Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.13314","last_updated":"2021-06-24T21:00:28Z","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T21:00:28Z","title":"Promises and Pitfalls of Black-Box Concept Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.13314","snapshot_observed_at":"2026-08-08T17:19:58.507640Z","title":"Mahinpei, J","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.05950","last_updated":"2025-02-09T16:41:04Z","snapshot_observed_at":"2026-08-08T17:13:57.017018Z","submitted_at":"2025-02-09T16:41:04Z","title":"Survival Concept-Based Learning Models","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-08T17:19:58.507640Z"},"links":{"cited_paper":"/paper/2106.13314","citing_paper":"/paper/2502.05950"},"observation_digest":"sha256:c958ae13e4493edef9beb39d41db9e274c6f384da90e6ff316ba8306917b7809","observation_id":"fd81d536-d05b-4785-b314-89e0932cb382","resolution":{"observed_at":"2026-08-08T17:19:58.507640Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.13314","last_updated":"2021-06-24T21:00:28Z","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T21:00:28Z","title":"Promises and Pitfalls of Black-Box Concept Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.13314","snapshot_observed_at":"2026-08-07T15:42:34.599256Z","title":"arXiv preprint arXiv:2106.13314 (2021)","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.14049","last_updated":"2025-06-09T05:45:56Z","snapshot_observed_at":"2026-08-10T04:05:03.751615Z","submitted_at":"2025-05-20T07:48:33Z","title":"Learning Concept-Driven Logical Rules for Interpretable and Generalizable Medical Image Classification","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T15:42:34.599256Z"},"links":{"cited_paper":"/paper/2106.13314","citing_paper":"/paper/2505.14049"},"observation_digest":"sha256:7fe23c01e465e087a6cea2117e39552749217950cdb1435fffe8f3cc0110b470","observation_id":"722369bb-8e35-455e-a6da-3dec3f4491bf","resolution":{"observed_at":"2026-08-07T15:42:34.599256Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.13314","last_updated":"2021-06-24T21:00:28Z","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T21:00:28Z","title":"Promises and Pitfalls of Black-Box Concept Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.13314","snapshot_observed_at":"2026-08-07T14:24:33.677368Z","title":"Promises and pitfalls of black-box concept learning models.arXiv preprint arXiv:2106.13314,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.04237","last_updated":"2025-05-25T03:53:26Z","snapshot_observed_at":"2026-08-09T19:58:23.937930Z","submitted_at":"2025-05-25T03:53:26Z","title":"A Comprehensive Survey on the Risks and Limitations of Concept-based Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T14:24:33.677368Z"},"links":{"cited_paper":"/paper/2106.13314","citing_paper":"/paper/2506.04237"},"observation_digest":"sha256:d07fbe7fc6a8b1614d76bb2406a08fb5ed50d820dea93caf6282dc54dd0ba4f4","observation_id":"e6573d2b-dbbb-4196-998e-e30ae835cfba","resolution":{"observed_at":"2026-08-07T14:24:33.677368Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.13314","last_updated":"2021-06-24T21:00:28Z","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T21:00:28Z","title":"Promises and Pitfalls of Black-Box Concept Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.13314","snapshot_observed_at":"2026-08-06T22:42:25.138243Z","title":"Promises and pitfalls of black-box concept learning models.arXiv preprint arXiv:2106.13314, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.21102","last_updated":"2025-06-26T08:56:55Z","snapshot_observed_at":"2026-08-06T22:31:44.929517Z","submitted_at":"2025-06-26T08:56:55Z","title":"Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T22:42:25.138243Z"},"links":{"cited_paper":"/paper/2106.13314","citing_paper":"/paper/2506.21102"},"observation_digest":"sha256:ecdd3347e7a652e8034bc88e6cbb07429380fb542b325976fe0be840a26b0d32","observation_id":"1f2504b7-f7db-4e49-8bc4-cf4f9144ecc7","resolution":{"observed_at":"2026-08-06T22:42:25.138243Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.13314","last_updated":"2021-06-24T21:00:28Z","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T21:00:28Z","title":"Promises and Pitfalls of Black-Box