{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:JTJYE2J7GOWFWOX2QGPE3A4FL5","short_pith_number":"pith:JTJYE2J7","canonical_record":{"source":{"id":"2605.17406","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2026-05-17T12:03:45Z","cross_cats_sorted":[],"title_canon_sha256":"f4d14b50bfb517b3168c13cec698d03c380811cf06251d4ff587adc30bac4f47","abstract_canon_sha256":"c98622e9feb154bfbb11a7a97a810eb90cb2887924dd1555c59fc79b857d4fed"},"schema_version":"1.0"},"canonical_sha256":"4cd382693f33ac5b3afa819e4d83855f66457d7e293fee7b4a4e733a41b623c7","source":{"kind":"arxiv","id":"2605.17406","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.17406","created_at":"2026-05-20T00:03:56Z"},{"alias_kind":"arxiv_version","alias_value":"2605.17406v1","created_at":"2026-05-20T00:03:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17406","created_at":"2026-05-20T00:03:56Z"},{"alias_kind":"pith_short_12","alias_value":"JTJYE2J7GOWF","created_at":"2026-05-20T00:03:56Z"},{"alias_kind":"pith_short_16","alias_value":"JTJYE2J7GOWFWOX2","created_at":"2026-05-20T00:03:56Z"},{"alias_kind":"pith_short_8","alias_value":"JTJYE2J7","created_at":"2026-05-20T00:03:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:JTJYE2J7GOWFWOX2QGPE3A4FL5","target":"record","payload":{"canonical_record":{"source":{"id":"2605.17406","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2026-05-17T12:03:45Z","cross_cats_sorted":[],"title_canon_sha256":"f4d14b50bfb517b3168c13cec698d03c380811cf06251d4ff587adc30bac4f47","abstract_canon_sha256":"c98622e9feb154bfbb11a7a97a810eb90cb2887924dd1555c59fc79b857d4fed"},"schema_version":"1.0"},"canonical_sha256":"4cd382693f33ac5b3afa819e4d83855f66457d7e293fee7b4a4e733a41b623c7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:03:56.859441Z","signature_b64":"HLNAF9IHGca21vvSFYVN8lekqM4Iw9t+Slxk/EsMnqXcSc9qXUXXlHEmOlISWv/DXpH2AZeWCiGAIt5+ywVBCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4cd382693f33ac5b3afa819e4d83855f66457d7e293fee7b4a4e733a41b623c7","last_reissued_at":"2026-05-20T00:03:56.857799Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:03:56.857799Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.17406","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":"2026-05-20T00:03:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CO8c5cZpVpBcWVO7tAIZuwI1EhXeUSK89CfNcFLcAQqcfyhdo3qnVtZS/LiMFNrlAedn+ZjTzd6LfPejUR3vDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T07:57:46.980473Z"},"content_sha256":"cd8d08082cc6c104d6eefc18aa42a0b2aee9c3c4e8c27dfcee7f92a70c0037bd","schema_version":"1.0","event_id":"sha256:cd8d08082cc6c104d6eefc18aa42a0b2aee9c3c4e8c27dfcee7f92a70c0037bd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:JTJYE2J7GOWFWOX2QGPE3A4FL5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Rethinking Side-Channel Analysis: Automated Discovery and Analysis of Side-Channel Leakage with LLM-Assisted Agents","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"SCAgent automates discovery of side-channel leaks by using LLM agents to explore sensitive events and verify channels without manual targets or large datasets.","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Xiaofeng Wang, Yuhua Sun, Zhen Xu, Zihao Wang","submitted_at":"2026-05-17T12:03:45Z","abstract_excerpt":"Side-channel attacks exploit unintended information leakage from system behavior and continue to pose serious privacy risks in modern platforms. Despite extensive prior work, side-channel analysis remains largely manual and fragmented, typically assuming predefined target events and a fixed set of known channels. As systems and applications grow increasingly complex, several fundamental questions remain unanswered: which user or system events are sensitive in practice, how side channels associated with these events can be systematically discovered without exhaustive manual effort, and how thei"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"SCAgent performs agent-driven system exploration guided by LLM-based semantic reasoning to identify sensitive targets beyond manually specified events, reasons over system documentation with explicit verification, and enables scalable analysis under limited data via few-shot learning with a time-shift-robust feature extraction layer.