{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:KA5T2CA3EAOY6ZZAG2562WOH2Z","short_pith_number":"pith:KA5T2CA3","canonical_record":{"source":{"id":"2604.22462","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"hep-ph","submitted_at":"2026-04-24T11:29:09Z","cross_cats_sorted":["astro-ph.CO","gr-qc"],"title_canon_sha256":"4e800ebdae2412215d344bf0e78ed73bf83bcab93b5c9668c47f18ebf47e9400","abstract_canon_sha256":"6b1ba26cab7554c7fb34fda977a56c8862339fb8ab10e04aab3508ef10fa8335"},"schema_version":"1.0"},"canonical_sha256":"503b3d081b201d8f672036bbed59c7d6784907d1242a3241923429aa1680703f","source":{"kind":"arxiv","id":"2604.22462","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.22462","created_at":"2026-07-02T00:18:29Z"},{"alias_kind":"arxiv_version","alias_value":"2604.22462v1","created_at":"2026-07-02T00:18:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.22462","created_at":"2026-07-02T00:18:29Z"},{"alias_kind":"pith_short_12","alias_value":"KA5T2CA3EAOY","created_at":"2026-07-02T00:18:29Z"},{"alias_kind":"pith_short_16","alias_value":"KA5T2CA3EAOY6ZZA","created_at":"2026-07-02T00:18:29Z"},{"alias_kind":"pith_short_8","alias_value":"KA5T2CA3","created_at":"2026-07-02T00:18:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:KA5T2CA3EAOY6ZZAG2562WOH2Z","target":"record","payload":{"canonical_record":{"source":{"id":"2604.22462","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"hep-ph","submitted_at":"2026-04-24T11:29:09Z","cross_cats_sorted":["astro-ph.CO","gr-qc"],"title_canon_sha256":"4e800ebdae2412215d344bf0e78ed73bf83bcab93b5c9668c47f18ebf47e9400","abstract_canon_sha256":"6b1ba26cab7554c7fb34fda977a56c8862339fb8ab10e04aab3508ef10fa8335"},"schema_version":"1.0"},"canonical_sha256":"503b3d081b201d8f672036bbed59c7d6784907d1242a3241923429aa1680703f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-02T00:18:29.084908Z","signature_b64":"y4l/EuNe/daEXDAxgySI/6Q2qvMNhxTzjWO1ASlAFZAbAZ8oapLrAJ5wNUp0H3A5SOElbzXrZ4Zf9abh+f7fBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"503b3d081b201d8f672036bbed59c7d6784907d1242a3241923429aa1680703f","last_reissued_at":"2026-07-02T00:18:29.083998Z","signature_status":"signed_v1","first_computed_at":"2026-07-02T00:18:29.083998Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2604.22462","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":"2026-07-02T00:18:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Vlz8xyclsUlduNm8+6yuwTEl64JBZdVceToX59V8KfKncMkfGepoxjWb2XwRLKLq+bsNx1D5pFSUmE/wwIzoDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T14:56:44.599040Z"},"content_sha256":"611b9b5bb865c3a33923ea01c4b25d9b55b8b3a49cb355f11e772c384b2b84fd","schema_version":"1.0","event_id":"sha256:611b9b5bb865c3a33923ea01c4b25d9b55b8b3a49cb355f11e772c384b2b84fd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:KA5T2CA3EAOY6ZZAG2562WOH2Z","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Machine Learning for Multi-messenger Probes of New Physics and Cosmology: A Review and Perspective","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Machine learning provides the key to integrating multi-messenger observations for probing dark matter and new physics.","cross_cats":["astro-ph.CO","gr-qc"],"primary_cat":"hep-ph","authors_text":"Andrea Addazi, Andrey Mayorov, Antonino Marciano, Antonio Morais, Artem Kharakhashyan, Atharv Mahajan, Danila Sopin, Deen Chen, Filippo Fabrocini, Jackson Levi Said, Konstantin Belotsky, Krid Jinklub, Maxim Khlopov, Maxim Krasnov, Oem Trivedi, Roman Pasechnik, Stefano Giagu, Timur Bikbaev, Viktor Stasenko, Vitaly Beylin, Vladimir Korchagin","submitted_at":"2026-04-24T11:29:09Z","abstract_excerpt":"The multi-messenger exploration of dark matter and physics beyond the Standard Model has emerged as a central direction in modern astro-particle physics, particularly following the discovery of gravitational waves. In this work, we present a comprehensive review and forward-looking perspective on machine-learning-enhanced multi-messenger approaches, combining information from gravitational waves, cosmic rays, gamma rays, neutrinos, and collider experiments. We summarize the current state of the field, discuss recent methodological developments, and outline a coherent research program aimed at "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We foresee that such a cross-fertilizing approach will represent the right path to extract information about the main questions left in fundamental physics.