{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:G7JMC3BOPGK4HWI7S7DXT22QRJ","short_pith_number":"pith:G7JMC3BO","canonical_record":{"source":{"id":"2601.02998","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-01-06T13:22:13Z","cross_cats_sorted":["stat.ME","stat.ML"],"title_canon_sha256":"b40bdad9db35f34374e0bfd3a4e98e86686970ce242dc6efbe0e1d0c959920ca","abstract_canon_sha256":"1488309efeeb4c8d20cf4377cc2a7a3860598bbca2b6818f0a83d1178ef2faec"},"schema_version":"1.0"},"canonical_sha256":"37d2c16c2e7995c3d91f97c779eb508a6d3cd8e619929d673591074e5ca9a428","source":{"kind":"arxiv","id":"2601.02998","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2601.02998","created_at":"2026-07-10T01:19:41Z"},{"alias_kind":"arxiv_version","alias_value":"2601.02998v2","created_at":"2026-07-10T01:19:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.02998","created_at":"2026-07-10T01:19:41Z"},{"alias_kind":"pith_short_12","alias_value":"G7JMC3BOPGK4","created_at":"2026-07-10T01:19:41Z"},{"alias_kind":"pith_short_16","alias_value":"G7JMC3BOPGK4HWI7","created_at":"2026-07-10T01:19:41Z"},{"alias_kind":"pith_short_8","alias_value":"G7JMC3BO","created_at":"2026-07-10T01:19:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:G7JMC3BOPGK4HWI7S7DXT22QRJ","target":"record","payload":{"canonical_record":{"source":{"id":"2601.02998","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-01-06T13:22:13Z","cross_cats_sorted":["stat.ME","stat.ML"],"title_canon_sha256":"b40bdad9db35f34374e0bfd3a4e98e86686970ce242dc6efbe0e1d0c959920ca","abstract_canon_sha256":"1488309efeeb4c8d20cf4377cc2a7a3860598bbca2b6818f0a83d1178ef2faec"},"schema_version":"1.0"},"canonical_sha256":"37d2c16c2e7995c3d91f97c779eb508a6d3cd8e619929d673591074e5ca9a428","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-10T01:19:41.398542Z","signature_b64":"97TLLDw2bXY5fRIdX9fyKc0d0rEU9t7oo5bXYeZKP37m1IwVF+Pk6B91MIWn2jk7J3LCRAO04uDrRwfwqmEyDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"37d2c16c2e7995c3d91f97c779eb508a6d3cd8e619929d673591074e5ca9a428","last_reissued_at":"2026-07-10T01:19:41.398067Z","signature_status":"signed_v1","first_computed_at":"2026-07-10T01:19:41.398067Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2601.02998","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-10T01:19:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"I5ngLec3yXTkKE58d7hOTvuI9GoouWrrFvSClWQ9toE/8j/DvFR4C2iai539HTXDaNhdv5iyjQmWeIDnmv1gCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T08:15:48.778404Z"},"content_sha256":"e43683c3ef1043c69730459fe04611c515ac4b8d9c4f7fb1f5c4bc45a6e8ed14","schema_version":"1.0","event_id":"sha256:e43683c3ef1043c69730459fe04611c515ac4b8d9c4f7fb1f5c4bc45a6e8ed14"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:G7JMC3BOPGK4HWI7S7DXT22QRJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-Distribution Robust Conformal Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ME","stat.ML"],"primary_cat":"cs.LG","authors_text":"Ying Jin, Yuqi Yang","submitted_at":"2026-01-06T13:22:13Z","abstract_excerpt":"In many fairness and distribution robustness problems, one has access to labeled data from multiple source distributions yet the test data may come from an arbitrary member or a mixture of them. We study the problem of constructing a conformal prediction set that is uniformly valid across multiple, heterogeneous distributions, in the sense that no matter which distribution the test point is from, the coverage of the prediction set is guaranteed to exceed a pre-specified level. We first propose a max-p aggregation scheme that delivers finite-sample, multi-distribution coverage given any conform"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.02998","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/2601.02998/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":"2026-07-10T01:19:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PZMk4xU/FKgZ+9v2qHBZ9a5hf3qYO53urPlsisvULHiXGskQbuNX9DFFpMEHygSUb7/qL1cG54avy+yrRVspBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T08:15:48.778930Z"},"content_sha256":"c954f192b74c3f6d1d93b7bb092c6c4030025e014cf9914f08b6b5f386488c02","schema_version":"1.0","event_id":"sha256:c954f192b74c3f6d1d93b7bb092c6c4030025e014cf9914f08b6b5f386488c02"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/G7JMC3BOPGK4HWI7S7DXT22QRJ/bundle.json","state_url":"https://pith.science/pith/G7JMC3BOPGK4HWI7S7DXT22QRJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/G7JMC3BOPGK4HWI7S7DXT22QRJ/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":"2026-08-09T08:15:48Z","links":{"resolver":"https://pith.science/pith/G7JMC3BOPGK4HWI7S7DXT22QRJ","bundle":"https://pith.science/pith/G7JMC3BOPGK4HWI7S7DXT22QRJ/bundle.json","state":"https://pith.science/pith/G7JMC3BOPGK4HWI7S7DXT22QRJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/G7JMC3BOPGK4HWI7S7DXT22QRJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:G7JMC3BOPGK4HWI7S7DXT22QRJ","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":"1488309efeeb4c8d20cf4377cc2a7a3860598bbca2b6818f0a83d1178ef2faec","cross_cats_sorted":["stat.ME","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-01-06T13:22:13Z","title_canon_sha256":"b40bdad9db35f34374e0bfd3a4e98e86686970ce242dc6efbe0e1d0c959920ca"},"schema_version":"1.0","source":{"id":"2601.02998","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2601.02998","created_at":"2026-07-10T01:19:41Z"},{"alias_kind":"arxiv_version","alias_value":"2601.02998v2","created_at":"2026-07-10T01:19:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.02998","created_at":"2026-07-10T01:19:41Z"},{"alias_kind":"pith_short_12","alias_value":"G7JMC3BOPGK4","created_at":"2026-07-10T01:19:41Z"},{"alias_kind":"pith_short_16","alias_value":"G7JMC3BOPGK4HWI7","created_at":"2026-07-10T01:19:41Z"},{"alias_kind":"pith_short_8","alias_value":"G7JMC3BO","created_at":"2026-07-10T01:19:41Z"}],"graph_snapshots":[{"event_id":"sha256:c954f192b74c3f6d1d93b7bb092c6c4030025e014cf9914f08b6b5f386488c02","target":"graph","created_at":"2026-07-10T01:19:41Z","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/2601.02998/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In many fairness and distribution robustness problems, one has access to labeled data from multiple source distributions yet the test data may come from an arbitrary member or a mixture of them. We study the problem of constructing a conformal prediction set that is uniformly valid across multiple, heterogeneous distributions, in the sense that no matter which distribution the test point is from, the coverage of the prediction set is guaranteed to exceed a pre-specified level. We first propose a max-p aggregation scheme that delivers finite-sample, multi-distribution coverage given any conform","authors_text":"Ying Jin, Yuqi Yang","cross_cats":["stat.ME","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-01-06T13:22:13Z","title":"Multi-Distribution Robust Conformal Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.02998","kind":"arxiv","version":2},"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:e43683c3ef1043c69730459fe04611c515ac4b8d9c4f7fb1f5c4bc45a6e8ed14","target":"record","created_at":"2026-07-10T01:19:41Z","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":"1488309efeeb4c8d20cf4377cc2a7a3860598bbca2b6818f0a83d1178ef2faec","cross_cats_sorted":["stat.ME","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-01-06T13:22:13Z","title_canon_sha256":"b40bdad9db35f34374e0bfd3a4e98e86686970ce242dc6efbe0e1d0c959920ca"},"schema_version":"1.0","source":{"id":"2601.02998","kind":"arxiv","version":2}},"canonical_sha256":"37d2c16c2e7995c3d91f97c779eb508a6d3cd8e619929d673591074e5ca9a428","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"37d2c16c2e7995c3d91f97c779eb508a6d3cd8e619929d673591074e5ca9a428","first_computed_at":"2026-07-10T01:19:41.398067Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-10T01:19:41.398067Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"97TLLDw2bXY5fRIdX9fyKc0d0rEU9t7oo5bXYeZKP37m1IwVF+Pk6B91MIWn2jk7J3LCRAO04uDrRwfwqmEyDw==","signature_status":"signed_v1","signed_at":"2026-07-10T01:19:41.398542Z","signed_message":"canonical_sha256_bytes"},"source_id":"2601.02998","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e43683c3ef1043c69730459fe04611c515ac4b8d9c4f7fb1f5c4bc45a6e8ed14","sha256:c954f192b74c3f6d1d93b7bb092c6c4030025e014cf9914f08b6b5f386488c02"],"state_sha256":"3ed8acb3db695fd883eeaa863af1ac4f4b492b23567d113931aa4104dd51bfb7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ygs1mm6AVW7I5IzyGNNrcUEyI6UH2EK32MRnFAFcN2gwAsDvrKOwdlw5bAJkX6whXZkqKEFvQG1yeXueVvcyDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T08:15:48.783094Z","bundle_sha256":"2fd3f5092d4befca76c9d2cf2b95e235f55d5ebda7bc89b690b0907b0f419695"}}