Concept Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.13314","snapshot_observed_at":"2026-08-06T19:22:35.085262Z","title":"Promises and pitfalls of black-box concept learning models.arXiv preprint arXiv:2106.13314, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.05810","last_updated":"2025-07-08T09:30:20Z","snapshot_observed_at":"2026-08-06T19:15:08.634417Z","submitted_at":"2025-07-08T09:30:20Z","title":"Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T19:22:35.085262Z"},"links":{"cited_paper":"/paper/2106.13314","citing_paper":"/paper/2507.05810"},"observation_digest":"sha256:bd0b6348343f25265c5646d248ca27b1a78bc906accb941eb83b057475e75aec","observation_id":"68a6465a-8dcf-4b7d-ac69-92aeded309ec","resolution":{"observed_at":"2026-08-06T19:22:35.085262Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.13314","last_updated":"2021-06-24T21:00:28Z","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T21:00:28Z","title":"Promises and Pitfalls of Black-Box Concept Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.13314","snapshot_observed_at":"2026-08-06T18:45:20.560966Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.07532","last_updated":"2026-06-19T16:08:31Z","snapshot_observed_at":"2026-08-07T21:45:50.466083Z","submitted_at":"2025-07-10T08:28:46Z","title":"Neural Concept Verifier: Scaling Prover-Verifier Games via Concept Encodings","version":4},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-06T18:45:20.560966Z"},"links":{"cited_paper":"/paper/2106.13314","citing_paper":"/paper/2507.07532"},"observation_digest":"sha256:3dcdd994adca076e1eff1d33558e9da0997f875ed21a9f500eb1de4a1ba22846","observation_id":"c245e3bb-3d07-4d3f-bd2e-2951dca8bf04","resolution":{"observed_at":"2026-08-06T18:45:20.560966Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.13314","last_updated":"2021-06-24T21:00:28Z","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T21:00:28Z","title":"Promises and Pitfalls of Black-Box Concept Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.13314","snapshot_observed_at":"2026-08-03T18:33:04.976353Z","title":"Promises and pitfalls of black-box concept learning models.ArXiv, abs/2106.13314, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2512.05038","last_updated":"2026-05-29T17:42:38Z","snapshot_observed_at":"2026-08-07T15:13:01.011752Z","submitted_at":"2025-12-04T17:55:55Z","title":"The SuperActivator Mechanism: Transformers Concentrate Reliable Concept Signals in the Tail","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-03T18:33:04.976353Z"},"links":{"cited_paper":"/paper/2106.13314","citing_paper":"/paper/2512.05038"},"observation_digest":"sha256:1b19f9f0957633389c31df66a15d35f69b19685e5aa5057c81fa8b85d80e77de","observation_id":"31d9d04e-2817-4b3e-b37d-fab4c5bd9833","resolution":{"observed_at":"2026-08-03T18:33:04.976353Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.13314","last_updated":"2021-06-24T21:00:28Z","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T21:00:28Z","title":"Promises and Pitfalls of Black-Box Concept Learning Models","version":1},"cited_work":{"arxiv_id":"2106.13314","doi":"10.48550/arxiv.2106.13314","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.13314","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Promises and pitfalls of black-box concept learning models","venue":"arXiv (Cornell University)","work_id":"b86575c4-ab88-4b9c-82b0-eb5758b8c5f3","year":2021},"citing_paper":{"arxiv_id":"2604.16042","last_updated":"2026-04-20T05:23:39Z","snapshot_observed_at":"2026-07-06T23:03:26.308324Z","submitted_at":"2026-04-17T13:15:46Z","title":"Towards Intrinsic Interpretability of Large Language Models:A Survey of Design Principles and Architectures","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-10T08:26:30.556480Z"},"links":{"cited_paper":"/paper/2106.13314","citing_paper":"/paper/2604.16042"},"observation_digest":"sha256:f7b101e9cf53325a9bf3b55dd443f1a5f97b1617dfc7961371be79172dbae367","observation_id":"b520773b-03ec-4104-ac92-5244d44bdb55","resolution":{"observed_at":"2026-05-10T08:27:51.532641Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.13314","last_updated":"2021-06-24T21:00:28Z","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T21:00:28Z","title":"Promises and Pitfalls of Black-Box Concept Learning