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That machine learning can integrate heterogeneous multi-messenger datasets into a unified inference framework that yields reliable new information on dark matter without introducing biases or losing critical details from individual messengers.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A review summarizing machine learning methods for multi-messenger probes of dark matter and new physics, with a proposed plan for future integrated analyses.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Machine learning provides the key to integrating multi-messenger observations for probing dark matter and new physics.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"11b37408a1cadc4b9fc75fec700d0d644b8176136b0d2f6e8bff778f1acc17c2"},"source":{"id":"2604.22462","kind":"arxiv","version":1},"verdict":{"id":"24533635-77ea-401c-9300-8c941d8936ba","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-08T10:59:13.374156Z","strongest_claim":"We foresee that such a cross-fertilizing approach will represent the right path to extract information about the main questions left in fundamental physics.","one_line_summary":"A review summarizing machine learning methods for multi-messenger probes of dark matter and new physics, with a proposed plan for future integrated analyses.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That machine learning can integrate heterogeneous multi-messenger datasets into a unified inference framework that yields reliable new information on dark matter without introducing biases or losing critical details from individual messengers.","pith_extraction_headline":"Machine learning provides the key to integrating multi-messenger observations for probing dark matter and new physics."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.22462/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-21T10:40:00.071742Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T23:56:51.048407Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"2ae21f84319271fc187880162eccbef899c4ac00f46a24a0fc49b5c4c4c4578c"},"references":{"count":293,"sample":[{"doi":"","year":null,"title":"In this approach, the final state is represented as a probability density function (PDF) describing the likelihood of finding a nucleon at a given point in phase space","work_id":"d7022fd9-c32b-49a7-982d-e7093f0d4e4e","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"A second major development is the deployment of machine learning algorithms at the trigger level, including im- plementations on FPGAs and heterogeneous hardware. At the LHC and future colliders, ML-e","work_id":"5bb56791-75c3-4719-bbec-fffd0683e7c3","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Another important class of applications concerns model-agnostic searches for new physics, where the goal is to detect deviations from the Standard Model without committing to specific signal hypothese","work_id":"dc1268e7-937d-482e-82c9-cf489bc3d326","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"A further application concerns the assessment of hadronic interaction models used in air-shower reconstruction from very-high-energy cosmic-ray observations, namely SIBYLL 2.3c [215], QGSJet II-04 [21","work_id":"0636ee5c-954b-4064-81f9-bc1505cfc2c0","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"hidden valley","work_id":"7cc5b4a6-90b6-497b-91f6-d44af30b60c7","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":293,"snapshot_sha256":"cbd35e57dde6412ac7e82f2af9b320214eab72097ba08be3c21e8be973cee99c","internal_anchors":14},"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":"24533635-77ea-401c-9300-8c941d8936ba"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-02T00:18:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NKn8MC2S48FPi5FmKlLtAtlAM0UPUWk2TGDR4W59BWe8Jq3uYdVbEQHoU5yNUoKCiN4VxrR+E06oJD6oFMiSDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T14:56:44.600514Z"},"content_sha256":"4848152b1616f5041854c3aabad3540e22959599cfa503966f4b869de8485c8d","schema_version":"1.0","event_id":"sha256:4848152b1616f5041854c3aabad3540e22959599cfa503966f4b869de8485c8d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KA5T2CA3EAOY6ZZAG2562WOH2Z/bundle.json