{"as_of":"2026-08-13T14:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:394b189102655169fd4858d9b83b3a367bd5ce6ea51dd247b2d20da09385afa6","coverage":[{"denominator":291,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T05:01:31.879510Z","state":"measured"},{"denominator":102,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":102,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-30T09:54:05.683478Z","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":"2026-06-30T09:54:34.440547Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"cited_work":{"arxiv_id":"2412.00800","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.00800","snapshot_observed_at":"2026-06-30T09:54:34.440547Z","title":"Grafana Labs","venue":null,"work_id":"5d1e883d-5b42-45d0-b4ba-3045d0bf7200","year":2024},"citing_paper":{"arxiv_id":"2512.13956","last_updated":"2026-04-28T12:10:07Z","snapshot_observed_at":"2026-07-06T22:39:08.850289Z","submitted_at":"2025-12-15T23:22:02Z","title":"AOI: Context-Aware Multi-Agent Operations via Dynamic Scheduling and Hierarchical Memory Compression","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-16T21:25:47.615557Z"},"links":{"cited_paper":"/paper/2412.00800","citing_paper":"/paper/2512.13956"},"observation_digest":"sha256:38bff0d05342f84e7671356979047a9e05db3664237f989a07782bbfd097dcae","observation_id":"25460c5d-9d3c-4e49-b126-334954d11087","resolution":{"observed_at":"2026-05-16T21:28:34.035794Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"cited_work":{"arxiv_id":"2412.00800","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.00800","snapshot_observed_at":"2026-06-30T09:54:34.440547Z","title":"Grafana Labs","venue":null,"work_id":"5d1e883d-5b42-45d0-b4ba-3045d0bf7200","year":2024},"citing_paper":{"arxiv_id":"2606.28943","last_updated":"2026-06-27T14:30:43Z","snapshot_observed_at":"2026-07-31T16:02:13.460407Z","submitted_at":"2026-06-27T14:30:43Z","title":"A3M: Adaptive, Adversarial and Multi-Objective Learning for Strategic Bidding in Repeated Auctions","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-06-30T09:54:05.683478Z"},"links":{"cited_paper":"/paper/2412.00800","citing_paper":"/paper/2606.28943"},"observation_digest":"sha256:0672dd1e0fa66ce97d155fc234cd7286fe62a2c9510988cd50dd31443447c47c","observation_id":"605b29a7-85f7-44fe-bf69-7695a1c04804","resolution":{"observed_at":"2026-06-30T09:54:34.442358Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.00800/citation-record","integrity":"/paper/2412.00800/integrity","json":"/paper/2412.00800/citation-record.json","paper":"/paper/2412.00800"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T05:01:31.408715Z","title":"Jordan and Tom M","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.408715Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:1e97bfb5f5379806999981860af7fcbccdaafc99e3de47f20e72a3a5dd66af77","observation_id":"e5f4d176-5c84-4e65-af3c-4c5721e26fa1","resolution":{"observed_at":"2026-08-12T05:01:31.408715Z","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":"2026-08-12T05:01:31.429786Z","title":"Gilpin, David Bau, Ben Z","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.429786Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:d892e5814ccf5160f17fdc15af5758299eafc145219e53b6699dd129ccb898b9","observation_id":"9cf224a3-ecd7-4d0f-8e69-14cc7b796bcb","resolution":{"observed_at":"2026-08-12T05:01:31.429786Z","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":"2026-08-12T05:01:31.434762Z","title":"General data protection regulation (gdpr)","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.434762Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:c966945637766c32fe5993c0cccf6924cb5e87c81276adf6df1d629af53e169c","observation_id":"8ef13d32-ce83-4f6b-b81b-d0b264892bba","resolution":{"observed_at":"2026-08-12T05:01:31.434762Z","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":"2026-08-12T05:01:31.440767Z","title":"European union regulations on algorithmic decision- making and a â ˘AIJright to explanationâ ˘A˙I","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.440767Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:44c60a12f0ee39a46e0b57db36fbcc73c446797a7336dc7bb910605700ae4bd4","observation_id":"156568f2-aa8b-43b7-b5fc-727c61b2710e","resolution":{"observed_at":"2026-08-12T05:01:31.440767Z","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":"2026-08-12T05:01:31.456525Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.456525Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:dddb8de2cd49b418a9f5f75a0f3667b4503dc134d3c42a8bb21ed788bac299c8","observation_id":"a67d992d-4832-4ffd-8b1a-f8ddb6b12ed9","resolution":{"observed_at":"2026-08-12T05:01:31.456525Z","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":"2026-08-12T05:01:31.460869Z","title":"Explainable artificial intelligence (xai): Concepts, taxonomies, opportunities and challenges toward responsible ai","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.460869Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:6ed545543ac6598f2ca5211726c0fc00111bc3f6973e4d63789a71fa427dadce","observation_id":"b5b1e08b-a8ec-46fa-94a1-f7e2970d66a9","resolution":{"observed_at":"2026-08-12T05:01:31.460869Z","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":"2026-08-12T05:01:31.465280Z","title":"Explanation in artificial intelligence: Insights from the social sciences","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.465280Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:46508fb2647b5ebebc733f25dfad110a40319bafb3ab405c270ca63412ba6e4d","observation_id":"78e210f1-9b2d-40d9-94f2-240c9d6b77c4","resolution":{"observed_at":"2026-08-12T05:01:31.465280Z","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":"2026-08-12T05:01:31.469571Z","title":"The Elements of Statistical Learning","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.469571Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:aeb0499609811339b7dd5c385db66f8677ae47932ff5f2049949ca07d57017b4","observation_id":"b3d04a02-7408-4188-a062-06230970fd52","resolution":{"observed_at":"2026-08-12T05:01:31.469571Z","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":"2026-08-12T05:01:31.473898Z","title":"Ross Quinlan","venue":null,"work_id":null,"year":1986},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.473898Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:a8bed6d2d2ccbdbcdd130813c078e36521330090e5a1be079f4b5ce0f45272b9","observation_id":"42f37589-4d37-448f-889a-56c58378633e","resolution":{"observed_at":"2026-08-12T05:01:31.473898Z","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":"2026-08-12T05:01:31.478350Z","title":"To