Models","version":1},"cited_work":{"arxiv_id":"2106.13314","doi":"10.48550/arxiv.2106.13314","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.13314","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Promises and pitfalls of black-box concept learning models","venue":"arXiv (Cornell University)","work_id":"b86575c4-ab88-4b9c-82b0-eb5758b8c5f3","year":2021},"citing_paper":{"arxiv_id":"2604.16076","last_updated":"2026-05-21T13:26:38Z","snapshot_observed_at":"2026-07-06T23:03:30.601598Z","submitted_at":"2026-04-17T14:04:14Z","title":"Prototype-Grounded Concept Models for Verifiable Concept Alignment","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-05-10T08:19:12.302781Z"},"links":{"cited_paper":"/paper/2106.13314","citing_paper":"/paper/2604.16076"},"observation_digest":"sha256:d627a07cabed59bb2f7e71985fe920f7f33984ded0de7885cdfc423e29f3e98a","observation_id":"ab9136d5-fc8d-4787-ad29-f24fbb427d32","resolution":{"observed_at":"2026-05-10T08:22:37.454641Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.13314","last_updated":"2021-06-24T21:00:28Z","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T21:00:28Z","title":"Promises and Pitfalls of Black-Box Concept Learning Models","version":1},"cited_work":{"arxiv_id":"2106.13314","doi":"10.48550/arxiv.2106.13314","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.13314","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Promises and pitfalls of black-box concept learning models","venue":"arXiv (Cornell University)","work_id":"b86575c4-ab88-4b9c-82b0-eb5758b8c5f3","year":2021},"citing_paper":{"arxiv_id":"2604.16076","last_updated":"2026-05-21T13:26:38Z","snapshot_observed_at":"2026-07-06T23:03:30.601598Z","submitted_at":"2026-04-17T14:04:14Z","title":"Prototype-Grounded Concept Models for Verifiable Concept Alignment","version":2},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-05-22T10:06:31.673188Z"},"links":{"cited_paper":"/paper/2106.13314","citing_paper":"/paper/2604.16076"},"observation_digest":"sha256:c7449f75b4d32c8b00cfeb1111e0bdc2ecaaa508cb1d24c06ba64cdb057b2ab8","observation_id":"ea99be16-d30c-4ccf-b73e-5c8af2008ae8","resolution":{"observed_at":"2026-05-22T10:11:23.497048Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.13314","last_updated":"2021-06-24T21:00:28Z","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T21:00:28Z","title":"Promises and Pitfalls of Black-Box Concept Learning Models","version":1},"cited_work":{"arxiv_id":"2106.13314","doi":"10.48550/arxiv.2106.13314","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.13314","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Promises and pitfalls of black-box concept learning models","venue":"arXiv (Cornell University)","work_id":"b86575c4-ab88-4b9c-82b0-eb5758b8c5f3","year":2021},"citing_paper":{"arxiv_id":"2604.19323","last_updated":"2026-04-21T10:45:50Z","snapshot_observed_at":"2026-07-06T23:05:58.167533Z","submitted_at":"2026-04-21T10:45:50Z","title":"Concept Inconsistency in Dermoscopic Concept Bottleneck Models: A Rough-Set Analysis of the Derm7pt Dataset","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-10T02:21:54.577723Z"},"links":{"cited_paper":"/paper/2106.13314","citing_paper":"/paper/2604.19323"},"observation_digest":"sha256:738b5d29a97b29c3ded656127e88df39e9be783930d5d5723dea8365d6ed7094","observation_id":"bfcad548-2e19-419f-b719-006263d0da76","resolution":{"observed_at":"2026-05-10T02:22:20.198451Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.13314","last_updated":"2021-06-24T21:00:28Z","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T21:00:28Z","title":"Promises and Pitfalls of Black-Box Concept Learning Models","version":1},"cited_work":{"arxiv_id":"2106.13314","doi":"10.48550/arxiv.2106.13314","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.13314","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Promises and pitfalls of black-box concept learning models","venue":"arXiv (Cornell University)","work_id":"b86575c4-ab88-4b9c-82b0-eb5758b8c5f3","year":2021},"citing_paper":{"arxiv_id":"2605.07140","last_updated":"2026-05-08T02:20:39Z","snapshot_observed_at":"2026-07-06T23:19:30.387067Z","submitted_at":"2026-05-08T02:20:39Z","title":"Neurosymbolic Framework for Concept-Driven Logical Reasoning in Skeleton-Based Human Action