{"as_of":"2026-08-08T03:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f3e80dc59f3439bf87e2f6ba5d6fd86a58d7c14f071edeacc4d732d48d16ea24","coverage":[{"denominator":67,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":67,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T14:42:43.581679Z","state":"measured"},{"denominator":68,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":68,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-09T17:12:53.262559Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-09T17:16:23.121168Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"cited_work":{"arxiv_id":"2509.23926","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2509.23926","snapshot_observed_at":"2026-07-22T01:22:14.093143Z","title":"Learning encoding-decoding direction pairs to un- veil concepts of influence in deep vision networks.arXiv preprint arXiv:2509.23926, 2025","venue":null,"work_id":"212b5b4b-41ff-48a1-92a1-584c37bcda5d","year":2025},"citing_paper":{"arxiv_id":"2607.07216","last_updated":"2026-07-08T09:56:06Z","snapshot_observed_at":"2026-08-07T07:45:14.552019Z","submitted_at":"2026-07-08T09:56:06Z","title":"Why Fake ? Unveiling the Semantic Vocabulary of Deepfake Detectors","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-09T17:12:53.262559Z"},"links":{"cited_paper":"/paper/2509.23926","citing_paper":"/paper/2607.07216"},"observation_digest":"sha256:52acf768c635e9adbfc5b34f35792ad5e512a1b9f67c5f36f2901525c83fed59","observation_id":"726c8d11-9155-4b7a-a20c-e3162b0ce112","resolution":{"observed_at":"2026-07-22T01:22:14.093143Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2509.23926/citation-record","integrity":"/paper/2509.23926/integrity","json":"/paper/2509.23926/citation-record.json","paper":"/paper/2509.23926"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1610.01644","last_updated":"2018-11-22T23:40:00Z","snapshot_observed_at":"2026-07-06T05:13:30.860932Z","submitted_at":"2016-10-05T20:59:01Z","title":"Understanding intermediate layers using linear classifier probes","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1610.01644","snapshot_observed_at":"2026-08-04T14:42:35.513314Z","title":"Understanding intermediate layers using linear classifier probes","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:35.513314Z"},"links":{"cited_paper":"/paper/1610.01644","citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:12c2f8704e34074c8b910800baafc255b75e0501527cca4273932516ca9e9a58","observation_id":"b44232bd-70ed-46be-bfe2-ada2297930d8","resolution":{"observed_at":"2026-08-04T14:42:35.513314Z","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":"2026-08-04T14:42:35.659158Z","title":"Anders, Leander Weber, David Neumann, Wojciech Samek, Klaus-Robert Müller, and Sebastian Lapuschkin","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:35.659158Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:3170137db15e6caabcce6cf38238241c877ac44e3fdaf38ab1dc74fc53df07f9","observation_id":"e53a2718-d9fb-4ac2-b05c-7ef357abf384","resolution":{"observed_at":"2026-08-04T14:42:35.659158Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1704.05796","last_updated":"2017-04-19T16:10:38Z","snapshot_observed_at":"2026-08-07T22:38:27.882276Z","submitted_at":"2017-04-19T16:10:38Z","title":"Network Dissection: Quantifying Interpretability of Deep Visual Representations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1704.05796","snapshot_observed_at":"2026-08-04T14:42:35.755807Z","title":"Network dissection: Quantifying interpretability of deep visual representations","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:35.755807Z"},"links":{"cited_paper":"/paper/1704.05796","citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:8e9e841fbd3b9b34f7eee04051c4104a355135d5b8562cfd7feb851ec81c361e","observation_id":"097d8091-1b58-4cc1-9cde-09f6990a4688","resolution":{"observed_at":"2026-08-04T14:42:35.755807Z","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":"2026-08-04T14:42:35.848895Z","title":"Show and tell: Visually explainable deep neural nets via spatially-aware concept bottleneck models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:35.848895Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:70248d031f2444dd012b856d386d70a8dfd3a1d5b92a0f673e62a70cb99f7912","observation_id":"69142f1e-9a2d-4003-83ab-a70b20d439b6","resolution":{"observed_at":"2026-08-04T14:42:35.848895Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.14082","last_updated":"2024-08-23T23:02:28Z","snapshot_observed_at":"2026-07-06T18:03:38.397804Z","submitted_at":"2024-04-22T11:01:51Z","title":"Mechanistic