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The assumption that LLM semantic reasoning combined with explicit verification steps over system documentation can reliably identify feasible side channels and mitigate hallucination risks sufficiently for practical use, as stated in the description of target identification and channel discovery.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"SCAgent automates side-channel leakage discovery via LLM agents for target identification and few-shot foundation models for scalable analysis on iOS.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"SCAgent automates discovery of side-channel leaks by using LLM agents to explore sensitive events and verify channels without manual targets or large datasets.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"b354a9bd0ead3ab684c26dcd19070edbe00a5a9e2cdf0238930038f8da0a8343"},"source":{"id":"2605.17406","kind":"arxiv","version":1},"verdict":{"id":"7ac8c288-c135-48b6-94b2-7de51011dd31","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T23:31:30.169368Z","strongest_claim":"SCAgent performs agent-driven system exploration guided by LLM-based semantic reasoning to identify sensitive targets beyond manually specified events, reasons over system documentation with explicit verification, and enables scalable analysis under limited data via few-shot learning with a time-shift-robust feature extraction layer.","one_line_summary":"SCAgent automates side-channel leakage discovery via LLM agents for target identification and few-shot foundation models for scalable analysis on iOS.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The assumption that LLM semantic reasoning combined with explicit verification steps over system documentation can reliably identify feasible side channels and mitigate hallucination risks sufficiently for practical use, as stated in the description of target identification and channel discovery.","pith_extraction_headline":"SCAgent automates discovery of side-channel leaks by using LLM agents to explore sensitive events and verify channels without manual targets or large datasets."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.17406/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-20T00:01:20.572815Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T23:41:09.774484Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T21:41:57.749774Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T21:33:23.693196Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"11c82fdfe5b4f247de2b7077165f9ac648f01db5b18a9f330cd9c632bf592588"},"references":{"count":67,"sample":[{"doi":"","year":2014,"title":"Peeking into your app without actually seeing it: UI state inference and novel android attacks,","work_id":"b6fc4108-ef1e-473d-bc1d-40ba7571fa12","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2012,"title":"Memento: Learning secrets from process footprints,","work_id":"1e7230f3-feb7-4918-ba32-489b2247d6e1","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2014,"title":"Screenmilker: How to milk your android screen for secrets,","work_id":"bc590658-a246-499f-9739-870b6b228b9b","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2013,"title":"Identity, location, disease and more: inferring your secrets from android public resources,","work_id":"fd6c7143-e7ff-4e79-be6d-6ad6f94506a2","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2018,"title":"Os-level side channels without procfs: Exploring cross-app information leakage on ios,","work_id":"662f7812-f3b3-45e7-acca-193d484cb79c","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":67,"snapshot_sha256":"78748d7c811efa14eaba081867d69c4864bdb4afdb45b970855725e5ef0f6f7f","internal_anchors":2},"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":"7ac8c288-c135-48b6-94b2-7de51011dd31"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:03:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EpLWQ54lvG5KIEYtnjSteC9OZ4m7ZMONF4+m3yaj2+HCpRHeljTcs/ncNvyS8xTQAu4QM5l1P41KMCTPEqVmDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T07:57:46.981594Z"},"content_sha256":"4250e45865b04069b0e76e7bcf02209b900e568c109b1dd90dd822ec35126814","schema_version":"1.0","event_id":"sha256:4250e45865b04069b0e76e7bcf02209b900e568c109b1dd90dd822ec35126814"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JTJYE2J7GOWFWOX2QGPE3A4FL5/bundle.json","state_url":"https://pith.science/pith/JTJYE2J7GOWFWOX2QGPE3A4FL5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JTJYE2J7GOWFWOX2QGPE3A4FL5/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":"2026-05-31T07:57:46Z","links":{"resolver":"https://pith.science/pith/JTJYE2J7GOWFWOX2QGPE3A4FL5","bundle":"https://pith.science/pith/JTJYE2J7GOWFWOX2QGPE3A4FL5/bundle.json","state":"https://pith.science/pith/JTJYE2J7GOWFWOX2QGPE3A4FL5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JTJYE2J7GOWFWOX2QGPE3A4FL5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:JTJYE2J7GOWFWOX2QGPE3A4FL5","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":"c98622e9feb154bfbb11a7a97a810eb90cb2887924dd1555c59fc79b857d4fed","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2026-05-17T12:03:45Z","title_canon_sha256":"f4d14b50bfb517b3168c13cec698d03c380811cf06251d4ff587adc30bac4f47"},"schema_version":"1.0","source":{"id":"2605.17406","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.17406","created_at":"2026-05-20T00:03:56Z"},{"alias_kind":"arxiv_version","alias_value":"2605.17406v1","created_at":"2026-05-20T00:03:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17406","created_at":"2026-05-20T00:03:56Z"},{"alias_kind":"pith_short_12","alias_value":"JTJYE2J7GOWF","created_at":"2026-05-20T00:03:56Z"},{"alias_kind":"pith_short_16","alias_value":"JTJYE2J7GOWFWOX2","created_at":"2026-05-20T00:03:56Z"},{"alias_kind":"pith_short_8","alias_value":"JTJYE2J7","created_at":"2026-05-20T00:03:56Z"}],"graph_snapshots":[{"event_id":"sha256:4250e45865b04069b0e76e7bcf02209b900e568c109b1dd90dd822ec35126814","target":"graph","created_at":"2026-05-20T00:03:56Z","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":"SCAgent performs agent-driven system exploration guided by LLM-based semantic reasoning to identify sensitive targets beyond manually specified events, reasons over system documentation with explicit verification, and enables scalable analysis under limited data via few-shot learning with a time-shift-robust feature extraction layer."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The assumption that LLM semantic reasoning combined with explicit verification steps over system documentation can reliably identify feasible side channels and mitigate hallucination risks sufficiently for practical use, as stated in the description of target identification and channel discovery."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"SCAgent automates side-channel leakage discovery via LLM agents for target identification and few-shot foundation models for scalable analysis on iOS."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"SCAgent automates discovery of side-channel leaks by using LLM agents to explore sensitive events and verify channels without manual targets or large datasets."}],"snapshot_sha256":"b354a9bd0ead3ab684c26dcd19070edbe00a5a9e2cdf0238930038f8da0a8343"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-20T00:01:20.572815Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T23:41:09.774484Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T21:41:57.749774Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T21:33:23.693196Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.17406/integrity.json","findings":[],"snapshot_sha256":"11c82fdfe5b4f247de2b7077165f9ac648f01db5b18a9f330cd9c632bf592588","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Side-channel attacks exploit unintended information leakage from system behavior and continue to pose serious privacy risks in modern platforms. Despite extensive prior work, side-channel analysis remains largely manual and fragmented, typically assuming predefined target events and a fixed set of known channels. As systems and applications grow increasingly complex, several fundamental questions remain unanswered: which user or system events are sensitive in practice, how side channels associated with these events can be systematically discovered without exhaustive manual effort, and how thei","authors_text":"Xiaofeng Wang, Yuhua Sun, Zhen Xu, Zihao Wang","cross_cats":[],"headline":"SCAgent automates discovery of side-channel leaks by using LLM agents to explore sensitive events and verify channels without manual targets or large datasets.