","state_url":"https://pith.science/pith/KA5T2CA3EAOY6ZZAG2562WOH2Z/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KA5T2CA3EAOY6ZZAG2562WOH2Z/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":"2026-08-12T14:56:44Z","links":{"resolver":"https://pith.science/pith/KA5T2CA3EAOY6ZZAG2562WOH2Z","bundle":"https://pith.science/pith/KA5T2CA3EAOY6ZZAG2562WOH2Z/bundle.json","state":"https://pith.science/pith/KA5T2CA3EAOY6ZZAG2562WOH2Z/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KA5T2CA3EAOY6ZZAG2562WOH2Z/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:KA5T2CA3EAOY6ZZAG2562WOH2Z","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":"6b1ba26cab7554c7fb34fda977a56c8862339fb8ab10e04aab3508ef10fa8335","cross_cats_sorted":["astro-ph.CO","gr-qc"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"hep-ph","submitted_at":"2026-04-24T11:29:09Z","title_canon_sha256":"4e800ebdae2412215d344bf0e78ed73bf83bcab93b5c9668c47f18ebf47e9400"},"schema_version":"1.0","source":{"id":"2604.22462","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.22462","created_at":"2026-07-02T00:18:29Z"},{"alias_kind":"arxiv_version","alias_value":"2604.22462v1","created_at":"2026-07-02T00:18:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.22462","created_at":"2026-07-02T00:18:29Z"},{"alias_kind":"pith_short_12","alias_value":"KA5T2CA3EAOY","created_at":"2026-07-02T00:18:29Z"},{"alias_kind":"pith_short_16","alias_value":"KA5T2CA3EAOY6ZZA","created_at":"2026-07-02T00:18:29Z"},{"alias_kind":"pith_short_8","alias_value":"KA5T2CA3","created_at":"2026-07-02T00:18:29Z"}],"graph_snapshots":[{"event_id":"sha256:4848152b1616f5041854c3aabad3540e22959599cfa503966f4b869de8485c8d","target":"graph","created_at":"2026-07-02T00:18:29Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"We foresee that such a cross-fertilizing approach will represent the right path to extract information about the main questions left in fundamental physics."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That machine learning can integrate heterogeneous multi-messenger datasets into a unified inference framework that yields reliable new information on dark matter without introducing biases or losing critical details from individual messengers."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A review summarizing machine learning methods for multi-messenger probes of dark matter and new physics, with a proposed plan for future integrated analyses."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Machine learning provides the key to integrating multi-messenger observations for probing dark matter and new physics."}],"snapshot_sha256":"11b37408a1cadc4b9fc75fec700d0d644b8176136b0d2f6e8bff778f1acc17c2"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-21T10:40:00.071742Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T23:56:51.048407Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2604.22462/integrity.json","findings":[],"snapshot_sha256":"2ae21f84319271fc187880162eccbef899c4ac00f46a24a0fc49b5c4c4c4578c","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The multi-messenger exploration of dark matter and physics beyond the Standard Model has emerged as a central direction in modern astro-particle physics, particularly following the discovery of gravitational waves. In this work, we present a comprehensive review and forward-looking perspective on machine-learning-enhanced multi-messenger approaches, combining information from gravitational waves, cosmic rays, gamma rays, neutrinos, and collider experiments. We summarize the current state of the field, discuss recent methodological developments, and outline a coherent research program aimed at ","authors_text":"Andrea Addazi, Andrey Mayorov, Antonino Marciano, Antonio Morais, Artem Kharakhashyan, Atharv Mahajan, Danila Sopin, Deen Chen, Filippo Fabrocini, Jackson Levi Said, Konstantin Belotsky, Krid Jinklub, Maxim Khlopov, Maxim Krasnov, Oem Trivedi, Roman Pasechnik, Stefano Giagu, Timur Bikbaev, Viktor Stasenko, Vitaly Beylin, Vladimir Korchagin","cross_cats":["astro-ph.CO","gr-qc"],"headline":"Machine learning provides the key to integrating multi-messenger observations for probing dark matter and new physics.