Interpretability for AI Safety -- A Review","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.14082","snapshot_observed_at":"2026-08-04T14:42:35.998740Z","title":"Mechanistic interpretability for ai safety--a review","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:35.998740Z"},"links":{"cited_paper":"/paper/2404.14082","citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:8457e08396d6970c8d15ec24109bad0ccdef7a7345997026c35913230b3bc97c","observation_id":"a2e5617a-867e-4d5a-9b6c-a07273185af2","resolution":{"observed_at":"2026-08-04T14:42:35.998740Z","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":"2026-08-04T14:42:36.138961Z","title":"Constrained optimization and Lagrange multiplier methods","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:36.138961Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:21907402e601a3b226927c359a9dcb667d30401bb2b48f458ea0208e49c81ace","observation_id":"374da523-82c0-4c55-83ee-aa1585ecde43","resolution":{"observed_at":"2026-08-04T14:42:36.138961Z","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":"2026-08-04T14:42:36.230228Z","title":"Towards monosemanticity: Decomposing language models with dictionary learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:36.230228Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:4a0f9b674dcf3d5e282732d8f30c6c392ea0897f509e46edee66afcc2a59ea5e","observation_id":"ac41f588-2e97-4ca5-bfa7-83e79dca799e","resolution":{"observed_at":"2026-08-04T14:42:36.230228Z","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":"2026-08-04T14:42:36.322662Z","title":"Learning multi-level features with matryoshka sparse autoencoders","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:36.322662Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:b5db716aeda022347116d1bcddb0449ceade2d486d745d29418614a90f4e23cf","observation_id":"de4db287-34c9-4702-a121-5de1026cfa42","resolution":{"observed_at":"2026-08-04T14:42:36.322662Z","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":"2026-08-04T14:42:36.427127Z","title":"Emerging properties in self-supervised vision transformers","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:36.427127Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:ceaa8204b1c1057da5c1e2feefc35b727698ec20132ead4394bb8fa07d2983fc","observation_id":"da6632cf-222a-48f5-a866-f98433f53912","resolution":{"observed_at":"2026-08-04T14:42:36.427127Z","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":"2026-08-04T14:42:36.571849Z","title":"Disentangled explanations of neural network predictions by finding relevant subspaces","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:36.571849Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:346c5a3bee56ca7ef077520072de50d521697c657b954b5b225bf0b1b8288bd7","observation_id":"03267961-230a-4070-a9bc-113e7e8b0e4e","resolution":{"observed_at":"2026-08-04T14:42:36.571849Z","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":"2026-08-04T14:42:36.716270Z","title":"Sparse autoencoders find highly interpretable features in language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:36.716270Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:4ce5211a1fec7252b24477982bc4ce0dee302e52aa06dd854f0cd5bf19635ee5","observation_id":"68a29019-3e81-46a5-9344-9325d5277d0d","resolution":{"observed_at":"2026-08-04T14:42:36.716270Z","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":"2026-08-04T14:42:36.815049Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:36.815049Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:57449689ee8e8e65a6eae64c1097f71b47f06ce72be12566d31921a8d15e1591","observation_id":"bb24afce-cf0a-4cc2-a2e8-e981c2efbef6","resolution":{"observed_at":"2026-08-04T14:42:36.815049Z","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":"2026-08-04T14:42:36.924187Z","title":"Unsupervised interpretable basis extraction for concept--based visual explanations","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:36.924187Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:aa5451456af5060fb5f4a9266fd4c153bf7ad6d8da6961dfb1beda8a14fd2c81","observation_id":"997928da-4239-4129-b98b-6878a9944581","resolution":{"observed_at":"2026-08-04T14:42:36.924187Z","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":"2026-08-04T14:42:37.045714Z","title":"Concept basis extraction for latent space interpretation of image classifiers","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:37.045714Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:c532f99ea39fae862bb34e3b995e179c16aa7acbf7ef77767138dde748aca0b1","observation_id":"e4f2e8b9-c401-49b5-b4d2-e7ddf1696925","resolution":{"observed_at":"2026-08-04T14:42:37.045714Z","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":"2026-08-04T14:42:37.174739Z","title":"From