{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:KSRVZIOSUCQKGUZG6VWMPSHFLW","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"0738637e9007eff41a60d508426609a33f9f51b11c3e593622182856ab2f0161","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-22T14:00:30Z","title_canon_sha256":"4fb4e3edace0f501b7483d532240dd07ab4f178bcf46c06d7b871218aa9dbfcb"},"schema_version":"1.0","source":{"id":"2606.00082","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.00082","created_at":"2026-06-02T00:03:14Z"},{"alias_kind":"arxiv_version","alias_value":"2606.00082v1","created_at":"2026-06-02T00:03:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.00082","created_at":"2026-06-02T00:03:14Z"},{"alias_kind":"pith_short_12","alias_value":"KSRVZIOSUCQK","created_at":"2026-06-02T00:03:14Z"},{"alias_kind":"pith_short_16","alias_value":"KSRVZIOSUCQKGUZG","created_at":"2026-06-02T00:03:14Z"},{"alias_kind":"pith_short_8","alias_value":"KSRVZIOS","created_at":"2026-06-02T00:03:14Z"}],"graph_snapshots":[{"event_id":"sha256:41741b8451edcdde20d30686874cf824249ac2c6c0808cdba51024a421180ab4","target":"graph","created_at":"2026-06-02T00:03:14Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2606.00082/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Explainability of deep learning algorithms is critical for computer-vision applications with high-stake decisions. Concept bottleneck models (CBM) have recently shown promising performance to provide explainable and accurate predictions for classification problems, based on a bottleneck of high-level concepts. Existing CBM methods rely on a linear aggregation of the concept scores to compute predictions. However, a large number of concepts is often used in this linear approach, which undermines explainability and favors information leakage. In general, the underlying relation between concepts ","authors_text":"Christophe Labreuche, Cl\\'ement B\\'enard, Manon Arfib, Victor Qu\\'etu","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-22T14:00:30Z","title":"Hoeffding Concept Bottleneck Models with Applications to Overhead Images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.00082","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:73a92b6278a8fac74624ab18f56b561b64acb0252f2e9123657110cc28db07d5","target":"record","created_at":"2026-06-02T00:03:14Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"0738637e9007eff41a60d508426609a33f9f51b11c3e593622182856ab2f0161","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-22T14:00:30Z","title_canon_sha256":"4fb4e3edace0f501b7483d532240dd07ab4f178bcf46c06d7b871218aa9dbfcb"},"schema_version":"1.0","source":{"id":"2606.00082","kind":"arxiv","version":1}},"canonical_sha256":"54a35ca1d2a0a0a35326f56cc7c8e55da31821076ae6512d6994ae36220353fa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"54a35ca1d2a0a0a35326f56cc7c8e55da31821076ae6512d6994ae36220353fa","first_computed_at":"2026-06-02T00:03:14.481592Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-02T00:03:14.481592Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"13RVzuBcKzIeDS9xC5ZPJhqY0eKfHTanP00UQ5YxA/DQfYnNJggm9sVGFdr/q6ZJCwDGNB2XSttlFzN8Rk7MCQ==","signature_status":"signed_v1","signed_at":"2026-06-02T00:03:14.482092Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.00082","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:73a92b6278a8fac74624ab18f56b561b64acb0252f2e9123657110cc28db07d5","sha256:41741b8451edcdde20d30686874cf824249ac2c6c0808cdba51024a421180ab4"],"state_sha256":"67b8903b0a442e3e81cfdbc67a8a3ff95c75f51625cdc320478af9c55bd059d3"}