{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:N2HTUNEMK3SN7DWCNKUN3INPTO","short_pith_number":"pith:N2HTUNEM","schema_version":"1.0","canonical_sha256":"6e8f3a348c56e4df8ec26aa8dda1af9b9275c30c43b1427772fbfa75ec659b5d","source":{"kind":"arxiv","id":"2606.05471","version":1},"attestation_state":"computed","paper":{"title":"Formal Concept Lattices are Good Semantic Scaffolds for Concept-Based Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ankit Saha, Deepika SN Vemuri, Krishn Vishwas Kher, Sayanta Adhikari, Vineeth N Balasubramanian","submitted_at":"2026-06-03T21:50:29Z","abstract_excerpt":"Learning semantics is essential for deep learning models to be interpretable and better aligned with human reasoning. Concept-based models approach this by representing classes through meaningful semantic abstractions, but typically treat all concepts as a flat, unstructured set learned at a single neural network layer. This overlooks a fundamental property of human semantic understanding: concepts being organized hierarchically, from general to specific. While deep networks do learn a hierarchy of visual features, this structure is rarely aligned with explicit semantic hierarchies. Drawing on"},"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":"2606.05471","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-06-03T21:50:29Z","cross_cats_sorted":[],"title_canon_sha256":"77579decbe55bed09c4230aa8ebf0990b5a11d3db86b3c9e4ca9ef15b604db5c","abstract_canon_sha256":"01cc1335e06c925e0cef910757dd899a325d7b7306c651c60f2816c3c20ae3c7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-05T01:14:52.226589Z","signature_b64":"EBIbP6ExtX1W4CG1G5CUEdMdvLgjKCH/XlccKuYso2nJoFnPUjcI0rRdsT/bEpXUWNKPdTQEx8Yit4f4AtwsBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6e8f3a348c56e4df8ec26aa8dda1af9b9275c30c43b1427772fbfa75ec659b5d","last_reissued_at":"2026-06-05T01:14:52.226123Z","signature_status":"signed_v1","first_computed_at":"2026-06-05T01:14:52.226123Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Formal Concept Lattices are Good Semantic Scaffolds for Concept-Based Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ankit Saha, Deepika SN Vemuri, Krishn Vishwas Kher, Sayanta Adhikari, Vineeth N Balasubramanian","submitted_at":"2026-06-03T21:50:29Z","abstract_excerpt":"Learning semantics is essential for deep learning models to be interpretable and better aligned with human reasoning. Concept-based models approach this by representing classes through meaningful semantic abstractions, but typically treat all concepts as a flat, unstructured set learned at a single neural network layer. This overlooks a fundamental property of human semantic understanding: concepts being organized hierarchically, from general to specific. While deep networks do learn a hierarchy of visual features, this structure is rarely aligned with explicit semantic hierarchies. Drawing on"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.05471","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/2606.05471/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":"2606.05471","created_at":"2026-06-05T01:14:52.226178+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.05471v1","created_at":"2026-06-05T01:14:52.226178+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.05471","created_at":"2026-06-05T01:14:52.226178+00:00"},{"alias_kind":"pith_short_12","alias_value":"N2HTUNEMK3SN","created_at":"2026-06-05T01:14:52.226178+00:00"},{"alias_kind":"pith_short_16","alias_value":"N2HTUNEMK3SN7DWC","created_at":"2026-06-05T01:14:52.226178+00:00"},{"alias_kind":"pith_short_8","alias_value":"N2HTUNEM","created_at":"2026-06-05T01:14:52.226178+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/N2HTUNEMK3SN7DWCNKUN3INPTO","json":"https://pith.science/pith/N2HTUNEMK3SN7DWCNKUN3INPTO.json","graph_json":"https://pith.science/api/pith-number/N2HTUNEMK3SN7DWCNKUN3INPTO/graph.json","events_json":"https://pith.science/api/pith-number/N2HTUNEMK3SN7DWCNKUN3INPTO/events.json","paper":"https://pith.science/paper/N2HTUNEM"},"agent_actions":{"view_html":"https://pith.science/pith/N2HTUNEMK3SN7DWCNKUN3INPTO","download_json":"https://pith.science/pith/N2HTUNEMK3SN7DWCNKUN3INPTO.json","view_paper":"https://pith.science/paper/N2HTUNEM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.05471&json=true","fetch_graph":"https://pith.science/api/pith-number/N2HTUNEMK3SN7DWCNKUN3INPTO/graph.json","fetch_events":"https://pith.science/api/pith-number/N2HTUNEMK3SN7DWCNKUN3INPTO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/N2HTUNEMK3SN7DWCNKUN3INPTO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/N2HTUNEMK3SN7DWCNKUN3INPTO/action/storage_attestation","attest_author":"https://pith.science/pith/N2HTUNEMK3SN7DWCNKUN3INPTO/action/author_attestation","sign_citation":"https://pith.science/pith/N2HTUNEMK3SN7DWCNKUN3INPTO/action/citation_signature","submit_replication":"https://pith.science/pith/N2HTUNEMK3SN7DWCNKUN3INPTO/action/replication_record"}},"created_at":"2026-06-05T01:14:52.226178+00:00","updated_at":"2026-06-05T01:14:52.226178+00:00"}