hope to safety: Unlearning biases of deep models via gradient penalization in latent space","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:37.174739Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:21d952f9f6b2cbe51f3a24b812e2a93b7841f14327264e98a9e2e18f115621f9","observation_id":"781d8e51-3849-4cfb-8075-d31da223cf9a","resolution":{"observed_at":"2026-08-04T14:42:37.174739Z","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":"2026-08-04T14:42:37.224459Z","title":"Pure: Turning polysemantic neurons into pure features by identifying relevant circuits","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:37.224459Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:3bb6d60d9098fa349b53936bdb9e1183b56fd80342bab31c7dd17ca77db13883","observation_id":"7add5e1d-32ba-4a95-8d59-8b60941a7408","resolution":{"observed_at":"2026-08-04T14:42:37.224459Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2209.10652","last_updated":"2022-09-21T20:49:26Z","snapshot_observed_at":"2026-07-06T13:54:56.779166Z","submitted_at":"2022-09-21T20:49:26Z","title":"Toy Models of Superposition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2209.10652","snapshot_observed_at":"2026-08-04T14:42:37.303112Z","title":"Toy models of superposition","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:37.303112Z"},"links":{"cited_paper":"/paper/2209.10652","citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:c3eec69448adda4b4a8fd3e22f5252065e3f09ddf3c08790724e7cd7d309eb9d","observation_id":"82294171-0542-4c2f-a356-90aeeb056d5e","resolution":{"observed_at":"2026-08-04T14:42:37.303112Z","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":"2026-08-04T14:42:37.413812Z","title":"Decomposing the dark matter of sparse autoencoders","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:37.413812Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:28dda1f4eec3247b0c1e2662df43a1cae4440f64754acd361ef0a7f367376a09","observation_id":"4a9e473c-adfb-420b-8aa8-258e43f62722","resolution":{"observed_at":"2026-08-04T14:42:37.413812Z","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":"2026-08-04T14:42:37.580834Z","title":"Unlocking feature visualization for deep network with magnitude constrained optimization","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:37.580834Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:ab89cbe4c7cbeb347ab4f9cb35aef11ac150ce9d1b9ed53c61d4928e7e666c79","observation_id":"b19d48b8-fd3e-447d-b541-69b5abe0a530","resolution":{"observed_at":"2026-08-04T14:42:37.580834Z","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":"2026-08-04T14:42:37.629500Z","title":"A holistic approach to unifying automatic concept extraction and concept importance estimation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:37.629500Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:c1c105dc588f4c7957e8ef65c285a9a158e8f1bcffc3dac582cba66b326fd090","observation_id":"7b0c5a3f-9faa-4f11-8da7-619a3deae3e3","resolution":{"observed_at":"2026-08-04T14:42:37.629500Z","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":"2026-08-04T14:42:37.894779Z","title":"Craft: Concept recursive activation factorization for explainability","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:37.894779Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:746bb0b23a6fa4c14be66291a1ccdafb7dcd03214f274438171cc57b1a5654c5","observation_id":"f55c73c0-8284-4387-9704-565208572254","resolution":{"observed_at":"2026-08-04T14:42:37.894779Z","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":"2026-08-04T14:42:38.079867Z","title":"Large-scale unsupervised semantic segmentation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:38.079867Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:58e3bec4f235942f599744f2d72134fbfe35b2595872227db2e4ec51014cff8f","observation_id":"5c6eed23-5563-421e-80b9-61498b95e20a","resolution":{"observed_at":"2026-08-04T14:42:38.079867Z","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":"2026-08-04T14:42:38.254329Z","title":"Concept discovery and dataset exploration with singular value decomposition","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:38.254329Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:f682840a0ef0e43489342dc5095cf74261e5503780498f608f856fcd7e27d08d","observation_id":"f8d66e09-a964-4530-b92a-78512857ce53","resolution":{"observed_at":"2026-08-04T14:42:38.254329Z","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":"2026-08-04T14:42:38.345525Z","title":"Uncovering