{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:E7VQMQ5KECMYAZ7RZT3PIREGQJ","short_pith_number":"pith:E7VQMQ5K","canonical_record":{"source":{"id":"2606.30230","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2026-06-29T12:43:21Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"74f098d416ed5e0e6d515be49290f55ec064fe4fb06366848390fb25c1658321","abstract_canon_sha256":"7778ac1c1497e3658f3a2d2a24d992906c2c6f97b9d1a2b2fa9bba998557d725"},"schema_version":"1.0"},"canonical_sha256":"27eb0643aa20998067f1ccf6f444868242e062e247088824f5e5feab8d7e295c","source":{"kind":"arxiv","id":"2606.30230","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.30230","created_at":"2026-06-30T02:17:55Z"},{"alias_kind":"arxiv_version","alias_value":"2606.30230v1","created_at":"2026-06-30T02:17:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.30230","created_at":"2026-06-30T02:17:55Z"},{"alias_kind":"pith_short_12","alias_value":"E7VQMQ5KECMY","created_at":"2026-06-30T02:17:55Z"},{"alias_kind":"pith_short_16","alias_value":"E7VQMQ5KECMYAZ7R","created_at":"2026-06-30T02:17:55Z"},{"alias_kind":"pith_short_8","alias_value":"E7VQMQ5K","created_at":"2026-06-30T02:17:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:E7VQMQ5KECMYAZ7RZT3PIREGQJ","target":"record","payload":{"canonical_record":{"source":{"id":"2606.30230","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2026-06-29T12:43:21Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"74f098d416ed5e0e6d515be49290f55ec064fe4fb06366848390fb25c1658321","abstract_canon_sha256":"7778ac1c1497e3658f3a2d2a24d992906c2c6f97b9d1a2b2fa9bba998557d725"},"schema_version":"1.0"},"canonical_sha256":"27eb0643aa20998067f1ccf6f444868242e062e247088824f5e5feab8d7e295c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T02:17:55.394943Z","signature_b64":"UZdDGzzE4iAy6ysvtD/BPrA0KgjM66Jv09UeYHg/94dMbOjT5OWZsj52EtpzaMr2YgXpg6+BELUsdujHjZS2Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"27eb0643aa20998067f1ccf6f444868242e062e247088824f5e5feab8d7e295c","last_reissued_at":"2026-06-30T02:17:55.394297Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T02:17:55.394297Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.30230","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-30T02:17:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"skzDPwTMkGHZKEbNGksU7w3Y5HV4+TPR7xYZuvXYslazGvftgw0cqm7Ff1Vrsjd3p3hDqKwt66VmwCW60v2LAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T18:20:52.701071Z"},"content_sha256":"3f5b9490ded3585580183b18974049025962046a043f74358dad90fbf560dc8a","schema_version":"1.0","event_id":"sha256:3f5b9490ded3585580183b18974049025962046a043f74358dad90fbf560dc8a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:E7VQMQ5KECMYAZ7RZT3PIREGQJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Distributionally Robust Framework for Learned Reconstructions in Inverse Problems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"math.OC","authors_text":"Christoph Brune, Floor van Maarschalkerwaart, Marcello Carioni, Subhadip Mukherjee","submitted_at":"2026-06-29T12:43:21Z","abstract_excerpt":"Learned reconstruction operators for inverse problems are typically trained under a fixed noise model, and generalize poorly when the distribution during testing differs from the one assumed during training. Distributionally robust optimization (DRO) addresses this by optimizing against the worst-case distribution within a prescribed ambiguity set, but standard Wasserstein DRO perturbs the full joint distribution uniformly, which can be overly conservative and ignores the physics of the measurement process. We develop a structured DRO framework in which the ambiguity set is restricted to struc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.30230","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/2606.30230/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-30T02:17:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7EUB8GhVusZeOfULxfyg8bQhgh/1aTdH8wvu01kH+gtFrFncDIX70PJU6kAtwJu1mAAGw4PdEC1uOEPo77seDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T18:20:52.701467Z"},"content_sha256":"3e73ce99882c149614a1f2d9878688bbb693fda399f5b77a31b9f207990ddd80","schema_version":"1.0","event_id":"sha256:3e73ce99882c149614a1f2d9878688bbb693fda399f5b77a31b9f207990ddd80"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/E7VQMQ5KECMYAZ7RZT3PIREGQJ/bundle.json","state_url":