Recognition","version":1},"reference_index":73,"source":"arxiv_source","source_observed_at":"2026-05-11T02:40:52.171346Z"},"links":{"cited_paper":"/paper/2106.13314","citing_paper":"/paper/2605.07140"},"observation_digest":"sha256:903d49e2a4347fdcb2de9d0ef824aba19540b47e1b57c86366dd4d5fac62b370","observation_id":"8c3fc5fe-cab3-4537-bba1-9adda6b475a5","resolution":{"observed_at":"2026-05-11T02:45:58.783166Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.13314","last_updated":"2021-06-24T21:00:28Z","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T21:00:28Z","title":"Promises and Pitfalls of Black-Box Concept Learning Models","version":1},"cited_work":{"arxiv_id":"2106.13314","doi":"10.48550/arxiv.2106.13314","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.13314","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Promises and pitfalls of black-box concept learning models","venue":"arXiv (Cornell University)","work_id":"b86575c4-ab88-4b9c-82b0-eb5758b8c5f3","year":2021},"citing_paper":{"arxiv_id":"2605.08482","last_updated":"2026-05-08T20:58:52Z","snapshot_observed_at":"2026-07-06T23:20:43.010758Z","submitted_at":"2026-05-08T20:58:52Z","title":"ShifaMind: A Multiplicative Concept Bottleneck for Interpretable ICD-10 Coding","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-12T02:25:16.535165Z"},"links":{"cited_paper":"/paper/2106.13314","citing_paper":"/paper/2605.08482"},"observation_digest":"sha256:c4332d6d52987c832c35819517ae9921c1312db5c12fa75374a551c8ee7136bd","observation_id":"fde040ac-1b09-4d28-b2f9-efabb28a7e3d","resolution":{"observed_at":"2026-05-12T07:37:19.127254Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.13314","last_updated":"2021-06-24T21:00:28Z","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T21:00:28Z","title":"Promises and Pitfalls of Black-Box Concept Learning Models","version":1},"cited_work":{"arxiv_id":"2106.13314","doi":"10.48550/arxiv.2106.13314","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.13314","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Promises and pitfalls of black-box concept learning models","venue":"arXiv (Cornell University)","work_id":"b86575c4-ab88-4b9c-82b0-eb5758b8c5f3","year":2021},"citing_paper":{"arxiv_id":"2605.20693","last_updated":"2026-05-20T04:41:44Z","snapshot_observed_at":"2026-07-06T23:31:13.042581Z","submitted_at":"2026-05-20T04:41:44Z","title":"Interpretable Discriminative Text Representations via Agreement and Label Disentanglement","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-21T05:45:10.111617Z"},"links":{"cited_paper":"/paper/2106.13314","citing_paper":"/paper/2605.20693"},"observation_digest":"sha256:bfaabf75e9b1ca2ed8f2b26e925818e958a0c1bfe93e3972cd3bf3b9cd94b848","observation_id":"33883d4e-4413-41dc-b283-b24eb07b4c05","resolution":{"observed_at":"2026-05-21T05:49:41.178358Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.13314","last_updated":"2021-06-24T21:00:28Z","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T21:00:28Z","title":"Promises and Pitfalls of Black-Box Concept Learning Models","version":1},"cited_work":{"arxiv_id":"2106.13314","doi":"10.48550/arxiv.2106.13314","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.13314","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Promises and pitfalls of black-box concept learning models","venue":"arXiv (Cornell University)","work_id":"b86575c4-ab88-4b9c-82b0-eb5758b8c5f3","year":2021},"citing_paper":{"arxiv_id":"2605.20908","last_updated":"2026-05-20T08:54:23Z","snapshot_observed_at":"2026-08-01T00:14:50.768886Z","submitted_at":"2026-05-20T08:54:23Z","title":"SynCB: A Synergy Concept-Based Model with Dynamic Routing Between Concepts and Complementary Neural Branches","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-21T05:46:42.018851Z"},"links":{"cited_paper":"/paper/2106.13314","citing_paper":"/paper/2605.20908"},"observation_digest":"sha256:14ef1a3e45301826d2f3d46851610305af822bc5e971a5ad605349d3e51bc121","observation_id":"d9844233-6b25-4039-8261-29cfad58c84c","resolution":{"observed_at":"2026-05-21T05:49:40.993164Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.13314","last_updated":"2021-06-24T21:00:28Z","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T21:00:28Z","title":"Promises and Pitfalls of Black-Box Concept Learning