unique concept vectors through latent space decomposition","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:38.345525Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:f00b7222bbf12cec7aae4490554c8fee1469fe155d72d851071cef114e7c3e7c","observation_id":"adfecd7e-ae91-46f9-a4f2-59bbf6a3f21b","resolution":{"observed_at":"2026-08-04T14:42:38.345525Z","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":"2026-08-04T14:42:38.511407Z","title":"On the interpretation of weight vectors of linear models in multivariate neuroimaging","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:38.511407Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:66b0fc803be0f09af95ab0f613e8f187b4ee5e0bed5d1bca426664292f725d7c","observation_id":"635a1ac6-dbc5-4e7b-b873-c7b0632eb095","resolution":{"observed_at":"2026-08-04T14:42:38.511407Z","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":"2026-08-04T14:42:38.599966Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:38.599966Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:a39a496d5b16cad59d745ff7b5e8d59748525fe6b6de190ab3a23254b91ecfa4","observation_id":"b9a76561-e03f-41ff-914e-ddf4beff75dc","resolution":{"observed_at":"2026-08-04T14:42:38.599966Z","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":"2026-08-04T14:42:38.787504Z","title":"Masked autoencoders are scalable vision learners","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:38.787504Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:49eb4246b30a21b732a2d06cad4b544bbc4e24a17ebe67ed6eadc226e4ed18b9","observation_id":"56c6c021-122e-41c9-a3ab-d9d0ffdb4295","resolution":{"observed_at":"2026-08-04T14:42:38.787504Z","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":"2026-08-04T14:42:38.864049Z","title":"Multiplier and gradient methods","venue":null,"work_id":null,"year":1969},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:38.864049Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:d8674c3243911ced8a53d366020b8829a1a543f88a12553f39d6b294fe121a63","observation_id":"509b6859-70c7-4685-82c6-8781614b3189","resolution":{"observed_at":"2026-08-04T14:42:38.864049Z","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":"2026-08-04T14:42:38.927726Z","title":"Which direction to choose? an analysis on the representation power of self-supervised vits in downstream tasks","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:38.927726Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:d95d84539ad42b3235e84cde736985a2b5f2401cb6c5801c3a8dfd5f9ecd7cb9","observation_id":"d9b8f98b-15b6-4ebe-9f42-21f80028f15f","resolution":{"observed_at":"2026-08-04T14:42:38.927726Z","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":"2026-08-04T14:42:39.023509Z","title":"Explaining ai through mechanistic interpretability","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:39.023509Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:88a7a01de3db51719ec4bd8d65e67e22b02f42110d566d57e4c05a179755e205","observation_id":"0fbdc958-33a7-4613-9bd6-7261efc50837","resolution":{"observed_at":"2026-08-04T14:42:39.023509Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.11279","last_updated":"2018-06-07T04:33:27Z","snapshot_observed_at":"2026-08-04T20:10:18.590341Z","submitted_at":"2017-11-30T09:26:12Z","title":"Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.11279","snapshot_observed_at":"2026-08-04T14:42:39.077122Z","title":"Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav), June 2018","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:39.077122Z"},"links":{"cited_paper":"/paper/1711.11279","citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:383ce2b59e30ffc98f477b7fbc60eace89bc73c7866bbc898994a0c30a123d4d","observation_id":"bc246801-d312-4d20-befa-b2dc063fd83c","resolution":{"observed_at":"2026-08-04T14:42:39.077122Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1705.05598","last_updated":"2017-10-24T23:10:33Z","snapshot_observed_at":"2026-07-06T05:42:48.879600Z","submitted_at":"2017-05-16T08:58:25Z","title":"Learning how to explain neural networks: PatternNet and PatternAttribution","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1705.05598","snapshot_observed_at":"2026-08-04T14:42:39.151997Z","title":"Schütt, Maximilian Alber, Klaus-Robert Müller, Dumitru Erhan, Been Kim, and Sven Dähne","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:39.151997Z"},"links":{"cited_paper":"/paper/1705.05598","citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:81d07e892c8e23bf467be79e8abbf9af7f5d8dae264fed77e54507ad4ff6863c","observation_id":"e47427af-1005-45b4-adf9-8ddf74c680a5","resolution":{"observed_at":"2026-08-04T14:42:39.151997Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-04T14:42:39.223864Z","title":"Adam: A method for stochastic optimization","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:39.223864Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:213d86f7c02c0bad2dbf8144869e587ea788e8afd4cee1bc7f663298277e6116","observation_id":"29e63c7a-8645-4b99-bcc9-aa9747a26db0","resolution":{"observed_at":"2026-08-04T14:42:39.223864Z","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":"2026-08-04T14:42:39.317918Z","title":"Concept bottleneck models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:39.317918Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:68ae181e9003557c681fa49e55b48704f52bda785f42d25f10d482eefcc9cb91","observation_id":"d0e578dd-1698-4a09-abe7-71698c86d670","resolution":{"observed_at":"2026-08-04T14:42:39.317918Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.05276","last_updated":"2025-03-21T13:02:14Z","snapshot_observed_at":"2026-07-06T20:02:57.733746Z","submitted_at":"2024-12-06T18:59:51Z","title":"Sparse autoencoders reveal selective remapping of visual concepts during adaptation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.05276","snapshot_observed_at":"2026-08-04T14:42:39.373139Z","title":"Sparse autoencoders reveal selective remapping of visual concepts during adaptation, December 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:39.373139Z"},"links":{"cited_paper":"/paper/2412.05276","citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:f101613e34f8a1e76a6ccf696bbb01f4b496ac15e50b211fee4e31b6c14142d9","observation_id":"c606dd38-4e19-407a-8b57-84218ccdd66a","resolution":{"observed_at":"2026-08-04T14:42:39.373139Z","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":"2026-08-04T14:42:39.431622Z","title":"Focal loss for dense object detection","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:39.431622Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:3304330f09dc9a3553c94d215090dab5bf549f255d3d3abb5505283cfcce79e7","observation_id":"85dc3d6e-713f-4356-bbef-ac2f8c7cade3","resolution":{"observed_at":"2026-08-04T14:42:39.431622Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1608.03983","last_updated":"2017-05-03T16:28:09Z","snapshot_observed_at":"2026-07-06T05:06:55.589962Z","submitted_at":"2016-08-13T13:46:05Z","title":"SGDR: Stochastic Gradient Descent with Warm Restarts","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1608.03983","snapshot_observed_at":"2026-08-04T14:42:39.646892Z","title":"SGDR : Stochastic gradient descent with warm restarts","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:39.646892Z"},"links":{"cited_paper":"/paper/1608.03983","citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:eb6eb43cc77993763b2239e7a3e266aaf2766b2596335a8987adeaa5f285aeb8","observation_id":"c84d8a31-76b9-426a-8b43-3ce59d37dc3a","resolution":{"observed_at":"2026-08-04T14:42:39.646892Z","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":"2026-08-04T14:42:39.883195Z","title":"Understanding deep image representations by inverting them","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:39.883195Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:432dc19b6ae44b29bdc2cf66c02f8372b6818d1a816d53e3b09b49222146203d","observation_id":"1f7acac8-20af-4220-bd6b-b3aea6b263ee","resolution":{"observed_at":"2026-08-04T14:42:39.883195Z","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":"2026-08-04T14:42:40.074421Z","title":"Visualizing deep convolutional neural networks using natural pre-images","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:40.074421Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:5f416b9697da03118d400914aada24fc310019445d39e9b8193e4c6d59fc8760","observation_id":"ae1cf4c8-2445-4c37-a4be-20e914def770","resolution":{"observed_at":"2026-08-04T14:42:40.074421Z","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":"2026-08-04T14:42:40.230102Z","title":"Moments in time dataset: one million videos for event understanding","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:40.230102Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:cee2fd9d1312e2d063822f245555eb8b990bde53c88fffa29518c7ea28060e54","observation_id":"d30c4765-34b5-4ba0-877c-ae97a9530ecb","resolution":{"observed_at":"2026-08-04T14:42:40.230102Z","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":"2026-08-04T14:42:40.423865Z","title":"Emergent linear representations in world models of self-supervised sequence