predict and serve? Significance, 13(5):14–19, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.478350Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:ef38afae1e9ee17d8b40bd2468b78dcdca905c96558300cada54bab4b35bc47c","observation_id":"b9f4cd0a-3d68-452b-b1de-2b96133dca0c","resolution":{"observed_at":"2026-08-12T05:01:31.478350Z","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":"2026-08-12T05:01:31.482718Z","title":"Support-vector networks","venue":null,"work_id":null,"year":1995},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.482718Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:47ad2747ef871ca97aa19d08e8df8ff7717ca207c37d6646b0921859dd2d2d7c","observation_id":"12f32538-1b1a-4bf9-9923-9b3c191a720f","resolution":{"observed_at":"2026-08-12T05:01:31.482718Z","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":"2026-08-12T05:01:31.487153Z","title":"Visualizing and understanding convolutional networks","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.487153Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:8a501ef41adb855f830c5a1da9f21bbf537dad3ff1e5b352614c686c57073344","observation_id":"2dd6d75a-dca9-490a-9164-00bc7f64b978","resolution":{"observed_at":"2026-08-12T05:01:31.487153Z","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":"2026-08-12T05:01:31.491453Z","title":"Neural machine translation by jointly learning to align and translate","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.491453Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:3e53ddf40433cdd5cf77eb06889e0e10988c17b186ab5d54142e61f63f20dc80","observation_id":"3c3e2dce-6354-458f-82ef-65910f8f5bd1","resolution":{"observed_at":"2026-08-12T05:01:31.491453Z","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":"2026-08-12T05:01:31.495753Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.495753Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:debf653d515252ef579c4acf3ad03b4e588d256d8be70aea363120b06ac76f9b","observation_id":"d5575999-b8cc-4c2d-a602-d6e669b689e2","resolution":{"observed_at":"2026-08-12T05:01:31.495753Z","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":"2026-08-12T05:01:31.500020Z","title":"Improving language understanding by generative pre-training","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.500020Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:fe5cf228f500a4b2e76eed9b2775c199a4fe2a81c05224841d2ab51fce9c507c","observation_id":"da9bab9f-7b21-477b-a104-c608d3317884","resolution":{"observed_at":"2026-08-12T05:01:31.500020Z","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":"2026-08-12T05:01:31.504138Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.504138Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:21d4b28d99a49b9a5b8eef9ab502717e6e0cee4bc28ddd82f112b6daa5ff4d84","observation_id":"5fdd91a4-b384-415f-94d1-a4eda8ebf6a7","resolution":{"observed_at":"2026-08-12T05:01:31.504138Z","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":"2026-08-12T05:01:31.508601Z","title":"Unified framework for interpretable methods","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.508601Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:2d0e50284178d7b0aace5265d5a7eb836ef45beebc6c95031fdfeb237e7270ed","observation_id":"782f70f7-ca24-47b5-a420-ca696340faff","resolution":{"observed_at":"2026-08-12T05:01:31.508601Z","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":"2026-08-12T05:01:31.513218Z","title":"â ˘AIJwhy should i trust you?â ˘A˙I: Ex- plaining the predictions of any classifier","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.513218Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:d066a9b4a769a02a17ee7500a89970e05a1f37feb76b76b14502b2fc86e04192","observation_id":"d2b0fb65-5aae-4887-9a7a-44b5e4c18874","resolution":{"observed_at":"2026-08-12T05:01:31.513218Z","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":"2026-08-12T05:01:31.517776Z","title":"Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Batra","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.517776Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:9f6a92ae999f2af61021939f5e43ce7551066301cea1db1d015cd929abaac628","observation_id":"6305f8b9-e5ab-4c39-89e9-603ec61e21de","resolution":{"observed_at":"2026-08-12T05:01:31.517776Z","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":"2026-08-12T05:01:31.522075Z","title":"A survey on explainable artificial intelligence (xai): Toward medical xai","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.522075Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:755845b12942f88762c64a9fb512c923363d5e7a8b1661557a7a18ef66d844c6","observation_id":"7be8edc2-c9d1-476d-82f3-ab741b21d15b","resolution":{"observed_at":"2026-08-12T05:01:31.522075Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2009.07896","last_updated":"2020-09-16T18:57:57Z","snapshot_observed_at":"2026-07-06T09:56:16.122954Z","submitted_at":"2020-09-16T18:57:57Z","title":"Captum: A unified and generic model interpretability library for PyTorch","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2009.07896","snapshot_observed_at":"2026-08-12T05:01:31.526916Z","title":"Hager, and Kaitlyn Confundus","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.526916Z"},"links":{"cited_paper":"/paper/2009.07896","citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:d7e6f95817c3a254d241d89ab252c3e5938d119140c1bb369e3d99297a001ffc","observation_id":"616ad4f3-e103-4b1b-9f83-5e93225567f5","resolution":{"observed_at":"2026-08-12T05:01:31.526916Z","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":"2026-08-12T05:01:31.539691Z","title":"MIT press, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.539691Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:c3911bd7fd7b2d1e3ee54b915696ab4ba27613de663f886183e25fdca5ba6521","observation_id":"3abbd7f5-b726-417e-b156-7ad3851db0ef","resolution":{"observed_at":"2026-08-12T05:01:31.539691Z","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":"2026-08-12T05:01:31.544055Z","title":"A survey of methods for explaining black box models.ACM computing surveys (CSUR), 51(5):1–42, 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.544055Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:0fa45888ae5a0aaa8376f307c3390cb1aa27da0e629e599dfb73b085838e512d","observation_id":"9702238f-bc81-461b-8349-f7afc6cb826e","resolution":{"observed_at":"2026-08-12T05:01:31.544055Z","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":"2026-08-12T05:01:31.548334Z","title":"Novoa, Justin Ko, Susan