{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:L3D3E6TOSRQPTBJAZQK5YNXOXP","short_pith_number":"pith:L3D3E6TO","canonical_record":{"source":{"id":"2602.19126","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-22T10:50:04Z","cross_cats_sorted":["math.PR","math.ST","stat.TH"],"title_canon_sha256":"adaafdb4d8f1ac1311a6b625cd40d4c48b510148b7e0f54138e4b74cd9882e28","abstract_canon_sha256":"0641a13e3648bc5386fd51366fefacfd4695f8ffd519898318896668d735e6fb"},"schema_version":"1.0"},"canonical_sha256":"5ec7b27a6e9460f98520cc15dc36eebbe62ee3cafefeafe166406a485ee38d0d","source":{"kind":"arxiv","id":"2602.19126","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.19126","created_at":"2026-06-02T03:05:05Z"},{"alias_kind":"arxiv_version","alias_value":"2602.19126v2","created_at":"2026-06-02T03:05:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.19126","created_at":"2026-06-02T03:05:05Z"},{"alias_kind":"pith_short_12","alias_value":"L3D3E6TOSRQP","created_at":"2026-06-02T03:05:05Z"},{"alias_kind":"pith_short_16","alias_value":"L3D3E6TOSRQPTBJA","created_at":"2026-06-02T03:05:05Z"},{"alias_kind":"pith_short_8","alias_value":"L3D3E6TO","created_at":"2026-06-02T03:05:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:L3D3E6TOSRQPTBJAZQK5YNXOXP","target":"record","payload":{"canonical_record":{"source":{"id":"2602.19126","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-22T10:50:04Z","cross_cats_sorted":["math.PR","math.ST","stat.TH"],"title_canon_sha256":"adaafdb4d8f1ac1311a6b625cd40d4c48b510148b7e0f54138e4b74cd9882e28","abstract_canon_sha256":"0641a13e3648bc5386fd51366fefacfd4695f8ffd519898318896668d735e6fb"},"schema_version":"1.0"},"canonical_sha256":"5ec7b27a6e9460f98520cc15dc36eebbe62ee3cafefeafe166406a485ee38d0d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T03:05:05.005129Z","signature_b64":"aDEIx4ObmAZmgj0xeZgoTNSB62G6C0ZzEWfQMiEUsaU6/PX1VOjngFX8LkaXfx+CNBS5e47M0NBOSNoiVUuLDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ec7b27a6e9460f98520cc15dc36eebbe62ee3cafefeafe166406a485ee38d0d","last_reissued_at":"2026-06-02T03:05:05.004612Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T03:05:05.004612Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2602.19126","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":"2026-06-02T03:05:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fKf6tRTvdiUEbwTB6K8NBMrTmVxWtHBeLVnmhxUltizrLkbPMKtEVgOs5+l2LFJr0oe8YNNPyImHRaCXcka2Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-24T00:34:42.959365Z"},"content_sha256":"09fdd5557bc049d023ae88ec0259552fe9d50025c350f6edbe712899b8056050","schema_version":"1.0","event_id":"sha256:09fdd5557bc049d023ae88ec0259552fe9d50025c350f6edbe712899b8056050"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:L3D3E6TOSRQPTBJAZQK5YNXOXP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Robust Predictive Uncertainty and Double Descent in Contaminated Bayesian Random Features","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.PR","math.ST","stat.TH"],"primary_cat":"cs.LG","authors_text":"Katerina Papagiannouli, Michele Caprio, Sayan Mukherjee, Siu Lun Chau","submitted_at":"2026-02-22T10:50:04Z","abstract_excerpt":"We propose a robust Bayesian formulation of random feature (RF) regression that accounts explicitly for prior and likelihood misspecification via Huber-style contamination sets. Starting from the classical equivalence between ridge-regularized RF training and Bayesian inference with Gaussian priors and likelihoods, we replace the single prior and likelihood with $\\epsilon$- and $\\eta$-contaminated credal sets, respectively, and perform inference using pessimistic generalized Bayesian updating. We derive explicit and tractable bounds for the resulting lower and upper posterior predictive densit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.19126","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/2602.19126/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":"2026-06-02T03:05:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0/dqqcz7f/qI7VlAHgL/NlHmI5H99nUKdgXdyRRGIhCL//puRvwCve4hg3OanJpu9wjnEBnX+oH45imzG9trCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-24T00:34:42.959740Z"},"content_sha256":"e8eabe2dc92fbb0bd0d5f4d3acc142e1cbe0c9e028097b788914eed27e00d3ef","schema_version":"1.0","event_id":"sha256:e8eabe2dc92fbb0bd0d5f4d3acc142e1cbe0c9e028097b788914eed27e00d3ef"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/L3D3E6TOSRQPTBJAZQK5YNXOXP/bundle.json","state_url":"https://pith.science