{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:LH5A4AHE57WE5CCRGPPSE2FA4F","short_pith_number":"pith:LH5A4AHE","schema_version":"1.0","canonical_sha256":"59fa0e00e4efec4e885133df2268a0e16a11a505c39a0ec378bd901c33dc81f4","source":{"kind":"arxiv","id":"2606.10669","version":1},"attestation_state":"computed","paper":{"title":"In Defense of Information Leakage in Concept-based Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.LG","authors_text":"Mateo Espinosa Zarlenga","submitted_at":"2026-06-09T10:19:41Z","abstract_excerpt":"Concept-based models (CMs), deep neural networks that ground their predictions on representations aligned with human-understandable concepts (e.g., \"round\", \"stripes\", etc.), have been shown to learn representations that leak concept-irrelevant information. As the traditional narrative goes, this leakage is undesirable and should be eradicated as it leads to uninterpretable models. In this paper, we posit that this conventional view of leakage in CMs is not only ill-posed, as the evidence of how leakage makes a model less interpretable is often inconclusive, but also bound to lead to impractic"},"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":"2606.10669","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-09T10:19:41Z","cross_cats_sorted":["cs.AI","cs.CR"],"title_canon_sha256":"75d599cabe2cdaeb4eb047058293b71f492a8db4ce82725a4094a73e44bc0621","abstract_canon_sha256":"e84e26b47671326e17ac7cddf381590939008d82ca8f90e4c33d01cf9b71b334"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-10T01:10:33.357804Z","signature_b64":"/IYKxyyaivZEpD6uQC+F7fw/Kcaz7pHmJaZcozx/TGrUugplzFHSGFmWkiatoK1RUobHkHqu8W42u5SpDP9xBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"59fa0e00e4efec4e885133df2268a0e16a11a505c39a0ec378bd901c33dc81f4","last_reissued_at":"2026-06-10T01:10:33.357004Z","signature_status":"signed_v1","first_computed_at":"2026-06-10T01:10:33.357004Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"In Defense of Information Leakage in Concept-based Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.LG","authors_text":"Mateo Espinosa Zarlenga","submitted_at":"2026-06-09T10:19:41Z","abstract_excerpt":"Concept-based models (CMs), deep neural networks that ground their predictions on representations aligned with human-understandable concepts (e.g., \"round\", \"stripes\", etc.), have been shown to learn representations that leak concept-irrelevant information. As the traditional narrative goes, this leakage is undesirable and should be eradicated as it leads to uninterpretable models. In this paper, we posit that this conventional view of leakage in CMs is not only ill-posed, as the evidence of how leakage makes a model less interpretable is often inconclusive, but also bound to lead to impractic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.10669","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/2606.10669/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":"2606.10669","created_at":"2026-06-10T01:10:33.357122+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.10669v1","created_at":"2026-06-10T01:10:33.357122+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.10669","created_at":"2026-06-10T01:10:33.357122+00:00"},{"alias_kind":"pith_short_12","alias_value":"LH5A4AHE57WE","created_at":"2026-06-10T01:10:33.357122+00:00"},{"alias_kind":"pith_short_16","alias_value":"LH5A4AHE57WE5CCR","created_at":"2026-06-10T01:10:33.357122+00:00"},{"alias_kind":"pith_short_8","alias_value":"LH5A4AHE","created_at":"2026-06-10T01:10:33.357122+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/LH5A4AHE57WE5CCRGPPSE2FA4F","json":"https://pith.science/pith/LH5A4AHE57WE5CCRGPPSE2FA4F.json","graph_json":"https://pith.science/api/pith-number/LH5A4AHE57WE5CCRGPPSE2FA4F/graph.json","events_json":"https://pith.science/api/pith-number/LH5A4AHE57WE5CCRGPPSE2FA4F/events.json","paper":"https://pith.science/paper/LH5A4AHE"},"agent_actions":{"view_html":"https://pith.science/pith/LH5A4AHE57WE5CCRGPPSE2FA4F","download_json":"https://pith.science/pith/LH5A4AHE57WE5CCRGPPSE2FA4F.json","view_paper":"https://pith.science/paper/LH5A4AHE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.10669&json=true","fetch_graph":"https://pith.science/api/pith-number/LH5A4AHE57WE5CCRGPPSE2FA4F/graph.json","fetch_events":"https://pith.science/api/pith-number/LH5A4AHE57WE5CCRGPPSE2FA4F/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LH5A4AHE57WE5CCRGPPSE2FA4F/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LH5A4AHE57WE5CCRGPPSE2FA4F/action/storage_attestation","attest_author":"https://pith.science/pith/LH5A4AHE57WE5CCRGPPSE2FA4F/action/author_attestation","sign_citation":"https://pith.science/pith/LH5A4AHE57WE5CCRGPPSE2FA4F/action/citation_signature","submit_replication":"https://pith.science/pith/LH5A4AHE57WE5CCRGPPSE2FA4F/action/replication_record"}},"created_at":"2026-06-10T01:10:33.357122+00:00","updated_at":"2026-06-10T01:10:33.357122+00:00"}