M","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.548334Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:b96b0b0963387ec1ade85a748dcb5aa12209925a3e0572d55b541dc7c3b5833a","observation_id":"1392c28e-c329-4a9f-bb94-9f4889af4e96","resolution":{"observed_at":"2026-08-12T05:01:31.548334Z","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":"2026-08-12T05:01:31.552902Z","title":"Khandani, Adlar J","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.552902Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:7e71365209c78270d227965960c44bff07c90719ccb5451481aae460728b6105","observation_id":"c9d2e6ae-1946-4db1-b19c-c037ca1fabc4","resolution":{"observed_at":"2026-08-12T05:01:31.552902Z","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":"2026-08-12T05:01:31.557176Z","title":"Frey, Joshua P","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.557176Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:5bc8269b36ecb0871356c8e4f48bec1f28bda7716ed6dfeb9208b9a499d74662","observation_id":"6157e8ab-0cee-46d1-afb9-7996ed84ccd0","resolution":{"observed_at":"2026-08-12T05:01:31.557176Z","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":"2026-08-12T05:01:31.561791Z","title":"Why a right to explanation of automated decision-making does not exist in the general data protection regulation","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.561791Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:7ab9f345d924bdf636e0296d71003c547cb8447168042b61b5ba7605a738c45e","observation_id":"b41387a4-7a3c-46f4-a87d-0a7530a159b5","resolution":{"observed_at":"2026-08-12T05:01:31.561791Z","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":"2026-08-12T05:01:31.566129Z","title":"Interpretable Machine Learning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.566129Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:25f3436da6acb154797558177519aaf3b1572aee30fbb563ee9a69874d6428f7","observation_id":"b15c135e-dbac-46f9-85e0-e1aa535d40d3","resolution":{"observed_at":"2026-08-12T05:01:31.566129Z","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":"2026-08-12T05:01:31.570602Z","title":"Imagenet classification with deep con- volutional neural networks","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.570602Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:cb00375a285caed036840461fc977fb6f7026a55e913748e588925ef9821517e","observation_id":"6d30af2b-515c-4620-9cf7-432baea18a63","resolution":{"observed_at":"2026-08-12T05:01:31.570602Z","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":"2026-08-12T05:01:31.575219Z","title":"Gomez, Lukasz Kaiser, and Illia Polosukhin","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.575219Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:2b591a4a4d998613de5224ed266d137e1cc32613edaee5a90b9c5b65c3d29601","observation_id":"64ec3f5b-8d48-472d-9fa2-6fab0dfebf85","resolution":{"observed_at":"2026-08-12T05:01:31.575219Z","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":"2026-08-12T05:01:31.579626Z","title":"Methods for interpreting and understanding deep neural networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.579626Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:47fe90eeca0de7ec141c5263cfcdf7433a9dad0258c6edcd7b2eade6d542a8f3","observation_id":"56ea7cc4-8639-4174-80e0-cbe3e808fdbd","resolution":{"observed_at":"2026-08-12T05:01:31.579626Z","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":"2026-08-12T05:01:31.583890Z","title":"Visual analytics for explainable deep learning","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.583890Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:d84f37ad13706c3d623bf94992961c4cf6af7347a12d6395f6e988e234a3f935","observation_id":"3146c383-a01e-426f-842f-038d4eed7eeb","resolution":{"observed_at":"2026-08-12T05:01:31.583890Z","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":"2026-08-12T05:01:31.588624Z","title":null,"venue":null,"work_id":null,"year":1936},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.588624Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:d186ee34e14d74ce0425c7667908be0a9d2176e51ddbba9a9ee7c82d57186dc0","observation_id":"b1356fdf-2cc9-4dc7-9145-56ef249e35a4","resolution":{"observed_at":"2026-08-12T05:01:31.588624Z","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":"2026-08-12T05:01:31.592780Z","title":"A survey of methods for explaining black box models","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.592780Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:6ef81d70c17bd69f946a4b93a0e57581a13416d0b02be4a701c83bce33b98349","observation_id":"95dfbf76-3b16-4e1e-9518-51044e3125d0","resolution":{"observed_at":"2026-08-12T05:01:31.592780Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1606.03490","last_updated":"2017-03-06T08:51:10Z","snapshot_observed_at":"2026-08-13T02:42:56.805694Z","submitted_at":"2016-06-10T21:28:47Z","title":"The Mythos of Model Interpretability","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.03490","snapshot_observed_at":"2026-08-12T05:01:31.596789Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.596789Z"},"links":{"cited_paper":"/paper/1606.03490","citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:5c9a755295bb95dd87beb48bddf5a73ab1f8a5984511d02ad6c2b3a1e9c33f78","observation_id":"2be1b0e6-46a3-4108-be78-20e62eed2bc9","resolution":{"observed_at":"2026-08-12T05:01:31.596789Z","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":"2026-08-12T05:01:31.601059Z","title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.601059Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:e5680646ea253f2c0138f00d5b5aa611a2329a2d316be7660ba4d2d8ee6d5d5b","observation_id":"83ea6ec3-f8af-4052-a630-dc772322566d","resolution":{"observed_at":"2026-08-12T05:01:31.601059Z","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":"2026-08-12T05:01:31.605026Z","title":"Ross Quinlan","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.605026Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:c0af52fb7415f2adf960bfe07b594e4286318d0c7421ea06ba3d6beff01185a0","observation_id":"b1bcbe33-1d73-4994-adbf-f3f387672ac9","resolution":{"observed_at":"2026-08-12T05:01:31.605026Z","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":"2026-08-12T05:01:31.609303Z","title":"Regression shrinkage and selection via the lasso","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.609303Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:0f6fdc068abfe025b00e2175c14341cf675f00d0aff618dd88e4348e9dd1617c","observation_id":"16d168de-466e-4086-b477-3c325b8d0685","resolution":{"observed_at":"2026-08-12T05:01:31.609303Z","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":"2026-08-12T05:01:31.613661Z","title":"Random