models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:40.423865Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:c5e4812fb8a10c8875e926437659f89d686e15251586dae95e7711fb8e20b247","observation_id":"5b7cfd57-d541-4284-a314-a107ef5fbfd4","resolution":{"observed_at":"2026-08-04T14:42:40.423865Z","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":"2026-08-04T14:42:40.508034Z","title":"Sparse autoencoder","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:40.508034Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:448ce6ff3d5b2d23dd681af7273e55d097f5cf63c29af7f2f21d88db69b93c19","observation_id":"fff7fe56-b15a-48ce-bdfc-28fcd83bf365","resolution":{"observed_at":"2026-08-04T14:42:40.508034Z","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":"2026-08-04T14:42:40.550211Z","title":"Understanding neural networks via feature visualization: A survey","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:40.550211Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:06e98c08f7c26f952b77d57a84b3239bcb0fe478d1df9bbc71175c931562caad","observation_id":"2c306bfd-8ca6-47e4-8c4f-3bf7defbbf0a","resolution":{"observed_at":"2026-08-04T14:42:40.550211Z","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":"2026-08-04T14:42:40.626808Z","title":"Label-free concept bottleneck models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:40.626808Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:5a0c5dfadeb8905541cf126b2135ecc7354d19d9fd5ef93f8ac37b5318ec409d","observation_id":"d20bee1c-9253-499e-916f-d26913ef6d01","resolution":{"observed_at":"2026-08-04T14:42:40.626808Z","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":"2026-08-04T14:42:40.694817Z","title":"Feature visualization","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:40.694817Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:f13c53e76ca335dbc13e1f178131d4cc45f3792054b5aca6a32b3a680211ef84","observation_id":"d7dc9370-93fc-4959-b4c3-ccbb3652c386","resolution":{"observed_at":"2026-08-04T14:42:40.694817Z","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":"2026-08-04T14:42:40.801784Z","title":"Reveal to revise: An explainable ai life cycle for iterative bias correction of deep models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:40.801784Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:542509e6140ffc2fe4b326afcca8cc9f987679e26dfb9284616ef927dc5d7a7a","observation_id":"f4f01b57-31e1-4481-b9f1-a66f47e0b7fa","resolution":{"observed_at":"2026-08-04T14:42:40.801784Z","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":"2026-08-04T14:42:40.941385Z","title":"Anders, Thomas Wiegand, Wojciech Samek, and Sebastian Lapuschkin","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:40.941385Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:10f1a77e0cdde0fd39378dfd780ba9dbcc2bfdce5bcc2fc00c87e8cce4d6e557","observation_id":"cbc54b1b-bb2c-499e-a83e-ed2e59b7895e","resolution":{"observed_at":"2026-08-04T14:42:40.941385Z","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":"2026-08-04T14:42:41.108259Z","title":"Robust semantic interpretability: Revisiting concept activation vectors","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:41.108259Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:d13b076fb13e4297b3deec58d2be1de3accbebabcfd73ec3faeec38aa3f18dae","observation_id":"8c0cf04a-1710-499c-bd8f-e9bd1ae69ebe","resolution":{"observed_at":"2026-08-04T14:42:41.108259Z","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":"2026-08-04T14:42:41.259265Z","title":"Learning transferable visual models from natural language supervision","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:41.259265Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:1df816b2d92ffd27122f4a9941b64515f632bb36fff5db16530d509fcc891502","observation_id":"14e447ab-2e54-4b0c-8cb4-8d6de8e00c6e","resolution":{"observed_at":"2026-08-04T14:42:41.259265Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.05941","last_updated":"2017-10-27T17:45:21Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-10-16T18:05:45Z","title":"Searching for Activation Functions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.05941","snapshot_observed_at":"2026-08-04T14:42:41.365575Z","title":"Searching for activation functions","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:41.365575Z"},"links":{"cited_paper":"/paper/1710.05941","citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:46713eae6ae3d80576671a10d1e524f3466c9b3cf2e27e174ada5fa5adc0d0ef","observation_id":"bcd3f8d0-f2a4-4820-a044-1c81f5b537b8","resolution":{"observed_at":"2026-08-04T14:42:41.365575Z","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":"2026-08-04T14:42:41.441095Z","title":"Identifying interpretable action concepts