{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:7XS7QDSRDF7YMNYUTY4J36KOLG","short_pith_number":"pith:7XS7QDSR","schema_version":"1.0","canonical_sha256":"fde5f80e51197f8637149e389df94e5998adc8ab0c920191e73c7bcfcee0e126","source":{"kind":"arxiv","id":"2309.12482","version":2},"attestation_state":"computed","paper":{"title":"State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Been Kim, Devleena Das, Sonia Chernova","submitted_at":"2023-09-21T20:55:21Z","abstract_excerpt":"As more non-AI experts use complex AI systems for daily tasks, there has been an increasing effort to develop methods that produce explanations of AI decision making that are understandable by non-AI experts. Towards this effort, leveraging higher-level concepts and producing concept-based explanations have become a popular method. Most concept-based explanations have been developed for classification techniques, and we posit that the few existing methods for sequential decision making are limited in scope. In this work, we first contribute a desiderata for defining concepts in sequential deci"},"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":"2309.12482","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-09-21T20:55:21Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1bc300b0a391b986ae8051a8e15d2cba5611f6d1745dab89fd5af8a7fcc8c24a","abstract_canon_sha256":"9288e259c293416d62c45f8af60aa3abf02cce601fe53227900e41acada88a4e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:11:18.412610Z","signature_b64":"azDOyrdi+AC/TghX9SJA6nL1Q3QgXcngXhjZXhiMPbtSgsWsZk+ppExKPa6h6ootnoYIqvqjN2E0nOcEBheyAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fde5f80e51197f8637149e389df94e5998adc8ab0c920191e73c7bcfcee0e126","last_reissued_at":"2026-07-05T07:11:18.412103Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:11:18.412103Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Been Kim, Devleena Das, Sonia Chernova","submitted_at":"2023-09-21T20:55:21Z","abstract_excerpt":"As more non-AI experts use complex AI systems for daily tasks, there has been an increasing effort to develop methods that produce explanations of AI decision making that are understandable by non-AI experts. Towards this effort, leveraging higher-level concepts and producing concept-based explanations have become a popular method. Most concept-based explanations have been developed for classification techniques, and we posit that the few existing methods for sequential decision making are limited in scope. In this work, we first contribute a desiderata for defining concepts in sequential deci"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.12482","kind":"arxiv","version":2},"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/2309.12482/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":"2309.12482","created_at":"2026-07-05T07:11:18.412161+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.12482v2","created_at":"2026-07-05T07:11:18.412161+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.12482","created_at":"2026-07-05T07:11:18.412161+00:00"},{"alias_kind":"pith_short_12","alias_value":"7XS7QDSRDF7Y","created_at":"2026-07-05T07:11:18.412161+00:00"},{"alias_kind":"pith_short_16","alias_value":"7XS7QDSRDF7YMNYU","created_at":"2026-07-05T07:11:18.412161+00:00"},{"alias_kind":"pith_short_8","alias_value":"7XS7QDSR","created_at":"2026-07-05T07:11:18.412161+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.14251","citing_title":"Natural Language Reinforcement Learning","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7XS7QDSRDF7YMNYUTY4J36KOLG","json":"https://pith.science/pith/7XS7QDSRDF7YMNYUTY4J36KOLG.json","graph_json":"https://pith.science/api/pith-number/7XS7QDSRDF7YMNYUTY4J36KOLG/graph.json","events_json":"https://pith.science/api/pith-number/7XS7QDSRDF7YMNYUTY4J36KOLG/events.json","paper":"https://pith.science/paper/7XS7QDSR"},"agent_actions":{"view_html":"https://pith.science/pith/7XS7QDSRDF7YMNYUTY4J36KOLG","download_json":"https://pith.science/pith/7XS7QDSRDF7YMNYUTY4J36KOLG.json","view_paper":"https://pith.science/paper/7XS7QDSR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.12482&json=true","fetch_graph":"https://pith.science/api/pith-number/7XS7QDSRDF7YMNYUTY4J36KOLG/graph.json","fetch_events":"https://pith.science/api/pith-number/7XS7QDSRDF7YMNYUTY4J36KOLG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7XS7QDSRDF7YMNYUTY4J36KOLG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7XS7QDSRDF7YMNYUTY4J36KOLG/action/storage_attestation","attest_author":"https://pith.science/pith/7XS7QDSRDF7YMNYUTY4J36KOLG/action/author_attestation","sign_citation":"https://pith.science/pith/7XS7QDSRDF7YMNYUTY4J36KOLG/action/citation_signature","submit_replication":"https://pith.science/pith/7XS7QDSRDF7YMNYUTY4J36KOLG/action/replication_record"}},"created_at":"2026-07-05T07:11:18.412161+00:00","updated_at":"2026-07-05T07:11:18.412161+00:00"}