Models","version":1},"cited_work":{"arxiv_id":"2106.13314","doi":"10.48550/arxiv.2106.13314","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.13314","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Promises and pitfalls of black-box concept learning models","venue":"arXiv (Cornell University)","work_id":"b86575c4-ab88-4b9c-82b0-eb5758b8c5f3","year":2021},"citing_paper":{"arxiv_id":"2605.29836","last_updated":"2026-05-28T12:16:41Z","snapshot_observed_at":"2026-08-04T17:02:42.545515Z","submitted_at":"2026-05-28T12:16:41Z","title":"CB-SLICE: Concept-Based Interpretable Error Slice Discovery","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-29T08:24:05.284777Z"},"links":{"cited_paper":"/paper/2106.13314","citing_paper":"/paper/2605.29836"},"observation_digest":"sha256:0176bc9628e34c317c9ed9a3845ef677863bf2cb346ddd01c8e4b6333d3dac7c","observation_id":"7ef03645-5219-418d-9825-25afba86d504","resolution":{"observed_at":"2026-06-29T08:33:15.860345Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.13314","last_updated":"2021-06-24T21:00:28Z","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T21:00:28Z","title":"Promises and Pitfalls of Black-Box Concept Learning Models","version":1},"cited_work":{"arxiv_id":"2106.13314","doi":"10.48550/arxiv.2106.13314","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.13314","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Promises and pitfalls of black-box concept learning models","venue":"arXiv (Cornell University)","work_id":"b86575c4-ab88-4b9c-82b0-eb5758b8c5f3","year":2021},"citing_paper":{"arxiv_id":"2606.04326","last_updated":"2026-06-03T01:01:05Z","snapshot_observed_at":"2026-07-06T23:44:28.265478Z","submitted_at":"2026-06-03T01:01:05Z","title":"Measuring What Matters: Synthetic Benchmarks for Concept Bottleneck Models","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-06-28T07:27:56.163337Z"},"links":{"cited_paper":"/paper/2106.13314","citing_paper":"/paper/2606.04326"},"observation_digest":"sha256:10293b85aff1351e9af17350c0fb7027d4d42d6a8fae12ca9d28199c841f40c1","observation_id":"bcfa27bf-44c4-4ca7-bc7c-294524553fff","resolution":{"observed_at":"2026-07-02T06:16:43.815993Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.13314","last_updated":"2021-06-24T21:00:28Z","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T21:00:28Z","title":"Promises and Pitfalls of Black-Box Concept Learning Models","version":1},"cited_work":{"arxiv_id":"2106.13314","doi":"10.48550/arxiv.2106.13314","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.13314","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Promises and pitfalls of black-box concept learning models","venue":"arXiv (Cornell University)","work_id":"b86575c4-ab88-4b9c-82b0-eb5758b8c5f3","year":2021},"citing_paper":{"arxiv_id":"2606.04364","last_updated":"2026-07-28T16:07:25Z","snapshot_observed_at":"2026-08-02T12:29:43.205581Z","submitted_at":"2026-06-03T02:28:42Z","title":"Spatially Grounded Concept Bottleneck Models via Part-Factorized Attention","version":2},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-06-28T07:10:25.012343Z"},"links":{"cited_paper":"/paper/2106.13314","citing_paper":"/paper/2606.04364"},"observation_digest":"sha256:7b6ca39c66209810d4cc2a69cb3f92246fe5672fdfe569a262cd3fab570caf82","observation_id":"c47f997e-3b37-4eae-b500-f21f6f2464b5","resolution":{"observed_at":"2026-07-02T07:06:44.253014Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.13314","last_updated":"2021-06-24T21:00:28Z","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T21:00:28Z","title":"Promises and Pitfalls of Black-Box Concept Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.13314","snapshot_observed_at":"2026-08-02T12:29:44.177803Z","title":"Promises and pitfalls of black-box concept learning models.arXiv preprint arXiv:2106.13314,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2606.04364","last_updated":"2026-07-28T16:07:25Z","snapshot_observed_at":"2026-08-02T12:29:43.205581Z","submitted_at":"2026-06-03T02:28:42Z","title":"Spatially Grounded Concept Bottleneck Models via Part-Factorized Attention","version":3},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-02T12:29:44.177803Z"},"links":{"cited_paper":"/paper/2106.13314","citing_paper":"/paper/2606.04364"},"observation_digest":"sha256:229eac36360ace154ec1fedd25b2a6c73ea2fcafa69d07df48c5b9f8a337e845","observation_id":"f8c0e68a-2655-40cd-954b-d6ae9e906aff","resolution":{"observed_at":"2026-08-02T12:29:44.177803Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.13314","last_updated":"2021-06-24T21:00:28Z","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T21:00:28Z","title":"Promises