{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:FAUZONQK64KTIQB7CJZVRVCHMN","short_pith_number":"pith:FAUZONQK","canonical_record":{"source":{"id":"2511.15068","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2025-11-19T03:20:04Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"31bba1e9ba4c084dd931aa691a0a70448bd41007f18defe1a56b8c02dc51a0d6","abstract_canon_sha256":"58f98323fd9b294efba0ce73cf9be9ce4deeba5b706a0ea795bb804650526ebf"},"schema_version":"1.0"},"canonical_sha256":"282997360af71534403f127358d447634930f29cfb73e4663d5d022b10cf4276","source":{"kind":"arxiv","id":"2511.15068","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2511.15068","created_at":"2026-07-21T02:21:25Z"},{"alias_kind":"arxiv_version","alias_value":"2511.15068v3","created_at":"2026-07-21T02:21:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.15068","created_at":"2026-07-21T02:21:25Z"},{"alias_kind":"pith_short_12","alias_value":"FAUZONQK64KT","created_at":"2026-07-21T02:21:25Z"},{"alias_kind":"pith_short_16","alias_value":"FAUZONQK64KTIQB7","created_at":"2026-07-21T02:21:25Z"},{"alias_kind":"pith_short_8","alias_value":"FAUZONQK","created_at":"2026-07-21T02:21:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:FAUZONQK64KTIQB7CJZVRVCHMN","target":"record","payload":{"canonical_record":{"source":{"id":"2511.15068","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2025-11-19T03:20:04Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"31bba1e9ba4c084dd931aa691a0a70448bd41007f18defe1a56b8c02dc51a0d6","abstract_canon_sha256":"58f98323fd9b294efba0ce73cf9be9ce4deeba5b706a0ea795bb804650526ebf"},"schema_version":"1.0"},"canonical_sha256":"282997360af71534403f127358d447634930f29cfb73e4663d5d022b10cf4276","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-21T02:21:25.912889Z","signature_b64":"Cs1lhU2YuDSiKUaUPuqXvVSbIlUM0cpOgPLKIN5CjJjseoK0uOsjlWrhKqpWQQHzsLapgGGFFeOkq/zmnm+FCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"282997360af71534403f127358d447634930f29cfb73e4663d5d022b10cf4276","last_reissued_at":"2026-07-21T02:21:25.911903Z","signature_status":"signed_v1","first_computed_at":"2026-07-21T02:21:25.911903Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2511.15068","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-21T02:21:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ETRaK9fnU7ptX06xC8EiJz9K1f8/K1lnyWaGYHk8p5uqq0cRBnlP2Jv/QmCiPUXcXrEpI3T2HmHhDILoD17SCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T12:57:40.022959Z"},"content_sha256":"903b9bc7dd292f2e1896c600f0ab2a3b069041b7599b927e28545e8da3b7a203","schema_version":"1.0","event_id":"sha256:903b9bc7dd292f2e1896c600f0ab2a3b069041b7599b927e28545e8da3b7a203"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:FAUZONQK64KTIQB7CJZVRVCHMN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Classification Trees with Valid Inference via the Exponential Mechanism","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Classification trees fitted via the exponential mechanism produce pivots for asymptotically valid inference on model parameters.","cross_cats":["stat.ML"],"primary_cat":"stat.ME","authors_text":"Snigdha Panigrahi, Soham Bakshi","submitted_at":"2025-11-19T03:20:04Z","abstract_excerpt":"Decision trees are widely used for non-linear modeling, as they capture interactions between predictors while producing inherently interpretable models. Despite their popularity, performing inference on the non-linear fit remains largely unaddressed. This paper focuses on classification trees and makes two key contributions. First, we introduce a novel tree-fitting method that replaces the greedy splitting of the predictor space in standard tree algorithms with a probabilistic approach. Each split in our approach is selected according to sampling probabilities defined by an exponential mechani"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Our method produces pivots directly from the sampling probabilities in the exponential mechanism. In theory, our pivots allow asymptotically valid inference on the parameters in the predictive fit, and in practice, our method delivers powerful inference without sacrificing predictive accuracy, in contrast to data splitting methods.