{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:WJZ6UMSBF2GLY32XYIMJVHNOBK","short_pith_number":"pith:WJZ6UMSB","schema_version":"1.0","canonical_sha256":"b273ea32412e8cbc6f57c2189a9dae0aa8ff2cb3052a8ead448934bdf9b1cfed","source":{"kind":"arxiv","id":"2604.08295","version":3},"attestation_state":"computed","paper":{"title":"U-CECE: A Universal Multi-Resolution Framework for Conceptual Counterfactual Explanations","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"U-CECE generates conceptual counterfactual explanations at three detail levels with neural approximations that match or exceed exact graph methods in human preference.","cross_cats":["cs.CV"],"primary_cat":"cs.AI","authors_text":"Angeliki Dimitriou, Giorgos Filandrianos, Giorgos Stamou, Maria Lymperaiou, Nikolaos Chaidos","submitted_at":"2026-04-09T14:30:00Z","abstract_excerpt":"As AI models grow more complex, explainability is essential for building trust, yet concept-based counterfactual methods still face a trade-off between expressivity and efficiency. Representing underlying concepts as atomic sets is fast but misses relational context, whereas full graph representations are more faithful but require solving the NP-hard Graph Edit Distance (GED) problem. We propose U-CECE, a unified, model-agnostic multi-resolution framework for conceptual counterfactual explanations that adapts to data regime and compute budget. U-CECE spans three levels of expressivity: atomic "},"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":true},"canonical_record":{"source":{"id":"2604.08295","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2026-04-09T14:30:00Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"69c8912e2bb5fa6ca3c6f6a7ae12459408b7086213b48538d7c3f327c058a935","abstract_canon_sha256":"a4a8877b52d5d698699d61d290252a2c922cafa7dc1a2222e6ab3ecc86530146"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-22T02:04:40.666244Z","signature_b64":"7zAEPqXxeZrmBsAGTWX4a4ftgrpXPfg74qT2+F1uwY+/SerEHGnVnlW8BnMgcjTES4axmYVMOrT3vkWHvCHuBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b273ea32412e8cbc6f57c2189a9dae0aa8ff2cb3052a8ead448934bdf9b1cfed","last_reissued_at":"2026-05-22T02:04:40.665144Z","signature_status":"signed_v1","first_computed_at":"2026-05-22T02:04:40.665144Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"U-CECE: A Universal Multi-Resolution Framework for Conceptual Counterfactual Explanations","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"U-CECE generates conceptual counterfactual explanations at three detail levels with neural approximations that match or exceed exact graph methods in human preference.","cross_cats":["cs.CV"],"primary_cat":"cs.AI","authors_text":"Angeliki Dimitriou, Giorgos Filandrianos, Giorgos Stamou, Maria Lymperaiou, Nikolaos Chaidos","submitted_at":"2026-04-09T14:30:00Z","abstract_excerpt":"As AI models grow more complex, explainability is essential for building trust, yet concept-based counterfactual methods still face a trade-off between expressivity and efficiency. Representing underlying concepts as atomic sets is fast but misses relational context, whereas full graph representations are more faithful but require solving the NP-hard Graph Edit Distance (GED) problem. We propose U-CECE, a unified, model-agnostic multi-resolution framework for conceptual counterfactual explanations that adapts to data regime and compute budget. U-CECE spans three levels of expressivity: atomic "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"human surveys and LVLM-based evaluation show that the retrieved structural counterfactuals are semantically equivalent to, and often preferred over, exact GED-based ground-truth explanations.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the chosen graph representations and the transductive GNN / inductive GAE approximations faithfully encode the semantic relations required for valid conceptual counterfactuals without introducing systematic distortions.