and Pitfalls of Black-Box Concept Learning Models","version":1},"cited_work":{"arxiv_id":"2106.13314","doi":"10.48550/arxiv.2106.13314","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.13314","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Promises and pitfalls of black-box concept learning models","venue":"arXiv (Cornell University)","work_id":"b86575c4-ab88-4b9c-82b0-eb5758b8c5f3","year":2021},"citing_paper":{"arxiv_id":"2606.10669","last_updated":"2026-06-09T10:19:41Z","snapshot_observed_at":"2026-08-10T03:56:22.167570Z","submitted_at":"2026-06-09T10:19:41Z","title":"In Defense of Information Leakage in Concept-based Models","version":1},"reference_index":185,"source":"arxiv_source","source_observed_at":"2026-06-27T13:43:55.952527Z"},"links":{"cited_paper":"/paper/2106.13314","citing_paper":"/paper/2606.10669"},"observation_digest":"sha256:e1324f1a7e3fdd197ddab4eec4ab1cdb8099ea4a56b7925edb029a8b9e5daa7c","observation_id":"d8dec802-fb56-4b52-b2cc-9e1a4f92f8b7","resolution":{"observed_at":"2026-07-03T04:37:37.639600Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.13314","last_updated":"2021-06-24T21:00:28Z","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T21:00:28Z","title":"Promises and Pitfalls of Black-Box Concept Learning Models","version":1},"cited_work":{"arxiv_id":"2106.13314","doi":"10.48550/arxiv.2106.13314","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.13314","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Promises and pitfalls of black-box concept learning models","venue":"arXiv (Cornell University)","work_id":"b86575c4-ab88-4b9c-82b0-eb5758b8c5f3","year":2021},"citing_paper":{"arxiv_id":"2606.19489","last_updated":"2026-06-17T18:27:17Z","snapshot_observed_at":"2026-08-08T00:47:44.045909Z","submitted_at":"2026-06-17T18:27:17Z","title":"Concept Flow Models: Anchoring Concept-Based Reasoning with Hierarchical Bottlenecks","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-06-26T21:16:21.252304Z"},"links":{"cited_paper":"/paper/2106.13314","citing_paper":"/paper/2606.19489"},"observation_digest":"sha256:2defb9390bc0f72d2ab91c29d0a14ea0ab173a47a42cdd4c93028715f2c362cd","observation_id":"3a22dfab-61dd-4242-9fb6-c62cdae647d7","resolution":{"observed_at":"2026-07-04T00:19:13.795086Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.13314","last_updated":"2021-06-24T21:00:28Z","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T21:00:28Z","title":"Promises and Pitfalls of Black-Box Concept Learning Models","version":1},"cited_work":{"arxiv_id":"2106.13314","doi":"10.48550/arxiv.2106.13314","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.13314","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Promises and pitfalls of black-box concept learning models","venue":"arXiv (Cornell University)","work_id":"b86575c4-ab88-4b9c-82b0-eb5758b8c5f3","year":2021},"citing_paper":{"arxiv_id":"2606.30498","last_updated":"2026-06-29T16:02:29Z","snapshot_observed_at":"2026-08-07T11:28:41.025852Z","submitted_at":"2026-06-29T16:02:29Z","title":"On the Faithfulness of Post-Hoc Concept Bottleneck Models","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-06-30T06:36:27.764509Z"},"links":{"cited_paper":"/paper/2106.13314","citing_paper":"/paper/2606.30498"},"observation_digest":"sha256:ad239748ad83cf5695cfaa0b3b5e5ecc293bff31fc13bb25041cd3d4b6c7fada","observation_id":"9a5eea8f-0b90-4a0d-b933-888fbe3bd349","resolution":{"observed_at":"2026-06-30T06:44:19.515508Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.13314","last_updated":"2021-06-24T21:00:28Z","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T21:00:28Z","title":"Promises and Pitfalls of Black-Box Concept Learning Models","version":1},"cited_work":{"arxiv_id":"2106.13314","doi":"10.48550/arxiv.2106.13314","metadata_source":"arxiv_reference","pith_arxiv_id":"2106.13314","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Promises and pitfalls of black-box concept learning models","venue":"arXiv (Cornell