/pith/L3D3E6TOSRQPTBJAZQK5YNXOXP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/L3D3E6TOSRQPTBJAZQK5YNXOXP/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":"2026-06-24T00:34:42Z","links":{"resolver":"https://pith.science/pith/L3D3E6TOSRQPTBJAZQK5YNXOXP","bundle":"https://pith.science/pith/L3D3E6TOSRQPTBJAZQK5YNXOXP/bundle.json","state":"https://pith.science/pith/L3D3E6TOSRQPTBJAZQK5YNXOXP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/L3D3E6TOSRQPTBJAZQK5YNXOXP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:L3D3E6TOSRQPTBJAZQK5YNXOXP","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":"0641a13e3648bc5386fd51366fefacfd4695f8ffd519898318896668d735e6fb","cross_cats_sorted":["math.PR","math.ST","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-22T10:50:04Z","title_canon_sha256":"adaafdb4d8f1ac1311a6b625cd40d4c48b510148b7e0f54138e4b74cd9882e28"},"schema_version":"1.0","source":{"id":"2602.19126","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.19126","created_at":"2026-06-02T03:05:05Z"},{"alias_kind":"arxiv_version","alias_value":"2602.19126v2","created_at":"2026-06-02T03:05:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.19126","created_at":"2026-06-02T03:05:05Z"},{"alias_kind":"pith_short_12","alias_value":"L3D3E6TOSRQP","created_at":"2026-06-02T03:05:05Z"},{"alias_kind":"pith_short_16","alias_value":"L3D3E6TOSRQPTBJA","created_at":"2026-06-02T03:05:05Z"},{"alias_kind":"pith_short_8","alias_value":"L3D3E6TO","created_at":"2026-06-02T03:05:05Z"}],"graph_snapshots":[{"event_id":"sha256:e8eabe2dc92fbb0bd0d5f4d3acc142e1cbe0c9e028097b788914eed27e00d3ef","target":"graph","created_at":"2026-06-02T03:05:05Z","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/2602.19126/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose a robust Bayesian formulation of random feature (RF) regression that accounts explicitly for prior and likelihood misspecification via Huber-style contamination sets. Starting from the classical equivalence between ridge-regularized RF training and Bayesian inference with Gaussian priors and likelihoods, we replace the single prior and likelihood with $\\epsilon$- and $\\eta$-contaminated credal sets, respectively, and perform inference using pessimistic generalized Bayesian updating. We derive explicit and tractable bounds for the resulting lower and upper posterior predictive densit","authors_text":"Katerina Papagiannouli, Michele Caprio, Sayan Mukherjee, Siu Lun Chau","cross_cats":["math.PR","math.ST","stat.TH"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-22T10:50:04Z","title":"Robust Predictive Uncertainty and Double Descent in Contaminated Bayesian Random Features"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.19126","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":"sha256:09fdd5557bc049d023ae88ec0259552fe9d50025c350f6edbe712899b8056050","target":"record","created_at":"2026-06-02T03:05:05Z","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":"0641a13e3648bc5386fd51366fefacfd4695f8ffd519898318896668d735e6fb","cross_cats_sorted":["math.PR","math.ST","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-22T10:50:04Z","title_canon_sha256":"adaafdb4d8f1ac1311a6b625cd40d4c48b510148b7e0f54138e4b74cd9882e28"},"schema_version":"1.0","source":{"id":"2602.19126","kind":"arxiv","version":2}},"canonical_sha256":"5ec7b27a6e9460f98520cc15dc36eebbe62ee3cafefeafe166406a485ee38d0d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5ec7b27a6e9460f98520cc15dc36eebbe62ee3cafefeafe166406a485ee38d0d","first_computed_at":"2026-06-02T03:05:05.004612Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-02T03:05:05.004612Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"aDEIx4ObmAZmgj0xeZgoTNSB62G6C0ZzEWfQMiEUsaU6/PX1VOjngFX8LkaXfx+CNBS5e47M0NBOSNoiVUuLDA==","signature_status":"signed_v1","signed_at":"2026-06-02T03:05:05.005129Z","signed_message":"canonical_sha256_bytes"},"source_id":"2602.19126","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:09fdd5557bc049d023ae88ec0259552fe9d50025c350f6edbe712899b8056050","sha256:e8eabe2dc92fbb0bd0d5f4d3acc142e1cbe0c9e028097b788914eed27e00d3ef"],"state_sha256":"6e878e91b47e7ac7e12e069984452cbc4e21452cdcae41362866e19732023428"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kosNyrcAEv56tN61u0S4qKTUurGAcjonR2oDzz3PotjmRNubxC4M71DnOjlaAMTiyJXlYRfMDpGMcljuOK5SCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-24T00:34:42.961623Z","bundle_sha256":"0b45c27925f631e4cecc42492adfe9d1785a3de45c46ffe298ef24d213bcaeaa"}}