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"U-CECE offers a unified model-agnostic framework spanning atomic, relational, and structural levels for conceptual counterfactuals, with structural GNN/GAE modes producing explanations semantically equivalent or preferred to exact GED ground truth on CUB and Visual Genome.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"U-CECE generates conceptual counterfactual explanations at three detail levels with neural approximations that match or exceed exact graph methods in human preference.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"b53abcd11f9bafd88b0d1ed559a22877dfcc2f242d0b25dcf72233ac902aa3f3"},"source":{"id":"2604.08295","kind":"arxiv","version":3},"verdict":{"id":"5cd24ab9-1201-4730-ae7c-8d14f83e06f3","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T18:19:46.387872Z","strongest_claim":"human surveys and LVLM-based evaluation show that the retrieved structural counterfactuals are semantically equivalent to, and often preferred over, exact GED-based ground-truth explanations.","one_line_summary":"U-CECE offers a unified model-agnostic framework spanning atomic, relational, and structural levels for conceptual counterfactuals, with structural GNN/GAE modes producing explanations semantically equivalent or preferred to exact GED ground truth on CUB and Visual Genome.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the chosen graph representations and the transductive GNN / inductive GAE approximations faithfully encode the semantic relations required for valid conceptual counterfactuals without introducing systematic distortions.","pith_extraction_headline":"U-CECE generates conceptual counterfactual explanations at three detail levels with neural approximations that match or exceed exact graph methods in human preference."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.08295/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":2,"snapshot_sha256":"63f02b7a32d8f4fbef2739f58d6dfdb7d0301d767848e75052b437e9b83acd81"},"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":"2604.08295","created_at":"2026-05-22T02:04:40.665305+00:00"},{"alias_kind":"arxiv_version","alias_value":"2604.08295v3","created_at":"2026-05-22T02:04:40.665305+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.08295","created_at":"2026-05-22T02:04:40.665305+00:00"},{"alias_kind":"pith_short_12","alias_value":"WJZ6UMSBF2GL","created_at":"2026-05-22T02:04:40.665305+00:00"},{"alias_kind":"pith_short_16","alias_value":"WJZ6UMSBF2GLY32X","created_at":"2026-05-22T02:04:40.665305+00:00"},{"alias_kind":"pith_short_8","alias_value":"WJZ6UMSB","created_at":"2026-05-22T02:04:40.665305+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":2,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WJZ6UMSBF2GLY32XYIMJVHNOBK","json":"https://pith.science/pith/WJZ6UMSBF2GLY32XYIMJVHNOBK.json","graph_json":"https://pith.science/api/pith-number/WJZ6UMSBF2GLY32XYIMJVHNOBK/graph.json","events_json":"https://pith.science/api/pith-number/WJZ6UMSBF2GLY32XYIMJVHNOBK/events.json","paper":"https://pith.science/paper/WJZ6UMSB"},"agent_actions":{"view_html":"https://pith.science/pith/WJZ6UMSBF2GLY32XYIMJVHNOBK","download_json":"https://pith.science/pith/WJZ6UMSBF2GLY32XYIMJVHNOBK.json","view_paper":"https://pith.science/paper/WJZ6UMSB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2604.08295&json=true","fetch_graph":"https://pith.science/api/pith-number/WJZ6UMSBF2GLY32XYIMJVHNOBK/graph.json","fetch_events":"https://pith.science/api/pith-number/WJZ6UMSBF2GLY32XYIMJVHNOBK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WJZ6UMSBF2GLY32XYIMJVHNOBK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WJZ6UMSBF2GLY32XYIMJVHNOBK/action/storage_attestation","attest_author":"https://pith.science/pith/WJZ6UMSBF2GLY32XYIMJVHNOBK/action/author_attestation","sign_citation":"https://pith.science/pith/WJZ6UMSBF2GLY32XYIMJVHNOBK/action/citation_signature","submit_replication":"https://pith.science/pith/WJZ6UMSBF2GLY32XYIMJVHNOBK/action/replication_record"}},"created_at":"2026-05-22T02:04:40.665305+00:00","updated_at":"2026-05-22T02:04:40.665305+00:00"}