forests","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.613661Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:6c0f5a04c80503ee17bb9642bde5022e638d85d1250f6f5d4a48ef9a702ea3f9","observation_id":"15a4411f-5ec0-4e25-8268-254a642c8d54","resolution":{"observed_at":"2026-08-12T05:01:31.613661Z","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":"2026-08-12T05:01:31.618153Z","title":"Xgboost: A scalable tree boosting system","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.618153Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:f3aace93ab5f346360bf91afd45a2e3fff36d882e4f51ca39e894833c6860a20","observation_id":"cfe9c4cf-44a6-4f7f-87ce-9b1f2f14960c","resolution":{"observed_at":"2026-08-12T05:01:31.618153Z","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":"2026-08-12T05:01:31.622854Z","title":"Deep learning.Nature, 521(7553):436–444, 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.622854Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:428f0ae4dce66b41703e881fa4ef735be781c5f52abca18e944c2f46589e2330","observation_id":"85a883d9-3d3a-44db-ae31-986651a8178b","resolution":{"observed_at":"2026-08-12T05:01:31.622854Z","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":"2026-08-12T05:01:31.626816Z","title":"Decision trees and multivariate analysis, volume 1","venue":null,"work_id":null,"year":1986},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.626816Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:7e2fa7481acfe5c065807bd7c7a4f2b13f8057c9feab0ba55dd763be97c8a94f","observation_id":"dc495199-6564-4601-815f-8d8538b0c87b","resolution":{"observed_at":"2026-08-12T05:01:31.626816Z","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":"2026-08-12T05:01:31.631467Z","title":"Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.631467Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:18fa789a57738c917d1a55518ad48c4af194caa30a7e40f0fce5db4a8db40c91","observation_id":"5866d302-2b79-4bbe-81b2-33a172d1a1af","resolution":{"observed_at":"2026-08-12T05:01:31.631467Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1708.08296","last_updated":"2017-08-28T12:53:49Z","snapshot_observed_at":"2026-08-10T06:42:01.845021Z","submitted_at":"2017-08-28T12:53:49Z","title":"Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.08296","snapshot_observed_at":"2026-08-12T05:01:31.635572Z","title":"Explainable artificial intel- ligence: Understanding, visualizing and interpreting deep learning models","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.635572Z"},"links":{"cited_paper":"/paper/1708.08296","citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:852eb0334ab1e8b2bb031ba68a91c4cbec74c7b87c41c972ef8816ae5e950513","observation_id":"b7ff2d77-d35d-4e15-b388-26c4a8d5e3d0","resolution":{"observed_at":"2026-08-12T05:01:31.635572Z","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":"2026-08-12T05:01:31.639757Z","title":"Logistic Regression Explained","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.639757Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:07929e7bb70511c1271061abc65e698738839e068f1f59cfd79a29396efd2834","observation_id":"6802c0b1-27bc-43d4-a9f9-9291dc279a6e","resolution":{"observed_at":"2026-08-12T05:01:31.639757Z","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":"2026-08-12T05:01:31.644057Z","title":"Applied Logistic Regression","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.644057Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:1182774858fc8a205b50cb563548bc4422ce5332c99811f988c0be57ac1a0e99","observation_id":"22eb7052-d575-4758-9b50-d25a41d84125","resolution":{"observed_at":"2026-08-12T05:01:31.644057Z","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":"2026-08-12T05:01:31.648331Z","title":"Pattern recognition and machine learning","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.648331Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:fbb8bec247cd9b6029b3c3bd529e637271a72e7a2f036eba039b074bff75ccd9","observation_id":"a5ae5c9d-4edf-4d57-b574-0ebda347b65b","resolution":{"observed_at":"2026-08-12T05:01:31.648331Z","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":"2026-08-12T05:01:31.652525Z","title":"An Introduction to Statis- tical Learning","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.652525Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:b816985e60001eee72fb1c5dff3c3204e5d7129674e83a47f1c0c714a5a97044","observation_id":"02ba10f0-c5af-4b35-9c55-88672648cd66","resolution":{"observed_at":"2026-08-12T05:01:31.652525Z","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":"2026-08-12T05:01:31.656804Z","title":"Understanding Machine Learning: From Theory to Algorithms","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.656804Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:6861837ddd51daa43c16334ab4b2f09a3abf1731e10f6c6f61f5c8346d8fd65d","observation_id":"53289a1b-4f54-482f-91b8-72a01546cad0","resolution":{"observed_at":"2026-08-12T05:01:31.656804Z","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":"2026-08-12T05:01:31.660983Z","title":"Scikit- learn: Machine learning in python","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.660983Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:a92006a89984258c08a9285e9609a73fa73e918a46555c376677ce2dd4971588","observation_id":"f2ebe673-8cd0-48a0-831a-c6325dc34126","resolution":{"observed_at":"2026-08-12T05:01:31.660983Z","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":"2026-08-12T05:01:31.665331Z","title":"James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, and Bin Yu","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.665331Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:8017de8b63b7a9dc407b632f8586caab87b74e65c2a17af6a6df4aa1360c6b98","observation_id":"577c824e-55f4-40cc-aba2-352ea7f2bd45","resolution":{"observed_at":"2026-08-12T05:01:31.665331Z","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":"2026-08-12T05:01:31.669579Z","title":"Artificial Intelligence: A Modern Approach","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.669579Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:e86eb82ef301cfb457d67863c304747ac227b0ef3c88d258ac2e642445a3831e","observation_id":"6c5dd67b-cb2c-4e28-b0f4-3cf4de79d3b1","resolution":{"observed_at":"2026-08-12T05:01:31.669579Z","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":"2026-08-12T05:01:31.673929Z","title":"McCormick, and David