{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:JPUBRCN2S3GAFB7WC5ZYPWQRAT","short_pith_number":"pith:JPUBRCN2","schema_version":"1.0","canonical_sha256":"4be81889ba96cc0287f6177387da1104fea5ee69fa37471d1e306073f3748702","source":{"kind":"arxiv","id":"2605.26990","version":1},"attestation_state":"computed","paper":{"title":"Constrained Bayesian Experimental Design via Online Planning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Ayush Bharti, Daolang Huang, Sammie Katt, Samuel Kaski, Xinyu Zhang, Yujia Guo","submitted_at":"2026-05-26T13:13:28Z","abstract_excerpt":"Bayesian experimental design (BED) is a principled framework for data-efficient design of sequential experiments. However, existing BED methods are unable to adapt to dynamic constraints inherent in real-world tasks due to budget limitations, varying costs, or physical constraints that restrict how designs evolve over time. In this paper, we introduce a novel approach to BED that enables constrained optimization of experimental designs by combining offline pre-training of an amortized policy and a posterior network with online multi-step lookahead planning using scenario trees. We empirically "},"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":"2605.26990","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2026-05-26T13:13:28Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"7a8201cae24d6d0754ac8b834a7ad95598950b97c59810e642c588c4dbecfac9","abstract_canon_sha256":"405d0f6ea57d185cf34a51d8db6ae856a06d90d729ce9fdd1738774f865281a8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-27T01:06:23.060418Z","signature_b64":"8QNz/+liDXCQh7sV2FpqAbTMs27F8/xNdk6Wry8smfFmontKQFjWYXax7LEPQNwe4tC85F6JB5e6C8DhSE8xAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4be81889ba96cc0287f6177387da1104fea5ee69fa37471d1e306073f3748702","last_reissued_at":"2026-05-27T01:06:23.059890Z","signature_status":"signed_v1","first_computed_at":"2026-05-27T01:06:23.059890Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Constrained Bayesian Experimental Design via Online Planning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Ayush Bharti, Daolang Huang, Sammie Katt, Samuel Kaski, Xinyu Zhang, Yujia Guo","submitted_at":"2026-05-26T13:13:28Z","abstract_excerpt":"Bayesian experimental design (BED) is a principled framework for data-efficient design of sequential experiments. However, existing BED methods are unable to adapt to dynamic constraints inherent in real-world tasks due to budget limitations, varying costs, or physical constraints that restrict how designs evolve over time. In this paper, we introduce a novel approach to BED that enables constrained optimization of experimental designs by combining offline pre-training of an amortized policy and a posterior network with online multi-step lookahead planning using scenario trees. We empirically "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.26990","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/2605.26990/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":"2605.26990","created_at":"2026-05-27T01:06:23.059961+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.26990v1","created_at":"2026-05-27T01:06:23.059961+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.26990","created_at":"2026-05-27T01:06:23.059961+00:00"},{"alias_kind":"pith_short_12","alias_value":"JPUBRCN2S3GA","created_at":"2026-05-27T01:06:23.059961+00:00"},{"alias_kind":"pith_short_16","alias_value":"JPUBRCN2S3GAFB7W","created_at":"2026-05-27T01:06:23.059961+00:00"},{"alias_kind":"pith_short_8","alias_value":"JPUBRCN2","created_at":"2026-05-27T01:06:23.059961+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/JPUBRCN2S3GAFB7WC5ZYPWQRAT","json":"https://pith.science/pith/JPUBRCN2S3GAFB7WC5ZYPWQRAT.json","graph_json":"https://pith.science/api/pith-number/JPUBRCN2S3GAFB7WC5ZYPWQRAT/graph.json","events_json":"https://pith.science/api/pith-number/JPUBRCN2S3GAFB7WC5ZYPWQRAT/events.json","paper":"https://pith.science/paper/JPUBRCN2"},"agent_actions":{"view_html":"https://pith.science/pith/JPUBRCN2S3GAFB7WC5ZYPWQRAT","download_json":"https://pith.science/pith/JPUBRCN2S3GAFB7WC5ZYPWQRAT.json","view_paper":"https://pith.science/paper/JPUBRCN2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.26990&json=true","fetch_graph":"https://pith.science/api/pith-number/JPUBRCN2S3GAFB7WC5ZYPWQRAT/graph.json","fetch_events":"https://pith.science/api/pith-number/JPUBRCN2S3GAFB7WC5ZYPWQRAT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JPUBRCN2S3GAFB7WC5ZYPWQRAT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JPUBRCN2S3GAFB7WC5ZYPWQRAT/action/storage_attestation","attest_author":"https://pith.science/pith/JPUBRCN2S3GAFB7WC5ZYPWQRAT/action/author_attestation","sign_citation":"https://pith.science/pith/JPUBRCN2S3GAFB7WC5ZYPWQRAT/action/citation_signature","submit_replication":"https://pith.science/pith/JPUBRCN2S3GAFB7WC5ZYPWQRAT/action/replication_record"}},"created_at":"2026-05-27T01:06:23.059961+00:00","updated_at":"2026-05-27T01:06:23.059961+00:00"}