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the sampling probabilities from the exponential mechanism, when used to define pivots, correctly account for the adaptivity of the entire tree-growing process and yield asymptotically valid inference for the parameters in the final predictive fit.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Classification trees built with the exponential mechanism generate asymptotically valid inference pivots from sampling probabilities without major accuracy loss.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Classification trees fitted via the exponential mechanism produce pivots for asymptotically valid inference on model parameters.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"b8d123896b9358e046265d12eaa79482f342c104ceac26134c8f314f2cf20dad"},"source":{"id":"2511.15068","kind":"arxiv","version":3},"verdict":{"id":"b237bef3-427f-4b2e-a59d-04a2d3bd255c","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-17T21:21:51.414738Z","strongest_claim":"Our method produces pivots directly from the sampling probabilities in the exponential mechanism. In theory, our pivots allow asymptotically valid inference on the parameters in the predictive fit, and in practice, our method delivers powerful inference without sacrificing predictive accuracy, in contrast to data splitting methods.","one_line_summary":"Classification trees built with the exponential mechanism generate asymptotically valid inference pivots from sampling probabilities without major accuracy loss.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the sampling probabilities from the exponential mechanism, when used to define pivots, correctly account for the adaptivity of the entire tree-growing process and yield asymptotically valid inference for the parameters in the final predictive fit.","pith_extraction_headline":"Classification trees fitted via the exponential mechanism produce pivots for asymptotically valid inference on model parameters."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2511.15068/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":6,"sample":[{"doi":"10.1007/bf01194075","year":2022,"title":"Holistic Evaluation of Language Models","work_id":"cc02a01e-7218-47dc-8e66-3333e7e4adec","ref_index":1,"cited_arxiv_id":"2211.09110","is_internal_anchor":true},{"doi":"","year":null,"title":"The gain functionsG k h(v)and their first- to third-order derivatives order∇G k h(v),∇2Gk h(v), ∇3Gk h(v)are bounded for allv∈ eDn","work_id":"411a36d0-f34c-437c-8bac-0508e33b8602","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"The functionf(.), derived from the logarithm of the sampling probabilities based on the gain functions, isL-Lipschitz, i.e.|f(v 1)−f(v 2)| ≤L||v 1 −v 2||for allv 1, v2 ∈ eDn","work_id":"af5bd0d4-c390-4116-8247-91b70db37157","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"49 Firstly, note that the absolute value of each component ofη k h ∈R 8 is uniformly bounded with respect ton","work_id":"269468e4-598c-4b94-b4fc-9b79cb58e1d4","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2003,"title":"Now, since eachb i,n is uni- 55 formly bounded, i.e., sup n,i |bi,n| ≤ ¯C+ (by Proposition B.1), and the uniform multivariate Berry–Esseen bound in Bentkus [2003] applies, it follows that sup A∈C¯k P(","work_id":"4766ee8b-d91e-4fd1-90d0-d966a1b3628c","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":6,"snapshot_sha256":"0242cfe8615d3dc41a70e63e44a36886c9e1094bf3ac0bde4613389c6e17ac10","internal_anchors":1},"formal_canon":{"evidence_count":2,"snapshot_sha256":"e4b2493fa9fff761bd57cfb3cfe959b177c300a6bfeb714879e3d889c2e8e9d0"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"b237bef3-427f-4b2e-a59d-04a2d3bd255c"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-21T02:21:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Zx+sXdbbEe0BkLthRrCxLTNVjxrlfEMG/Y88XKEHiTXGLp3lXd6sZpZeCjeqjhcIh7urO4