Madigan","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.673929Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:0845b61684808ded9a8fcebe6de8ea93a89778f2abefb1755e50a2da73430b8c","observation_id":"4d93093d-6235-49f9-af4d-1750e5b92620","resolution":{"observed_at":"2026-08-12T05:01:31.673929Z","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":"2026-08-12T05:01:31.678109Z","title":"Rule-based machine learning and knowledge extraction for model interpretability","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.678109Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:85de6540b3ee0132ba4c0dad56e1a05fa8ee7f22ac5330d65e9a86ed939742f7","observation_id":"a74a4994-c7b0-487c-866c-d270325f20fe","resolution":{"observed_at":"2026-08-12T05:01:31.678109Z","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":"2026-08-12T05:01:31.682403Z","title":"Rule-based systems","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.682403Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:b960538b7b7fa097019574d5a3e52e20f27c87617beb0a936dbdf3f3beadf08a","observation_id":"4d72ceed-edd8-4586-81b8-dddeb0b3bba7","resolution":{"observed_at":"2026-08-12T05:01:31.682403Z","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":"2026-08-12T05:01:31.686461Z","title":"Emerging trends and challenges in rule-based machine learning","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.686461Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:d8c8fb2040f7d87f968739b8054054b1c40254bcf86e2baf95adb55d140140f4","observation_id":"06e7c030-e0cb-43ba-9626-bd344aeed4fe","resolution":{"observed_at":"2026-08-12T05:01:31.686461Z","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":"2026-08-12T05:01:31.690988Z","title":"Biomedical Informatics: Computer Applications in Health Care and Biomedicine","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.690988Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:4e6cbda279d0213690ce8cfd582921c46215bb4ff09ba50140d035652e918fc8","observation_id":"7a1cde92-7bf8-4997-9f24-c6bbbf8620b3","resolution":{"observed_at":"2026-08-12T05:01:31.690988Z","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":"2026-08-12T05:01:31.695454Z","title":"Expert systems in medical applications: a review","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.695454Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:ff04ec5d54838c950abe6849c7fefa0ee306d95106689d524edefd1572769e86","observation_id":"645a3418-af3f-47ee-9708-2e3496df099c","resolution":{"observed_at":"2026-08-12T05:01:31.695454Z","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":"2026-08-12T05:01:31.699667Z","title":"Legal reasoning and legal argumentation.The Knowl- edge Engineering Review, 27(1):1–5, 2012","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.699667Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:a22d10f0e23e002de83b120f465f779c36eff926782a1b98753fced3f25fc598","observation_id":"b7930cff-3adc-4688-b4b8-5fba163dc0bb","resolution":{"observed_at":"2026-08-12T05:01:31.699667Z","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":"2026-08-12T05:01:31.703920Z","title":"The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.703920Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:8df25b6257756b3bd02f45e9a3303e47160d609d261796ea422f4ab76251957e","observation_id":"1202ea29-a658-467b-848b-45685fa4b045","resolution":{"observed_at":"2026-08-12T05:01:31.703920Z","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":"2026-08-12T05:01:31.708091Z","title":"Guidelines for a knowledge-based systems paper","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.708091Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:3e67c676d9688cb6786d4e9836fed70d734ed27eb62211a3a1ed0da5f1636227","observation_id":"a62edd5e-d580-4f0a-809b-7dfcf6c58011","resolution":{"observed_at":"2026-08-12T05:01:31.708091Z","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":"2026-08-12T05:01:31.712101Z","title":"Generalized Additive Models","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.712101Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:4f36ee05f60c97dc629b0d0fe29a696808c0a49f19710d9ffeac9bac79707334","observation_id":"3d680a59-2a4d-47b0-a828-c27377236bb0","resolution":{"observed_at":"2026-08-12T05:01:31.712101Z","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":"2026-08-12T05:01:31.716144Z","title":"Generalized Additive Models: An Introduction with R","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.716144Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:76fdfe4cd470bb2c9d56cc7143f6d1c411de06b96be45a2845389c88abe4c17f","observation_id":"4d122890-dde0-4387-a519-1f217ac7f0ad","resolution":{"observed_at":"2026-08-12T05:01:31.716144Z","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":"2026-08-12T05:01:31.720119Z","title":"Intelligible models for classification and re- gression","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.720119Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:5887c8d049329675499507f8003a8afc72a798dbf7f3df6c66082ff24b87fb24","observation_id":"4458b2da-8f87-4857-97ab-4d0d3f8efa0b","resolution":{"observed_at":"2026-08-12T05:01:31.720119Z","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":"2026-08-12T05:01:31.724474Z","title":"pygam: Generalized additive models in python","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.724474Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:5c7956dc3d041ab728a3a80a0b2b978a9e140f46f5985f7befb1e8ad33dec674","observation_id":"85f4ea4d-59ce-405a-8dcc-f410b871108c","resolution":{"observed_at":"2026-08-12T05:01:31.724474Z","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":"2026-08-12T05:01:31.728672Z","title":"Intel- ligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.728672Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:f6ec80839331858ce98f32ac58e3b3b21d649804c91c01e51ad4ca702a6df33e","observation_id":"ed50d77b-b833-470c-ab16-2717d2d6ab97","resolution":{"observed_at":"2026-08-12T05:01:31.728672Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1809.06512","last_updated":"2018-09-18T02:49:37Z","snapshot_observed_at":"2026-07-06T07:02:46.212753Z","submitted_at":"2018-09-18T02:49:37Z","title":"Applications of third order differential subordination and superordination involving generalized Struve function","version":1},"cited_work":{"arxiv_id":"1809.06512","doi":null,"metadata_source":"pith","pith_arxiv_id":"1809.06512","snapshot_observed_at":"2026-08-12T05:01:33.450702Z","title":"Applications of third order differential subordination and superordination involving