{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:2QKUSL3QDZFMEKNG6OYGLJGBPS","short_pith_number":"pith:2QKUSL3Q","schema_version":"1.0","canonical_sha256":"d415492f701e4ac229a6f3b065a4c17c90c85c94c570e9bcab65e22799d427c1","source":{"kind":"arxiv","id":"2506.21102","version":1},"attestation_state":"computed","paper":{"title":"Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"David Debot, Gabriele Dominici, Giuseppe Marra, Pietro Barbiero","submitted_at":"2025-06-26T08:56:55Z","abstract_excerpt":"Concept-Based Models (CBMs) are a class of deep learning models that provide interpretability by explaining predictions through high-level concepts. These models first predict concepts and then use them to perform a downstream task. However, current CBMs offer interpretability only for the final task prediction, while the concept predictions themselves are typically made via black-box neural networks. To address this limitation, we propose Hierarchical Concept Memory Reasoner (H-CMR), a new CBM that provides interpretability for both concept and task predictions. H-CMR models relationships bet"},"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":"2506.21102","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-26T08:56:55Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"cc0ea2fd05438f453dbc68136a117ffef0884218078fd988bd7c71b36c1de06b","abstract_canon_sha256":"89dfea615ee608ea54cc32ddf24e40aa88d7709036f67ec6bc5ff5e29165af4d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:27:40.145173Z","signature_b64":"vVZEU0Os+Q8INRZWcrvQaTppVNv/NV3z+/Z0nV6jZ0wzrxa8k3f72nYU5OZH5XRGq7uUWDWWNKRncYFdcCIyBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d415492f701e4ac229a6f3b065a4c17c90c85c94c570e9bcab65e22799d427c1","last_reissued_at":"2026-07-05T11:27:40.144711Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:27:40.144711Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Interpretable Hierarchical Concept Reasoning through Attention-Guided Graph Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"David Debot, Gabriele Dominici, Giuseppe Marra, Pietro Barbiero","submitted_at":"2025-06-26T08:56:55Z","abstract_excerpt":"Concept-Based Models (CBMs) are a class of deep learning models that provide interpretability by explaining predictions through high-level concepts. These models first predict concepts and then use them to perform a downstream task. However, current CBMs offer interpretability only for the final task prediction, while the concept predictions themselves are typically made via black-box neural networks. To address this limitation, we propose Hierarchical Concept Memory Reasoner (H-CMR), a new CBM that provides interpretability for both concept and task predictions. H-CMR models relationships bet"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.21102","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/2506.21102/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":"2506.21102","created_at":"2026-07-05T11:27:40.144768+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.21102v1","created_at":"2026-07-05T11:27:40.144768+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.21102","created_at":"2026-07-05T11:27:40.144768+00:00"},{"alias_kind":"pith_short_12","alias_value":"2QKUSL3QDZFM","created_at":"2026-07-05T11:27:40.144768+00:00"},{"alias_kind":"pith_short_16","alias_value":"2QKUSL3QDZFMEKNG","created_at":"2026-07-05T11:27:40.144768+00:00"},{"alias_kind":"pith_short_8","alias_value":"2QKUSL3Q","created_at":"2026-07-05T11:27:40.144768+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.06440","citing_title":"Hyperbolic Concept Bottleneck Models","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06440","citing_title":"Hyperbolic Concept Bottleneck Models","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2QKUSL3QDZFMEKNG6OYGLJGBPS","json":"https://pith.science/pith/2QKUSL3QDZFMEKNG6OYGLJGBPS.json","graph_json":"https://pith.science/api/pith-number/2QKUSL3QDZFMEKNG6OYGLJGBPS/graph.json","events_json":"https://pith.science/api/pith-number/2QKUSL3QDZFMEKNG6OYGLJGBPS/events.json","paper":"https://pith.science/paper/2QKUSL3Q"},"agent_actions":{"view_html":"https://pith.science/pith/2QKUSL3QDZFMEKNG6OYGLJGBPS","download_json":"https://pith.science/pith/2QKUSL3QDZFMEKNG6OYGLJGBPS.json","view_paper":"https://pith.science/paper/2QKUSL3Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.21102&json=true","fetch_graph":"https://pith.science/api/pith-number/2QKUSL3QDZFMEKNG6OYGLJGBPS/graph.json","fetch_events":"https://pith.science/api/pith-number/2QKUSL3QDZFMEKNG6OYGLJGBPS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2QKUSL3QDZFMEKNG6OYGLJGBPS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2QKUSL3QDZFMEKNG6OYGLJGBPS/action/storage_attestation","attest_author":"https://pith.science/pith/2QKUSL3QDZFMEKNG6OYGLJGBPS/action/author_attestation","sign_citation":"https://pith.science/pith/2QKUSL3QDZFMEKNG6OYGLJGBPS/action/citation_signature","submit_replication":"https://pith.science/pith/2QKUSL3QDZFMEKNG6OYGLJGBPS/action/replication_record"}},"created_at":"2026-07-05T11:27:40.144768+00:00","updated_at":"2026-07-05T11:27:40.144768+00:00"}