in deep networks","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:41.441095Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:8a34d59fa381f7037220c53fc371718758a3632371b4203e467b5aab050b9195","observation_id":"64f2f663-f425-42dc-9996-995368e5cf32","resolution":{"observed_at":"2026-08-04T14:42:41.441095Z","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":"2026-08-04T14:42:41.606782Z","title":"Discover-then-name: Task-agnostic concept bottlenecks via automated concept discovery","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:41.606782Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:708ada3ce6f5d7503977095cc0de27da96099c170b66068443ea0d2b2032d719","observation_id":"6de69bff-0c47-4278-a7bf-1f9d7b6b6d61","resolution":{"observed_at":"2026-08-04T14:42:41.606782Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.09087","last_updated":"2024-10-07T15:02:12Z","snapshot_observed_at":"2026-07-06T19:32:02.130965Z","submitted_at":"2024-10-07T15:02:12Z","title":"Mechanistic?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.09087","snapshot_observed_at":"2026-08-04T14:42:41.792799Z","title":"Mechanistic? arXiv preprint arXiv:2410.09087, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:41.792799Z"},"links":{"cited_paper":"/paper/2410.09087","citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:29e9e406b8acce49d2faf88a747e12c99bd0d506a1326393ac64769cac363cc1","observation_id":"bf99bf03-f03e-4e28-982f-3879557e73a0","resolution":{"observed_at":"2026-08-04T14:42:41.792799Z","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":"2026-08-04T14:42:41.977675Z","title":"Taking features out of superposition with sparse autoencoders, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:41.977675Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:a2eedc926da02089cbe898a968d977373288812a2baa4efdf3fcd8fd18bbd351","observation_id":"01e126f0-ea00-4d97-955f-c7d81c75cc02","resolution":{"observed_at":"2026-08-04T14:42:41.977675Z","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":"2026-08-04T14:42:42.113505Z","title":"What does clip know about a red circle? visual prompt engineering for vlms","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:42.113505Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:3ab27d87545158d027001dc411b48f9c9f365b1437075a547ac0db7761677a90","observation_id":"c44b7e19-896a-4962-a910-617c243ba2e6","resolution":{"observed_at":"2026-08-04T14:42:42.113505Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1409.1556","last_updated":"2015-04-10T16:25:04Z","snapshot_observed_at":"2026-07-06T03:53:32.549552Z","submitted_at":"2014-09-04T19:48:04Z","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.1556","snapshot_observed_at":"2026-08-04T14:42:42.306794Z","title":"Very deep convolutional networks for large-scale image recognition","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:42.306794Z"},"links":{"cited_paper":"/paper/1409.1556","citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:5e5667cc19b069f2643b2ea0a8c5ccd948075f470286f344f68169ef60c2cb33","observation_id":"85492d93-3a48-4c6a-8cc5-ac3b4abacf61","resolution":{"observed_at":"2026-08-04T14:42:42.306794Z","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":"2026-08-04T14:42:42.507515Z","title":"Goodfellow, and Rob Fergus","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:42.507515Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:9361f6b8286801d0df1f102c10e7499e183051014708dd8ebdbba62a79c0fc61","observation_id":"65f5fdb1-f53b-4935-94f1-c1958d98a9c4","resolution":{"observed_at":"2026-08-04T14:42:42.507515Z","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":"2026-08-04T14:42:42.642396Z","title":"Rethinking the inception architecture for computer vision","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:42.642396Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:7927db46f63dfe58e1298e1e0ed253294e31ac9e8ca90686140cc800df534be0","observation_id":"c8d139e5-8f59-4712-8516-042a7ec289ee","resolution":{"observed_at":"2026-08-04T14:42:42.642396Z","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":"2026-08-04T14:42:42.771648Z","title":"Efficientnet: Rethinking model scaling for convolutional neural networks","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:42.771648Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:a744c01030016f8fd7082fdc10d603ef5d6629153ca5ad122235e61c083ab1a3","observation_id":"08907995-f069-4c29-a7cb-61dd705c78dd","resolution":{"observed_at":"2026-08-04T14:42:42.771648Z","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":"2026-08-04T14:42:42.866092Z","title":"Multi-dimensional