{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:SPLEXO74LMXZJUOAYC52U45VZM","short_pith_number":"pith:SPLEXO74","schema_version":"1.0","canonical_sha256":"93d64bbbfc5b2f94d1c0c0bbaa73b5cb25c30a5d7092b68b0c7d85e0f0a32ae7","source":{"kind":"arxiv","id":"2506.01334","version":1},"attestation_state":"computed","paper":{"title":"Enhancing Interpretable Image Classification Through LLM Agents and Conditional Concept Bottleneck Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Deval Mehta, Wei Feng, Yiwen Jiang, Zongyuan Ge","submitted_at":"2025-06-02T05:25:52Z","abstract_excerpt":"Concept Bottleneck Models (CBMs) decompose image classification into a process governed by interpretable, human-readable concepts. Recent advances in CBMs have used Large Language Models (LLMs) to generate candidate concepts. However, a critical question remains: What is the optimal number of concepts to use? Current concept banks suffer from redundancy or insufficient coverage. To address this issue, we introduce a dynamic, agent-based approach that adjusts the concept bank in response to environmental feedback, optimizing the number of concepts for sufficiency yet concise coverage. Moreover,"},"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":"2506.01334","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-06-02T05:25:52Z","cross_cats_sorted":[],"title_canon_sha256":"47e150eb234269b7489a709dcc34612687469252544be81d3d5c3f2e6d43ed5b","abstract_canon_sha256":"aafbd5cfaca08154c6433ef025349874877d8ac4665552548d2805ab6a89ca9a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:14:11.895363Z","signature_b64":"9M0804H7Ap90Ge7ufzVLrAvl95DEt6SIDijhfseqdiQ6Qry6fhoO60VIpZJ59IdVfY0kPP6oZGTXhz3OrA0QCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"93d64bbbfc5b2f94d1c0c0bbaa73b5cb25c30a5d7092b68b0c7d85e0f0a32ae7","last_reissued_at":"2026-07-05T11:14:11.894837Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:14:11.894837Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhancing Interpretable Image Classification Through LLM Agents and Conditional Concept Bottleneck Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Deval Mehta, Wei Feng, Yiwen Jiang, Zongyuan Ge","submitted_at":"2025-06-02T05:25:52Z","abstract_excerpt":"Concept Bottleneck Models (CBMs) decompose image classification into a process governed by interpretable, human-readable concepts. Recent advances in CBMs have used Large Language Models (LLMs) to generate candidate concepts. However, a critical question remains: What is the optimal number of concepts to use? Current concept banks suffer from redundancy or insufficient coverage. To address this issue, we introduce a dynamic, agent-based approach that adjusts the concept bank in response to environmental feedback, optimizing the number of concepts for sufficiency yet concise coverage. Moreover,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.01334","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/2506.01334/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":"2506.01334","created_at":"2026-07-05T11:14:11.894897+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.01334v1","created_at":"2026-07-05T11:14:11.894897+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.01334","created_at":"2026-07-05T11:14:11.894897+00:00"},{"alias_kind":"pith_short_12","alias_value":"SPLEXO74LMXZ","created_at":"2026-07-05T11:14:11.894897+00:00"},{"alias_kind":"pith_short_16","alias_value":"SPLEXO74LMXZJUOA","created_at":"2026-07-05T11:14:11.894897+00:00"},{"alias_kind":"pith_short_8","alias_value":"SPLEXO74","created_at":"2026-07-05T11:14:11.894897+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.04326","citing_title":"Measuring What Matters: Synthetic Benchmarks for Concept Bottleneck Models","ref_index":40,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07140","citing_title":"Neurosymbolic Framework for Concept-Driven Logical Reasoning in Skeleton-Based Human Action Recognition","ref_index":54,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SPLEXO74LMXZJUOAYC52U45VZM","json":"https://pith.science/pith/SPLEXO74LMXZJUOAYC52U45VZM.json","graph_json":"https://pith.science/api/pith-number/SPLEXO74LMXZJUOAYC52U45VZM/graph.json","events_json":"https://pith.science/api/pith-number/SPLEXO74LMXZJUOAYC52U45VZM/events.json","paper":"https://pith.science/paper/SPLEXO74"},"agent_actions":{"view_html":"https://pith.science/pith/SPLEXO74LMXZJUOAYC52U45VZM","download_json":"https://pith.science/pith/SPLEXO74LMXZJUOAYC52U45VZM.json","view_paper":"https://pith.science/paper/SPLEXO74","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.01334&json=true","fetch_graph":"https://pith.science/api/pith-number/SPLEXO74LMXZJUOAYC52U45VZM/graph.json","fetch_events":"https://pith.science/api/pith-number/SPLEXO74LMXZJUOAYC52U45VZM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SPLEXO74LMXZJUOAYC52U45VZM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SPLEXO74LMXZJUOAYC52U45VZM/action/storage_attestation","attest_author":"https://pith.science/pith/SPLEXO74LMXZJUOAYC52U45VZM/action/author_attestation","sign_citation":"https://pith.science/pith/SPLEXO74LMXZJUOAYC52U45VZM/action/citation_signature","submit_replication":"https://pith.science/pith/SPLEXO74LMXZJUOAYC52U45VZM/action/replication_record"}},"created_at":"2026-07-05T11:14:11.894897+00:00","updated_at":"2026-07-05T11:14:11.894897+00:00"}