"https://pith.science/pith/E7VQMQ5KECMYAZ7RZT3PIREGQJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/E7VQMQ5KECMYAZ7RZT3PIREGQJ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-30T18:20:52Z","links":{"resolver":"https://pith.science/pith/E7VQMQ5KECMYAZ7RZT3PIREGQJ","bundle":"https://pith.science/pith/E7VQMQ5KECMYAZ7RZT3PIREGQJ/bundle.json","state":"https://pith.science/pith/E7VQMQ5KECMYAZ7RZT3PIREGQJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/E7VQMQ5KECMYAZ7RZT3PIREGQJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:E7VQMQ5KECMYAZ7RZT3PIREGQJ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"7778ac1c1497e3658f3a2d2a24d992906c2c6f97b9d1a2b2fa9bba998557d725","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2026-06-29T12:43:21Z","title_canon_sha256":"74f098d416ed5e0e6d515be49290f55ec064fe4fb06366848390fb25c1658321"},"schema_version":"1.0","source":{"id":"2606.30230","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.30230","created_at":"2026-06-30T02:17:55Z"},{"alias_kind":"arxiv_version","alias_value":"2606.30230v1","created_at":"2026-06-30T02:17:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.30230","created_at":"2026-06-30T02:17:55Z"},{"alias_kind":"pith_short_12","alias_value":"E7VQMQ5KECMY","created_at":"2026-06-30T02:17:55Z"},{"alias_kind":"pith_short_16","alias_value":"E7VQMQ5KECMYAZ7R","created_at":"2026-06-30T02:17:55Z"},{"alias_kind":"pith_short_8","alias_value":"E7VQMQ5K","created_at":"2026-06-30T02:17:55Z"}],"graph_snapshots":[{"event_id":"sha256:3e73ce99882c149614a1f2d9878688bbb693fda399f5b77a31b9f207990ddd80","target":"graph","created_at":"2026-06-30T02:17:55Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2606.30230/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Learned reconstruction operators for inverse problems are typically trained under a fixed noise model, and generalize poorly when the distribution during testing differs from the one assumed during training. Distributionally robust optimization (DRO) addresses this by optimizing against the worst-case distribution within a prescribed ambiguity set, but standard Wasserstein DRO perturbs the full joint distribution uniformly, which can be overly conservative and ignores the physics of the measurement process. We develop a structured DRO framework in which the ambiguity set is restricted to struc","authors_text":"Christoph Brune, Floor van Maarschalkerwaart, Marcello Carioni, Subhadip Mukherjee","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2026-06-29T12:43:21Z","title":"A Distributionally Robust Framework for Learned Reconstructions in Inverse Problems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.30230","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3f5b9490ded3585580183b18974049025962046a043f74358dad90fbf560dc8a","target":"record","created_at":"2026-06-30T02:17:55Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"7778ac1c1497e3658f3a2d2a24d992906c2c6f97b9d1a2b2fa9bba998557d725","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2026-06-29T12:43:21Z","title_canon_sha256":"74f098d416ed5e0e6d515be49290f55ec064fe4fb06366848390fb25c1658321"},"schema_version":"1.0","source":{"id":"2606.30230","kind":"arxiv","version":1}},"canonical_sha256":"27eb0643aa20998067f1ccf6f444868242e062e247088824f5e5feab8d7e295c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"27eb0643aa20998067f1ccf6f444868242e062e247088824f5e5feab8d7e295c","first_computed_at":"2026-06-30T02:17:55.394297Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-30T02:17:55.394297Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UZdDGzzE4iAy6ysvtD/BPrA0KgjM66Jv09UeYHg/94dMbOjT5OWZsj52EtpzaMr2YgXpg6+BELUsdujHjZS2Cw==","signature_status":"signed_v1","signed_at":"2026-06-30T02:17:55.394943Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.30230","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3f5b9490ded3585580183b18974049025962046a043f74358dad90fbf560dc8a","sha256:3e73ce99882c149614a1f2d9878688bbb693fda399f5b77a31b9f207990ddd80"],"state_sha256":"40b5be5c3a7b7d223767234b8bafa8fded292f0706daaee811871b3b63f6556a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"U+rMCKOxWcexIcouBtn9/9aYzEWZQvwnp4tqMPej31VFipCsqvUOuFijJV1O9dxDsdWv2wJFlp9htVWiXJVNDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-30T18:20:52.703476Z","bundle_sha256":"8676479912f84b088d55bf4178942a6616b2ad2cac643e0a76a5c563a9792be5"}}