concept discovery ( MCD ): A unifying framework with completeness guarantees","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:42.866092Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:4cd113b28d6d16bae031e20623dea529bf03fbed5a13f523d68157801fd8979f","observation_id":"9e170910-ce3b-4342-9f60-493d8dd9bdc9","resolution":{"observed_at":"2026-08-04T14:42:42.866092Z","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":"2026-08-04T14:42:43.035511Z","title":"pytorch-nmf: Non-negative matrix fatorization in pytorch","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:43.035511Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:6adae1b7d051e1fbcb7b945b7c5abc6c96376a8458c5258403f0ff9c8fde9483","observation_id":"311eef29-b9bb-4809-ae45-b65ffe9d3bb7","resolution":{"observed_at":"2026-08-04T14:42:43.035511Z","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":"2026-08-04T14:42:43.177903Z","title":"Post-hoc concept bottleneck models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:43.177903Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:c9c3b84d5694b08d54abb210ddd6d6be969c5ca4810ad5960fb37c4105c4a0f0","observation_id":"8a969c49-23bc-497f-a35a-a2f90c3482c5","resolution":{"observed_at":"2026-08-04T14:42:43.177903Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.15949","last_updated":"2023-04-04T06:43:19Z","snapshot_observed_at":"2026-07-06T10:54:39.400060Z","submitted_at":"2021-03-29T20:51:33Z","title":"Transformer visualization via dictionary learning: contextualized embedding as a linear superposition of transformer factors","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.15949","snapshot_observed_at":"2026-08-04T14:42:43.296691Z","title":"Olshausen, and Yann LeCun","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:43.296691Z"},"links":{"cited_paper":"/paper/2103.15949","citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:c33e63319834bd8a178e1ce4d65bf91b9651160d1e3b9e3c7ac7dea2396f2cd3","observation_id":"04420119-9f57-438c-bc58-87fe52c4fc88","resolution":{"observed_at":"2026-08-04T14:42:43.296691Z","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":"2026-08-04T14:42:43.373068Z","title":"Invertible concept-based explanations for cnn models with non-negative concept activation vectors","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:43.373068Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:b357b27d01dbe49f5974c24270f32d9e475bca77e0290bf8c9fa00d505621b0d","observation_id":"27883a32-4a9a-4838-b000-e79d2ffb20a8","resolution":{"observed_at":"2026-08-04T14:42:43.373068Z","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":"2026-08-04T14:42:43.443673Z","title":"Places: A 10 million image database for scene recognition","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:43.443673Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:9a6c47024fbac8fb08b397f28e24b1c8ce7297f1f116d33e5613c0e8fc938107","observation_id":"4dc857c2-59d1-40c9-bafb-555e39592480","resolution":{"observed_at":"2026-08-04T14:42:43.443673Z","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":"2026-08-04T14:42:43.514986Z","title":"Interpretable basis decomposition for visual explanation","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:43.514986Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:41c4cacda402109d9647c1370379911747ecb7f9045f819ad918d313a328c906","observation_id":"faadb2c4-bf52-4727-ad90-16beab72a331","resolution":{"observed_at":"2026-08-04T14:42:43.514986Z","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":"2026-08-04T14:42:43.581679Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks","version":4},"reference_index":67,"source":"arxiv_source","source_observed_at":"2026-08-04T14:42:43.581679Z"},"links":{"citing_paper":"/paper/2509.23926"},"observation_digest":"sha256:b2e328e1d2297dbf564e8f6f16ca437c083b338727402dbca0f13623cbcd1505","observation_id":"2f47cb22-3e56-4034-8598-c0cb3a14d098","resolution":{"observed_at":"2026-08-04T14:42:43.581679Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2509.23926","last_updated":"2026-07-20T20:23:11Z","latest_version":4,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T07:45:46.173974Z","submitted_at":"2025-09-28T15:02:34Z","title":"Learning Encoding-Decoding Direction Pairs to Unveil Concepts of Influence in Deep Vision Networks"},"reference_resolution":{"displayed":67,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":67,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":67},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 67 of 67 outbound references and 1 inbound Pith citation observation for arXiv:2509.23926."}