{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:L3EQJYTMVSHAH5KLF3L4SUXUYJ","short_pith_number":"pith:L3EQJYTM","schema_version":"1.0","canonical_sha256":"5ec904e26cac8e03f54b2ed7c952f4c2654c35cf6ed2270409e8464910a89592","source":{"kind":"arxiv","id":"2007.07407","version":2},"attestation_state":"computed","paper":{"title":"XAlgo: a Design Probe of Explaining Algorithms' Internal States via Question-Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Jordan Combitsis, Juan Rebanal, Xiang 'Anthony' Chen, Yuqi Tang","submitted_at":"2020-07-14T23:54:36Z","abstract_excerpt":"Algorithms often appear as 'black boxes' to non-expert users. While prior work focuses on explainable representations and expert-oriented exploration, we propose and study an interactive approach using question answering to explain deterministic algorithms to non-expert users who need to understand the algorithms' internal states (e.g., students learning algorithms, operators monitoring robots, admins troubleshooting network routing). We construct XAlgo -- a formal model that first classifies the type of question based on a taxonomy and generates an answer based on a set of rules that extract "},"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":"2007.07407","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2020-07-14T23:54:36Z","cross_cats_sorted":[],"title_canon_sha256":"346aa573bdf8143e8912a3a045f647a8482ba3d1e7e515ca239cdca4c76bf41f","abstract_canon_sha256":"df2dd25a0cfa65b253149f48bedfbfcc7c29f350d1ca897f7b9d815bd72e90a8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:18:53.864275Z","signature_b64":"DoD4NiTZYCfLBeIgE8VQBoQisZw5s/dTaHf2br5ZasLVnRFtBusrlTxQ/iL5u811ogfpMszjuPQ0UPc6hZclCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ec904e26cac8e03f54b2ed7c952f4c2654c35cf6ed2270409e8464910a89592","last_reissued_at":"2026-07-05T02:18:53.863925Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:18:53.863925Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"XAlgo: a Design Probe of Explaining Algorithms' Internal States via Question-Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Jordan Combitsis, Juan Rebanal, Xiang 'Anthony' Chen, Yuqi Tang","submitted_at":"2020-07-14T23:54:36Z","abstract_excerpt":"Algorithms often appear as 'black boxes' to non-expert users. While prior work focuses on explainable representations and expert-oriented exploration, we propose and study an interactive approach using question answering to explain deterministic algorithms to non-expert users who need to understand the algorithms' internal states (e.g., students learning algorithms, operators monitoring robots, admins troubleshooting network routing). We construct XAlgo -- a formal model that first classifies the type of question based on a taxonomy and generates an answer based on a set of rules that extract "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.07407","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/2007.07407/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":"2007.07407","created_at":"2026-07-05T02:18:53.863980+00:00"},{"alias_kind":"arxiv_version","alias_value":"2007.07407v2","created_at":"2026-07-05T02:18:53.863980+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.07407","created_at":"2026-07-05T02:18:53.863980+00:00"},{"alias_kind":"pith_short_12","alias_value":"L3EQJYTMVSHA","created_at":"2026-07-05T02:18:53.863980+00:00"},{"alias_kind":"pith_short_16","alias_value":"L3EQJYTMVSHAH5KL","created_at":"2026-07-05T02:18:53.863980+00:00"},{"alias_kind":"pith_short_8","alias_value":"L3EQJYTM","created_at":"2026-07-05T02:18:53.863980+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.07005","citing_title":"Explainable AI the Latest Advancements and New Trends","ref_index":77,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L3EQJYTMVSHAH5KLF3L4SUXUYJ","json":"https://pith.science/pith/L3EQJYTMVSHAH5KLF3L4SUXUYJ.json","graph_json":"https://pith.science/api/pith-number/L3EQJYTMVSHAH5KLF3L4SUXUYJ/graph.json","events_json":"https://pith.science/api/pith-number/L3EQJYTMVSHAH5KLF3L4SUXUYJ/events.json","paper":"https://pith.science/paper/L3EQJYTM"},"agent_actions":{"view_html":"https://pith.science/pith/L3EQJYTMVSHAH5KLF3L4SUXUYJ","download_json":"https://pith.science/pith/L3EQJYTMVSHAH5KLF3L4SUXUYJ.json","view_paper":"https://pith.science/paper/L3EQJYTM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2007.07407&json=true","fetch_graph":"https://pith.science/api/pith-number/L3EQJYTMVSHAH5KLF3L4SUXUYJ/graph.json","fetch_events":"https://pith.science/api/pith-number/L3EQJYTMVSHAH5KLF3L4SUXUYJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L3EQJYTMVSHAH5KLF3L4SUXUYJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L3EQJYTMVSHAH5KLF3L4SUXUYJ/action/storage_attestation","attest_author":"https://pith.science/pith/L3EQJYTMVSHAH5KLF3L4SUXUYJ/action/author_attestation","sign_citation":"https://pith.science/pith/L3EQJYTMVSHAH5KLF3L4SUXUYJ/action/citation_signature","submit_replication":"https://pith.science/pith/L3EQJYTMVSHAH5KLF3L4SUXUYJ/action/replication_record"}},"created_at":"2026-07-05T02:18:53.863980+00:00","updated_at":"2026-07-05T02:18:53.863980+00:00"}