OH8NaOv9gx0F5rDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T12:57:40.024572Z"},"content_sha256":"ba13f052e5149a0b1cf3a78ad45c10f4656369c37b8da73a3cd499318fe8d14f","schema_version":"1.0","event_id":"sha256:ba13f052e5149a0b1cf3a78ad45c10f4656369c37b8da73a3cd499318fe8d14f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FAUZONQK64KTIQB7CJZVRVCHMN/bundle.json","state_url":"https://pith.science/pith/FAUZONQK64KTIQB7CJZVRVCHMN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FAUZONQK64KTIQB7CJZVRVCHMN/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":"2026-08-04T12:57:40Z","links":{"resolver":"https://pith.science/pith/FAUZONQK64KTIQB7CJZVRVCHMN","bundle":"https://pith.science/pith/FAUZONQK64KTIQB7CJZVRVCHMN/bundle.json","state":"https://pith.science/pith/FAUZONQK64KTIQB7CJZVRVCHMN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FAUZONQK64KTIQB7CJZVRVCHMN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:FAUZONQK64KTIQB7CJZVRVCHMN","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":"58f98323fd9b294efba0ce73cf9be9ce4deeba5b706a0ea795bb804650526ebf","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2025-11-19T03:20:04Z","title_canon_sha256":"31bba1e9ba4c084dd931aa691a0a70448bd41007f18defe1a56b8c02dc51a0d6"},"schema_version":"1.0","source":{"id":"2511.15068","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2511.15068","created_at":"2026-07-21T02:21:25Z"},{"alias_kind":"arxiv_version","alias_value":"2511.15068v3","created_at":"2026-07-21T02:21:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.15068","created_at":"2026-07-21T02:21:25Z"},{"alias_kind":"pith_short_12","alias_value":"FAUZONQK64KT","created_at":"2026-07-21T02:21:25Z"},{"alias_kind":"pith_short_16","alias_value":"FAUZONQK64KTIQB7","created_at":"2026-07-21T02:21:25Z"},{"alias_kind":"pith_short_8","alias_value":"FAUZONQK","created_at":"2026-07-21T02:21:25Z"}],"graph_snapshots":[{"event_id":"sha256:ba13f052e5149a0b1cf3a78ad45c10f4656369c37b8da73a3cd499318fe8d14f","target":"graph","created_at":"2026-07-21T02:21:25Z","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":"Our method produces pivots directly from the sampling probabilities in the exponential mechanism. In theory, our pivots allow asymptotically valid inference on the parameters in the predictive fit, and in practice, our method delivers powerful inference without sacrificing predictive accuracy, in contrast to data splitting methods."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the sampling probabilities from the exponential mechanism, when used to define pivots, correctly account for the adaptivity of the entire tree-growing process and yield asymptotically valid inference for the parameters in the final predictive fit."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Classification trees built with the exponential mechanism generate asymptotically valid inference pivots from sampling probabilities without major accuracy loss."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Classification trees fitted via the exponential mechanism produce pivots for asymptotically valid inference on model parameters."}],"snapshot_sha256":"b8d123896b9358e046265d12eaa79482f342c104ceac26134c8f314f2cf20dad"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"e4b2493fa9fff761bd57cfb3cfe959b177c300a6bfeb714879e3d889c2e8e9d0"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2511.15068/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Decision trees are widely used for non-linear modeling, as they capture interactions between predictors while producing inherently interpretable models. Despite their popularity, performing inference on the non-linear fit remains largely unaddressed. This paper focuses on classification trees and makes two key contributions. First, we introduce a novel tree-fitting method that replaces the greedy splitting of the predictor space in standard tree algorithms with a probabilistic approach. Each split in our approach is selected according to sampling probabilities defined by an exponential mechani","authors_text":"Snigdha Panigrahi, Soham Bakshi","cross_cats":["stat.ML"],"headline":"Classification trees fitted via the exponential mechanism produce pivots for asymptotically valid inference on model parameters.