generalized Struve function","venue":"math.CV","work_id":"f945db54-85d4-4f53-b806-599c4d9e1182","year":2018},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.732770Z"},"links":{"cited_paper":"/paper/1809.06512","citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:401c1d85c3575f676adb99a3d7c2ccf22321553dd1f388975a6b787c3ee9962a","observation_id":"5a72ac05-8760-4f54-9b98-6bbf9d972f88","resolution":{"observed_at":"2026-08-12T05:01:33.455569Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T05:01:31.737130Z","title":"Gam: The predictive modeling silver bullet","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.737130Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:c4a5813541e05c1ea0a01f4761e7f15095e832bf651d7a948db7ceecdfc8e38d","observation_id":"672e68f1-3df1-4bbf-a716-d6642ecb81bc","resolution":{"observed_at":"2026-08-12T05:01:31.737130Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1903.06066","last_updated":"2019-03-14T15:13:31Z","snapshot_observed_at":"2026-08-05T04:09:28.711832Z","submitted_at":"2019-03-14T15:13:31Z","title":"Strong and weak divergence of exponential and linear-implicit Euler approximations for stochastic partial differential equations with superlinearly growing nonlinearities","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.06066","snapshot_observed_at":"2026-08-12T05:01:31.741335Z","title":"Frequentist model averaging of generalized additive models","venue":null,"work_id":null,"year":1903},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.741335Z"},"links":{"cited_paper":"/paper/1903.06066","citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:859eae71fb1fe715b50e68722e63e9c00e439ab8b70914c9bac97810f1412bbf","observation_id":"3c883af5-f178-404c-b3cd-124fb11504a9","resolution":{"observed_at":"2026-08-12T05:01:31.741335Z","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":"2026-08-12T05:01:31.746148Z","title":"Machine learning: a probabilistic perspective","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.746148Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:8a32f3c4a5c51ecedd00d800c43c8b1972c20fbbf07bffa5ec3d6eaaa2ed9894","observation_id":"919ef878-1fb7-4a16-abaa-e9a8a5fd87b1","resolution":{"observed_at":"2026-08-12T05:01:31.746148Z","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":"2026-08-12T05:01:31.750414Z","title":"Bayesian data analysis","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.750414Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:ee17f31a6f9e2544a9ccd9d3bb3ad04ce368f1b986cc70c02f03458261aaa02a","observation_id":"29c4df3c-2b6a-4169-914f-c4583f1aefdc","resolution":{"observed_at":"2026-08-12T05:01:31.750414Z","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":"2026-08-12T05:01:31.754611Z","title":"Pattern Recognition and Machine Learning","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.754611Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:5663d0660b779c5bbe26db112343a7f5149189be27194a7b41d5939ea5d93c79","observation_id":"681753b6-e62b-4556-99cf-af0816e91d7b","resolution":{"observed_at":"2026-08-12T05:01:31.754611Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.10604","last_updated":"2017-11-28T23:05:15Z","snapshot_observed_at":"2026-07-06T06:11:42.746828Z","submitted_at":"2017-11-28T23:05:15Z","title":"TensorFlow Distributions","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.10604","snapshot_observed_at":"2026-08-12T05:01:31.759026Z","title":"Tensorflow distributions","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.759026Z"},"links":{"cited_paper":"/paper/1711.10604","citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:42c5cf11169943a1f09cc7745b32605588a85581e65e144de64c131f0f92f5a5","observation_id":"e9f4887c-0b23-4808-b375-021e01d4c3da","resolution":{"observed_at":"2026-08-12T05:01:31.759026Z","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":"2026-08-12T05:01:31.763550Z","title":"Handbook of Markov Chain Monte Carlo","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.763550Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:9548de78689afba1b1ca218310e48ba9283709cf760a9c8bb9ef68271d70a34a","observation_id":"7f017009-f504-4864-90a4-0c4eea0a2e64","resolution":{"observed_at":"2026-08-12T05:01:31.763550Z","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":"2026-08-12T05:01:31.767906Z","title":"Bayesian portfolio analysis","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.767906Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:9c10519fc9427ca6cda95650dc665e3a9c8a1babd9c8cb0f1dd8675edc1a8ce5","observation_id":"f486a306-a00f-4943-9148-b5723e856c9a","resolution":{"observed_at":"2026-08-12T05:01:31.767906Z","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":"2026-08-12T05:01:31.772188Z","title":"Online controlled experiments and a/b testing","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.772188Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:d55639ad8b904e3fe30b0362c332094b85848754826b63b74221cb3bace2a5a0","observation_id":"4d10e6be-687a-4f96-a007-ed1c0bbc4156","resolution":{"observed_at":"2026-08-12T05:01:31.772188Z","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":"2026-08-12T05:01:31.776299Z","title":"Variational inference: A review for statisti- cians","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.776299Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:bd23ce5f4d7365001347e4c82626f7c23763261e6c421a1a82db1b2611007956","observation_id":"167ffc21-7316-475e-8f50-b6d2f3e75cc5","resolution":{"observed_at":"2026-08-12T05:01:31.776299Z","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":"2026-08-12T05:01:31.780431Z","title":"Streaming variational bayes","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.780431Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:09134a9c972ec037799c493ad551e05cc96159f37043f42669f0cdb82069f95a","observation_id":"6ae499ba-7156-49f6-89e9-deb9d695dfd7","resolution":{"observed_at":"2026-08-12T05:01:31.780431Z","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":"2026-08-12T05:01:31.784483Z","title":"Representation learning: A review and new perspectives","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.784483Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:d747a87bca37162db564289f69a35bfad1db1c4e0f50836d597dae8915587bfa","observation_id":"2fb1ba8c-d1f5-463b-b95f-a2152f22c88a","resolution":{"observed_at":"2026-08-12T05:01:31.784483Z","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":"2026-08-12T05:01:31.788558Z","title":"Understanding neu- ral networks through deep