{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:4NHJZT6D2KQ7SZD2CMPZMKK4WN","short_pith_number":"pith:4NHJZT6D","schema_version":"1.0","canonical_sha256":"e34e9ccfc3d2a1f9647a131f96295cb35811359cbe7c3a2d7993b373c2ea7609","source":{"kind":"arxiv","id":"2606.22447","version":1},"attestation_state":"computed","paper":{"title":"A Differentiable Atari VCS:A Complex, Fully Known Ground Truth for Explainable AI","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Andreas Maier, Patrick Krauss, Siming Bayer","submitted_at":"2026-06-21T11:46:00Z","abstract_excerpt":"Explanation requires ground truth: to verify an account of a system we must know its inner functioning-just what is missing where explainable AI (XAI) is most needed. Systems we can study fall into two camps. Simple, procedural one-decision trees, rule lists, sparse linear models-have a known but trivial mechanism, so explaining them tests nothing; genuinely complex ones-deep networks, real-world tasks-need XAI but have no ground-truth inner functioning, so an explanation can be plausible, confident, and wrong with no way to tell. We remove this dichotomy with a study object both genuinely com"},"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":"2606.22447","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-21T11:46:00Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c019e695c0271649dd93daa3f16ca2cbc3e546ef12f1c8cece9c7a47f7e0fa46","abstract_canon_sha256":"24dedbdeb99cb6e6a78080612d4e2839d3f3e3b8d29c100b084c6a53b8366cc6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T02:13:38.639075Z","signature_b64":"rpblk3mN+TS4HUGru9Ni726bLEXDuXbi4x2ZRaBC5so0JtBP4M5XbYLUOfKsuYi5asiZrFEI5reCxpX5fiEJAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e34e9ccfc3d2a1f9647a131f96295cb35811359cbe7c3a2d7993b373c2ea7609","last_reissued_at":"2026-06-23T02:13:38.638701Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T02:13:38.638701Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Differentiable Atari VCS:A Complex, Fully Known Ground Truth for Explainable AI","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Andreas Maier, Patrick Krauss, Siming Bayer","submitted_at":"2026-06-21T11:46:00Z","abstract_excerpt":"Explanation requires ground truth: to verify an account of a system we must know its inner functioning-just what is missing where explainable AI (XAI) is most needed. Systems we can study fall into two camps. Simple, procedural one-decision trees, rule lists, sparse linear models-have a known but trivial mechanism, so explaining them tests nothing; genuinely complex ones-deep networks, real-world tasks-need XAI but have no ground-truth inner functioning, so an explanation can be plausible, confident, and wrong with no way to tell. We remove this dichotomy with a study object both genuinely com"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.22447","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/2606.22447/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":"2606.22447","created_at":"2026-06-23T02:13:38.638765+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.22447v1","created_at":"2026-06-23T02:13:38.638765+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.22447","created_at":"2026-06-23T02:13:38.638765+00:00"},{"alias_kind":"pith_short_12","alias_value":"4NHJZT6D2KQ7","created_at":"2026-06-23T02:13:38.638765+00:00"},{"alias_kind":"pith_short_16","alias_value":"4NHJZT6D2KQ7SZD2","created_at":"2026-06-23T02:13:38.638765+00:00"},{"alias_kind":"pith_short_8","alias_value":"4NHJZT6D","created_at":"2026-06-23T02:13:38.638765+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/4NHJZT6D2KQ7SZD2CMPZMKK4WN","json":"https://pith.science/pith/4NHJZT6D2KQ7SZD2CMPZMKK4WN.json","graph_json":"https://pith.science/api/pith-number/4NHJZT6D2KQ7SZD2CMPZMKK4WN/graph.json","events_json":"https://pith.science/api/pith-number/4NHJZT6D2KQ7SZD2CMPZMKK4WN/events.json","paper":"https://pith.science/paper/4NHJZT6D"},"agent_actions":{"view_html":"https://pith.science/pith/4NHJZT6D2KQ7SZD2CMPZMKK4WN","download_json":"https://pith.science/pith/4NHJZT6D2KQ7SZD2CMPZMKK4WN.json","view_paper":"https://pith.science/paper/4NHJZT6D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.22447&json=true","fetch_graph":"https://pith.science/api/pith-number/4NHJZT6D2KQ7SZD2CMPZMKK4WN/graph.json","fetch_events":"https://pith.science/api/pith-number/4NHJZT6D2KQ7SZD2CMPZMKK4WN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4NHJZT6D2KQ7SZD2CMPZMKK4WN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4NHJZT6D2KQ7SZD2CMPZMKK4WN/action/storage_attestation","attest_author":"https://pith.science/pith/4NHJZT6D2KQ7SZD2CMPZMKK4WN/action/author_attestation","sign_citation":"https://pith.science/pith/4NHJZT6D2KQ7SZD2CMPZMKK4WN/action/citation_signature","submit_replication":"https://pith.science/pith/4NHJZT6D2KQ7SZD2CMPZMKK4WN/action/replication_record"}},"created_at":"2026-06-23T02:13:38.638765+00:00","updated_at":"2026-06-23T02:13:38.638765+00:00"}