{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:XYXLNXVTHG3DDVMCTIVJLSCKI6","short_pith_number":"pith:XYXLNXVT","schema_version":"1.0","canonical_sha256":"be2eb6deb339b631d5829a2a95c84a479833be8213a6c53b5a6797e0a3142006","source":{"kind":"arxiv","id":"2605.31200","version":1},"attestation_state":"computed","paper":{"title":"Beyond Additive Decompositions: Interpretability Through Separability","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Jinyang Liu, Munir Eberhardt Hiabu","submitted_at":"2026-05-29T12:08:14Z","abstract_excerpt":"Interpretable machine learning requires models that are accurate and structurally faithful to the data.Existing explainability methods rely heavily on additive representations (e.g., Generalized Additive Models (GAMs), SHapley Additive exPlanations (SHAP), functional ANOVA), which can suffer from signal cancellation and off-support extrapolation in the presence of strong interactions. We propose Tensor Separation Learning (TSL), a regression model that learns a sum of rank-1 products of univariate per-feature functions via a stagewise greedy procedure with orthogonal refitting. By enforcing se"},"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":"2605.31200","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-29T12:08:14Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"3778d38cd445032adff8c0198da93d4e1a6346bd89c352e51f97f56c1ffebf27","abstract_canon_sha256":"87478c00d2d932590c9137fd3129ec0e7e442d8a983ac10183b61eac14766500"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-01T01:04:03.574650Z","signature_b64":"Ojrf/nO4WaJt2thayI/rLnt7SoWzk4tAlMs4ov3JgtyPgfzz2vzyTDJh+GgEG66JGYu9U8/My/8CrK9w7Qk2Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"be2eb6deb339b631d5829a2a95c84a479833be8213a6c53b5a6797e0a3142006","last_reissued_at":"2026-06-01T01:04:03.574220Z","signature_status":"signed_v1","first_computed_at":"2026-06-01T01:04:03.574220Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Beyond Additive Decompositions: Interpretability Through Separability","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Jinyang Liu, Munir Eberhardt Hiabu","submitted_at":"2026-05-29T12:08:14Z","abstract_excerpt":"Interpretable machine learning requires models that are accurate and structurally faithful to the data.Existing explainability methods rely heavily on additive representations (e.g., Generalized Additive Models (GAMs), SHapley Additive exPlanations (SHAP), functional ANOVA), which can suffer from signal cancellation and off-support extrapolation in the presence of strong interactions. We propose Tensor Separation Learning (TSL), a regression model that learns a sum of rank-1 products of univariate per-feature functions via a stagewise greedy procedure with orthogonal refitting. By enforcing se"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.31200","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/2605.31200/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":"2605.31200","created_at":"2026-06-01T01:04:03.574284+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.31200v1","created_at":"2026-06-01T01:04:03.574284+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.31200","created_at":"2026-06-01T01:04:03.574284+00:00"},{"alias_kind":"pith_short_12","alias_value":"XYXLNXVTHG3D","created_at":"2026-06-01T01:04:03.574284+00:00"},{"alias_kind":"pith_short_16","alias_value":"XYXLNXVTHG3DDVMC","created_at":"2026-06-01T01:04:03.574284+00:00"},{"alias_kind":"pith_short_8","alias_value":"XYXLNXVT","created_at":"2026-06-01T01:04:03.574284+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/XYXLNXVTHG3DDVMCTIVJLSCKI6","json":"https://pith.science/pith/XYXLNXVTHG3DDVMCTIVJLSCKI6.json","graph_json":"https://pith.science/api/pith-number/XYXLNXVTHG3DDVMCTIVJLSCKI6/graph.json","events_json":"https://pith.science/api/pith-number/XYXLNXVTHG3DDVMCTIVJLSCKI6/events.json","paper":"https://pith.science/paper/XYXLNXVT"},"agent_actions":{"view_html":"https://pith.science/pith/XYXLNXVTHG3DDVMCTIVJLSCKI6","download_json":"https://pith.science/pith/XYXLNXVTHG3DDVMCTIVJLSCKI6.json","view_paper":"https://pith.science/paper/XYXLNXVT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.31200&json=true","fetch_graph":"https://pith.science/api/pith-number/XYXLNXVTHG3DDVMCTIVJLSCKI6/graph.json","fetch_events":"https://pith.science/api/pith-number/XYXLNXVTHG3DDVMCTIVJLSCKI6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XYXLNXVTHG3DDVMCTIVJLSCKI6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XYXLNXVTHG3DDVMCTIVJLSCKI6/action/storage_attestation","attest_author":"https://pith.science/pith/XYXLNXVTHG3DDVMCTIVJLSCKI6/action/author_attestation","sign_citation":"https://pith.science/pith/XYXLNXVTHG3DDVMCTIVJLSCKI6/action/citation_signature","submit_replication":"https://pith.science/pith/XYXLNXVTHG3DDVMCTIVJLSCKI6/action/replication_record"}},"created_at":"2026-06-01T01:04:03.574284+00:00","updated_at":"2026-06-01T01:04:03.574284+00:00"}