visualization","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.788558Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:d07fbb1830bbae31fd71f5625c513451748836be8c17d859aaf89edc32d76f48","observation_id":"7ad49e39-a830-4cc3-972b-3b357bd2e96c","resolution":{"observed_at":"2026-08-12T05:01:31.788558Z","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":"2026-08-12T05:01:31.793649Z","title":"Very deep convolutional networks for large-scale image recognition","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.793649Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:e734fdcc09daf4311c498f983a535391786a36b55d6fae8ac70fae8b065282aa","observation_id":"71844d70-2b78-4b65-b64b-90cea558c60a","resolution":{"observed_at":"2026-08-12T05:01:31.793649Z","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":"2026-08-12T05:01:31.797904Z","title":"Deep inside convolutional networks: Visualising image classification models and saliency maps","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.797904Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:b285b59cd77bf9defa1f3db305e458bfc9ae73575eeadac3f6cce8a5f68f5421","observation_id":"bdb73837-a6a1-4060-990c-9a7021e42e8f","resolution":{"observed_at":"2026-08-12T05:01:31.797904Z","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":"2026-08-12T05:01:31.802219Z","title":"Axiomatic attribution for deep networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.802219Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:8760ff0973d570de50700fdaffc1c48784caa99e511c0ada8abf9d4367d8656f","observation_id":"ee4bfffa-1289-4ccb-935c-0a9701a6b9aa","resolution":{"observed_at":"2026-08-12T05:01:31.802219Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1506.00019","last_updated":"2015-10-17T05:06:11Z","snapshot_observed_at":"2026-07-06T04:19:22.444137Z","submitted_at":"2015-05-29T20:16:51Z","title":"A Critical Review of Recurrent Neural Networks for Sequence Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1506.00019","snapshot_observed_at":"2026-08-12T05:01:31.806758Z","title":"Critical review of recurrent neural net- works for sequence learning","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.806758Z"},"links":{"cited_paper":"/paper/1506.00019","citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:62cd335e3acc83c75972673de2e30d0557a32be94060163ac942cc9f7ce2d080","observation_id":"f5e90e96-6f1b-4f8d-b9b8-40303d603270","resolution":{"observed_at":"2026-08-12T05:01:31.806758Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1506.02078","last_updated":"2015-11-17T02:42:24Z","snapshot_observed_at":"2026-08-04T02:12:07.668406Z","submitted_at":"2015-06-05T22:33:04Z","title":"Visualizing and Understanding Recurrent Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1506.02078","snapshot_observed_at":"2026-08-12T05:01:31.811343Z","title":"Visualizing and understanding recurrent net- works","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.811343Z"},"links":{"cited_paper":"/paper/1506.02078","citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:7bd5a0db2fe3ed0ff7a9ee4aad3f522dbd022f58fce7bd69d0c73d4f5d444bc2","observation_id":"ead0d47f-b369-455a-b2f7-c3983799f965","resolution":{"observed_at":"2026-08-12T05:01:31.811343Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-07-30T09:12:38.100527Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-12T05:01:31.815989Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.815989Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:535969e760ca216eec2ad355ac2e3295140b979e9a69c7a0b62a591fdc1ed435","observation_id":"077f6673-e512-4a0f-8a2a-8d56ef806ccf","resolution":{"observed_at":"2026-08-12T05:01:31.815989Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.10683","last_updated":"2023-09-19T15:14:48Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2019-10-23T17:37:36Z","title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.10683","snapshot_observed_at":"2026-08-12T05:01:31.820557Z","title":"Exploring the limits of transfer learning with a unified text-to-text transformer","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.820557Z"},"links":{"cited_paper":"/paper/1910.10683","citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:b07b7626f3d8ad42b2ef7c67b9299d32f2bb07ee3205ed5752ed6d47685615b0","observation_id":"7fa0ab5a-82c0-4119-8f0f-8563de27b4dc","resolution":{"observed_at":"2026-08-12T05:01:31.820557Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1906.05714","last_updated":"2019-06-12T15:45:26Z","snapshot_observed_at":"2026-08-13T12:18:40.834133Z","submitted_at":"2019-06-12T15:45:26Z","title":"A Multiscale Visualization of Attention in the Transformer Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.05714","snapshot_observed_at":"2026-08-12T05:01:31.825058Z","title":"A multiscale visualization of attention in the transformer model","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.825058Z"},"links":{"cited_paper":"/paper/1906.05714","citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:f9b732c82ecda49f4c4c0085c506a14c7d39f9ecabe1495b34f61368eb50b0fc","observation_id":"e9d7876b-7d0e-4d5f-8ee1-a9f1be3fa89a","resolution":{"observed_at":"2026-08-12T05:01:31.825058Z","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":"2026-08-12T05:01:31.830219Z","title":"Language models are few-shot learners","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.830219Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:83f321aea45629312d01c46b91b1722e7af9e077fe9bfe3df2a339f34203b89f","observation_id":"88b8db3a-5585-4521-aea7-85036e72fc00","resolution":{"observed_at":"2026-08-12T05:01:31.830219Z","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":"2026-08-12T05:01:31.834627Z","title":"Large Lan- guage Models and Cognitive Science: A Comprehensive Review of Similarities, Differences, and Challenges","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.834627Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:c8c9d5aa518b81caab6452bc557e311a023f7201e10f8eb4dce045d87120ac05","observation_id":"2bd6014c-b69f-4bf8-a53c-7b89a133916e","resolution":{"observed_at":"2026-08-12T05:01:31.834627Z","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":"2026-08-12T05:01:31.838966Z","title":"Design of intelligent customer service system based on deep learning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.838966Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:733656d56ef80c8af9f6e33c3f113ecc0cadc28fc64fd9a912a7649222fbd977","observation_id":"5f2483d0-8493-46f4-bd5c-4f091a5fbf7c","resolution":{"observed_at":"2026-08-12T05:01:31.838966Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1909.05858","last_updated":"2019-09-20T20:08:56Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2019-09-11T17:57:18Z","title":"CTRL: