{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:JHI7YOCNZMCNM5N26YKA4VQQZU","short_pith_number":"pith:JHI7YOCN","schema_version":"1.0","canonical_sha256":"49d1fc384dcb04d675baf6140e5610cd114091f7d20d2fdd198a2b0bccf354b4","source":{"kind":"arxiv","id":"2606.25923","version":1},"attestation_state":"computed","paper":{"title":"$\\text{DT}^2$: Decision-Targeted Digital Twins","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Harry Amad, Mihaela van der Schaar","submitted_at":"2026-06-24T15:02:24Z","abstract_excerpt":"A digital twin (DT) is a virtual model of a real-world system that can assist decision-making by simulating scenarios induced by different policies. However, typical machine learning-based DTs do not optimise for this use case. We prove that, when model capacity is limited, training DTs to minimise one-step transition errors can produce suboptimal models for ranking sets of policies according to a reward function. We further show that this holds empirically, even with expressive model classes. To address this, we introduce $\\text{DT}^2$, a decision-targeted DT training paradigm. Firstly, $\\tex"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2606.25923","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-24T15:02:24Z","cross_cats_sorted":[],"title_canon_sha256":"e283d77c55ce09972c4d783730f1531e04a9272f4a0f33f3f9d9408775ae7769","abstract_canon_sha256":"d3381e84bdd529fd5d936de3f37f2c9247f9b9230a737b0165e735c4d545ab0c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-25T01:18:43.323910Z","signature_b64":"5FswZj9KKSNf9hpfwbCoEMH9amzVCiz3mUi1ZU8n7PvnZb1gJQsUPJR+fKSFByrGJRemVoH79fKpfhclPENUDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"49d1fc384dcb04d675baf6140e5610cd114091f7d20d2fdd198a2b0bccf354b4","last_reissued_at":"2026-06-25T01:18:43.323560Z","signature_status":"signed_v1","first_computed_at":"2026-06-25T01:18:43.323560Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"$\\text{DT}^2$: Decision-Targeted Digital Twins","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Harry Amad, Mihaela van der Schaar","submitted_at":"2026-06-24T15:02:24Z","abstract_excerpt":"A digital twin (DT) is a virtual model of a real-world system that can assist decision-making by simulating scenarios induced by different policies. However, typical machine learning-based DTs do not optimise for this use case. We prove that, when model capacity is limited, training DTs to minimise one-step transition errors can produce suboptimal models for ranking sets of policies according to a reward function. We further show that this holds empirically, even with expressive model classes. To address this, we introduce $\\text{DT}^2$, a decision-targeted DT training paradigm. Firstly, $\\tex"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.25923","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/2606.25923/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":"2606.25923","created_at":"2026-06-25T01:18:43.323624+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.25923v1","created_at":"2026-06-25T01:18:43.323624+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.25923","created_at":"2026-06-25T01:18:43.323624+00:00"},{"alias_kind":"pith_short_12","alias_value":"JHI7YOCNZMCN","created_at":"2026-06-25T01:18:43.323624+00:00"},{"alias_kind":"pith_short_16","alias_value":"JHI7YOCNZMCNM5N2","created_at":"2026-06-25T01:18:43.323624+00:00"},{"alias_kind":"pith_short_8","alias_value":"JHI7YOCN","created_at":"2026-06-25T01:18:43.323624+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/JHI7YOCNZMCNM5N26YKA4VQQZU","json":"https://pith.science/pith/JHI7YOCNZMCNM5N26YKA4VQQZU.json","graph_json":"https://pith.science/api/pith-number/JHI7YOCNZMCNM5N26YKA4VQQZU/graph.json","events_json":"https://pith.science/api/pith-number/JHI7YOCNZMCNM5N26YKA4VQQZU/events.json","paper":"https://pith.science/paper/JHI7YOCN"},"agent_actions":{"view_html":"https://pith.science/pith/JHI7YOCNZMCNM5N26YKA4VQQZU","download_json":"https://pith.science/pith/JHI7YOCNZMCNM5N26YKA4VQQZU.json","view_paper":"https://pith.science/paper/JHI7YOCN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.25923&json=true","fetch_graph":"https://pith.science/api/pith-number/JHI7YOCNZMCNM5N26YKA4VQQZU/graph.json","fetch_events":"https://pith.science/api/pith-number/JHI7YOCNZMCNM5N26YKA4VQQZU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JHI7YOCNZMCNM5N26YKA4VQQZU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JHI7YOCNZMCNM5N26YKA4VQQZU/action/storage_attestation","attest_author":"https://pith.science/pith/JHI7YOCNZMCNM5N26YKA4VQQZU/action/author_attestation","sign_citation":"https://pith.science/pith/JHI7YOCNZMCNM5N26YKA4VQQZU/action/citation_signature","submit_replication":"https://pith.science/pith/JHI7YOCNZMCNM5N26YKA4VQQZU/action/replication_record"}},"created_at":"2026-06-25T01:18:43.323624+00:00","updated_at":"2026-06-25T01:18:43.323624+00:00"}