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2026-05-17T12:03:45Z","title":"Rethinking Side-Channel Analysis: Automated Discovery and Analysis of Side-Channel Leakage with LLM-Assisted Agents"},"references":{"count":67,"internal_anchors":2,"resolved_work":67,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Peeking into your app without actually seeing it: UI state inference and novel android attacks,","work_id":"b6fc4108-ef1e-473d-bc1d-40ba7571fa12","year":2014},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Memento: Learning secrets from process footprints,","work_id":"1e7230f3-feb7-4918-ba32-489b2247d6e1","year":2012},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Screenmilker: How to milk your android screen for secrets,","work_id":"bc590658-a246-499f-9739-870b6b228b9b","year":2014},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Identity, location, disease and more: inferring your secrets from android public resources,","work_id":"fd6c7143-e7ff-4e79-be6d-6ad6f94506a2","year":2013},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Os-level side channels without procfs: Exploring cross-app information leakage on ios,","work_id":"662f7812-f3b3-45e7-acca-193d484cb79c","year":2018}],"snapshot_sha256":"78748d7c811efa14eaba081867d69c4864bdb4afdb45b970855725e5ef0f6f7f"},"source":{"id":"2605.17406","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T23:31:30.169368Z","id":"7ac8c288-c135-48b6-94b2-7de51011dd31","model_set":{"reader":"grok-4.3"},"one_line_summary":"SCAgent automates side-channel leakage discovery via LLM agents for target identification and few-shot foundation models for scalable analysis on iOS.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"SCAgent automates discovery of side-channel leaks by using LLM agents to explore sensitive events and verify channels without manual targets or large datasets.","strongest_claim":"SCAgent performs agent-driven system exploration guided by LLM-based semantic reasoning to identify sensitive targets beyond manually specified events, reasons over system documentation with explicit verification, and enables scalable analysis under limited data via few-shot learning with a time-shift-robust feature extraction layer.","weakest_assumption":"The assumption that LLM semantic reasoning combined with explicit verification steps over system documentation can reliably identify feasible side channels and mitigate hallucination risks sufficiently for practical use, as stated in the description of target identification and channel discovery."}},"verdict_id":"7ac8c288-c135-48b6-94b2-7de51011dd31"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:cd8d08082cc6c104d6eefc18aa42a0b2aee9c3c4e8c27dfcee7f92a70c0037bd","target":"record","created_at":"2026-05-20T00:03:56Z","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":"c98622e9feb154bfbb11a7a97a810eb90cb2887924dd1555c59fc79b857d4fed","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2026-05-17T12:03:45Z","title_canon_sha256":"f4d14b50bfb517b3168c13cec698d03c380811cf06251d4ff587adc30bac4f47"},"schema_version":"1.0","source":{"id":"2605.17406","kind":"arxiv","version":1}},"canonical_sha256":"4cd382693f33ac5b3afa819e4d83855f66457d7e293fee7b4a4e733a41b623c7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4cd382693f33ac5b3afa819e4d83855f66457d7e293fee7b4a4e733a41b623c7","first_computed_at":"2026-05-20T00:03:56.857799Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:03:56.857799Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HLNAF9IHGca21vvSFYVN8lekqM4Iw9t+Slxk/EsMnqXcSc9qXUXXlHEmOlISWv/DXpH2AZeWCiGAIt5+ywVBCA==","signature_status":"signed_v1","signed_at":"2026-05-20T00:03:56.859441Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.17406","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cd8d08082cc6c104d6eefc18aa42a0b2aee9c3c4e8c27dfcee7f92a70c0037bd","sha256:4250e45865b04069b0e76e7bcf02209b900e568c109b1dd90dd822ec35126814"],"state_sha256":"423ec7766a929b066634511a8313b697e411ef869043d6e462c2fbd910fc0ab8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RcgjE7pXFOFmfu8T3xHw4Nx6xh2pZZOkcurj6C/3DqGbHdKvgz8RJGFzdnPz3JQOt0aLYDidheXEaqLBwY9MBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T07:57:46.986120Z","bundle_sha256":"8f79ee7f9d387e270393205e1cc966856b3a5c72e1c857ba98fd61253f64d3ad"}}