{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:KXKL7F2MM6M6LFHI7V43QEYITZ","short_pith_number":"pith:KXKL7F2M","schema_version":"1.0","canonical_sha256":"55d4bf974c6799e594e8fd79b813089e5a6ac717be78213b8c92973ed1b30f29","source":{"kind":"arxiv","id":"2307.10026","version":1},"attestation_state":"computed","paper":{"title":"Contextual Reliability: When Different Features Matter in Different Contexts","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Aditi Raghunathan, Amrith Setlur, Anca D. Dragan, Daniel S. Brown, Gaurav Ghosal","submitted_at":"2023-07-19T15:11:04Z","abstract_excerpt":"Deep neural networks often fail catastrophically by relying on spurious correlations. Most prior work assumes a clear dichotomy into spurious and reliable features; however, this is often unrealistic. For example, most of the time we do not want an autonomous car to simply copy the speed of surrounding cars -- we don't want our car to run a red light if a neighboring car does so. However, we cannot simply enforce invariance to next-lane speed, since it could provide valuable information about an unobservable pedestrian at a crosswalk. Thus, universally ignoring features that are sometimes (but"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2307.10026","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-07-19T15:11:04Z","cross_cats_sorted":[],"title_canon_sha256":"d75801678e76d331c08ab5d3cf54133c36357ffa43c769d638b92a05d6af923c","abstract_canon_sha256":"5281ee9897c683c48fd5454e3a9369c000a22e946524663e66c60fccf6f4932f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:32:49.538284Z","signature_b64":"1VfY95q7ZIs8Vx/j3NpFWqAPxRXmceBBHjNf9VMmNRyOzy6JEsUKFtXIvo5xs5hKxMTROvwMserdqmcotx3JCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"55d4bf974c6799e594e8fd79b813089e5a6ac717be78213b8c92973ed1b30f29","last_reissued_at":"2026-07-05T06:32:49.537768Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:32:49.537768Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Contextual Reliability: When Different Features Matter in Different Contexts","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Aditi Raghunathan, Amrith Setlur, Anca D. Dragan, Daniel S. Brown, Gaurav Ghosal","submitted_at":"2023-07-19T15:11:04Z","abstract_excerpt":"Deep neural networks often fail catastrophically by relying on spurious correlations. Most prior work assumes a clear dichotomy into spurious and reliable features; however, this is often unrealistic. For example, most of the time we do not want an autonomous car to simply copy the speed of surrounding cars -- we don't want our car to run a red light if a neighboring car does so. However, we cannot simply enforce invariance to next-lane speed, since it could provide valuable information about an unobservable pedestrian at a crosswalk. Thus, universally ignoring features that are sometimes (but"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.10026","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2307.10026/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2307.10026","created_at":"2026-07-05T06:32:49.537838+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.10026v1","created_at":"2026-07-05T06:32:49.537838+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.10026","created_at":"2026-07-05T06:32:49.537838+00:00"},{"alias_kind":"pith_short_12","alias_value":"KXKL7F2MM6M6","created_at":"2026-07-05T06:32:49.537838+00:00"},{"alias_kind":"pith_short_16","alias_value":"KXKL7F2MM6M6LFHI","created_at":"2026-07-05T06:32:49.537838+00:00"},{"alias_kind":"pith_short_8","alias_value":"KXKL7F2M","created_at":"2026-07-05T06:32:49.537838+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KXKL7F2MM6M6LFHI7V43QEYITZ","json":"https://pith.science/pith/KXKL7F2MM6M6LFHI7V43QEYITZ.json","graph_json":"https://pith.science/api/pith-number/KXKL7F2MM6M6LFHI7V43QEYITZ/graph.json","events_json":"https://pith.science/api/pith-number/KXKL7F2MM6M6LFHI7V43QEYITZ/events.json","paper":"https://pith.science/paper/KXKL7F2M"},"agent_actions":{"view_html":"https://pith.science/pith/KXKL7F2MM6M6LFHI7V43QEYITZ","download_json":"https://pith.science/pith/KXKL7F2MM6M6LFHI7V43QEYITZ.json","view_paper":"https://pith.science/paper/KXKL7F2M","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.10026&json=true","fetch_graph":"https://pith.science/api/pith-number/KXKL7F2MM6M6LFHI7V43QEYITZ/graph.json","fetch_events":"https://pith.science/api/pith-number/KXKL7F2MM6M6LFHI7V43QEYITZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KXKL7F2MM6M6LFHI7V43QEYITZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KXKL7F2MM6M6LFHI7V43QEYITZ/action/storage_attestation","attest_author":"https://pith.science/pith/KXKL7F2MM6M6LFHI7V43QEYITZ/action/author_attestation","sign_citation":"https://pith.science/pith/KXKL7F2MM6M6LFHI7V43QEYITZ/action/citation_signature","submit_replication":"https://pith.science/pith/KXKL7F2MM6M6LFHI7V43QEYITZ/action/replication_record"}},"created_at":"2026-07-05T06:32:49.537838+00:00","updated_at":"2026-07-05T06:32:49.537838+00:00"}