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"hep-ph","submitted_at":"2026-04-24T11:29:09Z","title":"Machine Learning for Multi-messenger Probes of New Physics and Cosmology: A Review and Perspective"},"references":{"count":293,"internal_anchors":14,"resolved_work":293,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"In this approach, the final state is represented as a probability density function (PDF) describing the likelihood of finding a nucleon at a given point in phase space","work_id":"d7022fd9-c32b-49a7-982d-e7093f0d4e4e","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"A second major development is the deployment of machine learning algorithms at the trigger level, including im- plementations on FPGAs and heterogeneous hardware. At the LHC and future colliders, ML-e","work_id":"5bb56791-75c3-4719-bbec-fffd0683e7c3","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Another important class of applications concerns model-agnostic searches for new physics, where the goal is to detect deviations from the Standard Model without committing to specific signal hypothese","work_id":"dc1268e7-937d-482e-82c9-cf489bc3d326","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"A further application concerns the assessment of hadronic interaction models used in air-shower reconstruction from very-high-energy cosmic-ray observations, namely SIBYLL 2.3c [215], QGSJet II-04 [21","work_id":"0636ee5c-954b-4064-81f9-bc1505cfc2c0","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"hidden valley","work_id":"7cc5b4a6-90b6-497b-91f6-d44af30b60c7","year":null}],"snapshot_sha256":"cbd35e57dde6412ac7e82f2af9b320214eab72097ba08be3c21e8be973cee99c"},"source":{"id":"2604.22462","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-08T10:59:13.374156Z","id":"24533635-77ea-401c-9300-8c941d8936ba","model_set":{"reader":"grok-4.3"},"one_line_summary":"A review summarizing machine learning methods for multi-messenger probes of dark matter and new physics, with a proposed plan for future integrated analyses.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Machine learning provides the key to integrating multi-messenger observations for probing dark matter and new physics.","strongest_claim":"We foresee that such a cross-fertilizing approach will represent the right path to extract information about the main questions left in fundamental physics.","weakest_assumption":"That machine learning can integrate heterogeneous multi-messenger datasets into a unified inference framework that yields reliable new information on dark matter without introducing biases or losing critical details from individual messengers."}},"verdict_id":"24533635-77ea-401c-9300-8c941d8936ba"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:611b9b5bb865c3a33923ea01c4b25d9b55b8b3a49cb355f11e772c384b2b84fd","target":"record","created_at":"2026-07-02T00:18:29Z","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":"6b1ba26cab7554c7fb34fda977a56c8862339fb8ab10e04aab3508ef10fa8335","cross_cats_sorted":["astro-ph.CO","gr-qc"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"hep-ph","submitted_at":"2026-04-24T11:29:09Z","title_canon_sha256":"4e800ebdae2412215d344bf0e78ed73bf83bcab93b5c9668c47f18ebf47e9400"},"schema_version":"1.0","source":{"id":"2604.22462","kind":"arxiv","version":1}},"canonical_sha256":"503b3d081b201d8f672036bbed59c7d6784907d1242a3241923429aa1680703f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"503b3d081b201d8f672036bbed59c7d6784907d1242a3241923429aa1680703f","first_computed_at":"2026-07-02T00:18:29.083998Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-02T00:18:29.083998Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"y4l/EuNe/daEXDAxgySI/6Q2qvMNhxTzjWO1ASlAFZAbAZ8oapLrAJ5wNUp0H3A5SOElbzXrZ4Zf9abh+f7fBQ==","signature_status":"signed_v1","signed_at":"2026-07-02T00:18:29.084908Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.22462","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:611b9b5bb865c3a33923ea01c4b25d9b55b8b3a49cb355f11e772c384b2b84fd","sha256:4848152b1616f5041854c3aabad3540e22959599cfa503966f4b869de8485c8d"],"state_sha256":"ff4c411c985278d317de174f5e62129f7c7d453cdb6e37a743181911b9febcc4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"f4WzhQooQMoGDjKlVCWQxT/V8Lo1Nwmm/N75tk3BJmW0SfNgIkEqo7MXNBmgyKEP2Wh7PqxvGvbdrJZk6yWZAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T14:56:44.608452Z","bundle_sha256":"4a61254c83c50ca7a352e28f03ec9fbe88e708f582735606b360f8517eae0f9e"}}