University)","work_id":"b86575c4-ab88-4b9c-82b0-eb5758b8c5f3","year":2021},"citing_paper":{"arxiv_id":"2607.00578","last_updated":"2026-07-01T08:00:48Z","snapshot_observed_at":"2026-08-08T06:39:32.150155Z","submitted_at":"2026-07-01T08:00:48Z","title":"Caption Bottleneck Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-02T14:58:06.673957Z"},"links":{"cited_paper":"/paper/2106.13314","citing_paper":"/paper/2607.00578"},"observation_digest":"sha256:859e1b89e9d7679679b16ca055caf313eb6761e5b047897d8b75c0033ed3dafe","observation_id":"f4e42de6-bd6c-4b7f-87b1-b86141ece829","resolution":{"observed_at":"2026-07-02T15:07:04.092790Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.13314","last_updated":"2021-06-24T21:00:28Z","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T21:00:28Z","title":"Promises and Pitfalls of Black-Box Concept Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.13314","snapshot_observed_at":"2026-07-13T01:29:02.336552Z","title":"Promises and pitfalls of black-box concept learning models.arXiv preprint arXiv:2106.13314, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.09649","last_updated":"2026-07-10T17:47:38Z","snapshot_observed_at":"2026-08-06T09:56:18.408208Z","submitted_at":"2026-07-10T17:47:38Z","title":"ConceptSMILE: Auditing the Trustworthiness of Concept-Based Explainable AI","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-13T01:29:02.336552Z"},"links":{"cited_paper":"/paper/2106.13314","citing_paper":"/paper/2607.09649"},"observation_digest":"sha256:4ae70bc18a248d2845e0fd8e8fd860f25ac227628a55880b386c766d9fc0f6c6","observation_id":"82c09e5b-7390-4f12-9867-25e5312f2a97","resolution":{"observed_at":"2026-07-13T01:29:02.336552Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.13314","last_updated":"2021-06-24T21:00:28Z","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T21:00:28Z","title":"Promises and Pitfalls of Black-Box Concept Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.13314","snapshot_observed_at":"2026-08-01T10:03:01.989420Z","title":"arXiv preprint arXiv:2106.13314 , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.20379","last_updated":"2026-07-22T17:10:23Z","snapshot_observed_at":"2026-08-07T23:15:05.523185Z","submitted_at":"2026-07-22T17:10:23Z","title":"Train the Model, Not the Reader: Decodability Supervision for Verifiable Activation Explanations","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-01T10:03:01.989420Z"},"links":{"cited_paper":"/paper/2106.13314","citing_paper":"/paper/2607.20379"},"observation_digest":"sha256:48e941404abf4e6d46718fcfeedd9a600626deb742fc43a08745e1bf264a2de7","observation_id":"37ade2a8-e326-4dde-b13e-80466244493c","resolution":{"observed_at":"2026-08-01T10:03:01.989420Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.13314","last_updated":"2021-06-24T21:00:28Z","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T21:00:28Z","title":"Promises and Pitfalls of Black-Box Concept Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.13314","snapshot_observed_at":"2026-08-01T01:40:43.864735Z","title":"Promises and pitfalls of black-box concept learning models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.25748","last_updated":"2026-07-28T14:11:10Z","snapshot_observed_at":"2026-08-06T02:42:14.203227Z","submitted_at":"2026-07-28T14:11:10Z","title":"Loss Invariance Determines What Concept Layers Encode: Volume Grounding in Echocardiography","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-01T01:40:43.864735Z"},"links":{"cited_paper":"/paper/2106.13314","citing_paper":"/paper/2607.25748"},"observation_digest":"sha256:ae7b5deec366d4c45a5a0b2c7a7f67244f41708170c56c4aa38e50951d77e252","observation_id":"3094537a-9b59-49d8-9cd5-559d2f84fa80","resolution":{"observed_at":"2026-08-01T01:40:43.864735Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2106.13314/citation-record","integrity":"/paper/2106.13314/integrity","json":"/paper/2106.13314/citation-record.json","paper":"/paper/2106.13314"},"outbound":[],"paper":{"arxiv_id":"2106.13314","last_updated":"2021-06-24T21:00:28Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T11:22:55.799017Z","submitted_at":"2021-06-24T21:00:28Z","title":"Promises and Pitfalls of Black-Box Concept Learning Models"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 27 inbound Pith citation observations for arXiv:2106.13314."}