A Conditional Transformer Language Model for Controllable Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1909.05858","snapshot_observed_at":"2026-08-12T05:01:31.843129Z","title":"Ctrl: A conditional transformer language model for controllable generation","venue":null,"work_id":null,"year":1909},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.843129Z"},"links":{"cited_paper":"/paper/1909.05858","citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:5b64cdc87d5b1eef715bff458dfe516c845117e96078acf0db1b62cd32c21981","observation_id":"cf5b9608-cefd-41d6-a858-2c076fc58ca9","resolution":{"observed_at":"2026-08-12T05:01:31.843129Z","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":"2026-08-12T05:01:31.847565Z","title":"Github copilot: Y our ai pair programmer","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.847565Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:0625ed0c73e91ce274c59442c87a02b601098f2c76f1dceef9c5184d9bb7dfd9","observation_id":"b567346c-4053-47d2-93ac-090a8537f70f","resolution":{"observed_at":"2026-08-12T05:01:31.847565Z","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":"2026-08-12T05:01:31.851884Z","title":"Biobert: a pre-trained biomedical language representation model for biomedical text mining","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.851884Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:c02781c1a5228afc46a9c6c87bc52c3b85786f7ef2a25906332ebabddc520a59","observation_id":"32208586-f769-499f-be7a-5835849bab09","resolution":{"observed_at":"2026-08-12T05:01:31.851884Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1606.05250","last_updated":"2016-10-11T02:42:36Z","snapshot_observed_at":"2026-08-12T23:01:26.406202Z","submitted_at":"2016-06-16T16:36:00Z","title":"SQuAD: 100,000+ Questions for Machine Comprehension of Text","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1606.05250","snapshot_observed_at":"2026-08-12T05:01:31.856139Z","title":"Squad: 100,000+ ques- tions for machine comprehension of text","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":101,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.856139Z"},"links":{"cited_paper":"/paper/1606.05250","citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:3d7fe44a74d68d9c00655ee864502d8767d84017db94eacaa237fd25f5e92c14","observation_id":"ad459a42-6e80-4203-b558-3d74ac493065","resolution":{"observed_at":"2026-08-12T05:01:31.856139Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","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":"2026-08-12T05:01:31.860558Z","title":"Gpt-4 technical report","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":102,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.860558Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:3cc7296b09897867719e34909023e43c6eeb2c08e717a50c8c073462d9c314b2","observation_id":"3fe92703-0e56-4f64-b32c-8b210621e8f0","resolution":{"observed_at":"2026-08-12T05:01:31.860558Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13971","last_updated":"2023-02-27T17:11:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-27T17:11:15Z","title":"LLaMA: Open and Efficient Foundation Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13971","snapshot_observed_at":"2026-08-12T05:01:31.865661Z","title":"Llama: Open and efficient foundation language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":103,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.865661Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:400fe067c2bff3f75186606fcca8a1115adfd3ad01150f72c17ce849bd784a1d","observation_id":"959347ec-d355-4298-bdf8-43f9be70f0f5","resolution":{"observed_at":"2026-08-12T05:01:31.865661Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.09288","last_updated":"2023-07-19T17:08:59Z","snapshot_observed_at":"2026-08-07T12:56:43.323460Z","submitted_at":"2023-07-18T14:31:57Z","title":"Llama 2: Open Foundation and Fine-Tuned Chat Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.09288","snapshot_observed_at":"2026-08-12T05:01:31.870230Z","title":"Llama 2: Open foundation and fine-tuned chat models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":104,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.870230Z"},"links":{"cited_paper":"/paper/2307.09288","citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:e23a1e21d226650ae4265d9cc15bb2a4e9bf05712b554e7b236b10837d6c10cf","observation_id":"adb05308-342e-47ba-a3ed-a5c4e6ee6fbb","resolution":{"observed_at":"2026-08-12T05:01:31.870230Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.08361","last_updated":"2020-01-23T03:59:20Z","snapshot_observed_at":"2026-07-06T08:52:12.656082Z","submitted_at":"2020-01-23T03:59:20Z","title":"Scaling Laws for Neural Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.08361","snapshot_observed_at":"2026-08-12T05:01:31.874992Z","title":"Scaling laws for neural language models","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":105,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.874992Z"},"links":{"cited_paper":"/paper/2001.08361","citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:3622989ee78075cd165581ab5e4a49fc65467397b66912c824f54607baee57ca","observation_id":"bd088d7f-a695-4882-844c-37401f2fc0cc","resolution":{"observed_at":"2026-08-12T05:01:31.874992Z","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":"2026-08-12T05:01:31.879510Z","title":"Energy and policy considerations for deep learning in nlp","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs","version":2},"reference_index":106,"source":"pdf_text","source_observed_at":"2026-08-12T05:01:31.879510Z"},"links":{"citing_paper":"/paper/2412.00800"},"observation_digest":"sha256:e8c1974d97e19d8fa5bcc712193469e29867eb89633ea52bfd9e9bd6c3039d66","observation_id":"0e81de2c-7d20-4fc1-9f36-5d9c7645a3b7","resolution":{"observed_at":"2026-08-12T05:01:31.879510Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.00800","last_updated":"2024-12-08T06:24:32Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T13:26:22.597203Z","submitted_at":"2024-12-01T13:01:01Z","title":"A Comprehensive Guide to Explainable AI: From Classical Models to LLMs"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":99,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":291},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 100 of 291 outbound references and 2 inbound Pith citation observations for arXiv:2412.00800."}