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2025-11-19T03:20:04Z","title":"Classification Trees with Valid Inference via the Exponential Mechanism"},"references":{"count":6,"internal_anchors":1,"resolved_work":6,"sample":[{"cited_arxiv_id":"2211.09110","doi":"10.1007/bf01194075","is_internal_anchor":true,"ref_index":1,"title":"Holistic Evaluation of Language Models","work_id":"cc02a01e-7218-47dc-8e66-3333e7e4adec","year":2022},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"The gain functionsG k h(v)and their first- to third-order derivatives order∇G k h(v),∇2Gk h(v), ∇3Gk h(v)are bounded for allv∈ eDn","work_id":"411a36d0-f34c-437c-8bac-0508e33b8602","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"The functionf(.), derived from the logarithm of the sampling probabilities based on the gain functions, isL-Lipschitz, i.e.|f(v 1)−f(v 2)| ≤L||v 1 −v 2||for allv 1, v2 ∈ eDn","work_id":"af5bd0d4-c390-4116-8247-91b70db37157","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"49 Firstly, note that the absolute value of each component ofη k h ∈R 8 is uniformly bounded with respect ton","work_id":"269468e4-598c-4b94-b4fc-9b79cb58e1d4","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Now, since eachb i,n is uni- 55 formly bounded, i.e., sup n,i |bi,n| ≤ ¯C+ (by Proposition B.1), and the uniform multivariate Berry–Esseen bound in Bentkus [2003] applies, it follows that sup A∈C¯k P(","work_id":"4766ee8b-d91e-4fd1-90d0-d966a1b3628c","year":2003}],"snapshot_sha256":"0242cfe8615d3dc41a70e63e44a36886c9e1094bf3ac0bde4613389c6e17ac10"},"source":{"id":"2511.15068","kind":"arxiv","version":3},"verdict":{"created_at":"2026-05-17T21:21:51.414738Z","id":"b237bef3-427f-4b2e-a59d-04a2d3bd255c","model_set":{"reader":"grok-4.3"},"one_line_summary":"Classification trees built with the exponential mechanism generate asymptotically valid inference pivots from sampling probabilities without major accuracy loss.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Classification trees fitted via the exponential mechanism produce pivots for asymptotically valid inference on model parameters.","strongest_claim":"Our method produces pivots directly from the sampling probabilities in the exponential mechanism. In theory, our pivots allow asymptotically valid inference on the parameters in the predictive fit, and in practice, our method delivers powerful inference without sacrificing predictive accuracy, in contrast to data splitting methods.","weakest_assumption":"That the sampling probabilities from the exponential mechanism, when used to define pivots, correctly account for the adaptivity of the entire tree-growing process and yield asymptotically valid inference for the parameters in the final predictive fit."}},"verdict_id":"b237bef3-427f-4b2e-a59d-04a2d3bd255c"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:903b9bc7dd292f2e1896c600f0ab2a3b069041b7599b927e28545e8da3b7a203","target":"record","created_at":"2026-07-21T02:21:25Z","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":"58f98323fd9b294efba0ce73cf9be9ce4deeba5b706a0ea795bb804650526ebf","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2025-11-19T03:20:04Z","title_canon_sha256":"31bba1e9ba4c084dd931aa691a0a70448bd41007f18defe1a56b8c02dc51a0d6"},"schema_version":"1.0","source":{"id":"2511.15068","kind":"arxiv","version":3}},"canonical_sha256":"282997360af71534403f127358d447634930f29cfb73e4663d5d022b10cf4276","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"282997360af71534403f127358d447634930f29cfb73e4663d5d022b10cf4276","first_computed_at":"2026-07-21T02:21:25.911903Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-21T02:21:25.911903Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Cs1lhU2YuDSiKUaUPuqXvVSbIlUM0cpOgPLKIN5CjJjseoK0uOsjlWrhKqpWQQHzsLapgGGFFeOkq/zmnm+FCg==","signature_status":"signed_v1","signed_at":"2026-07-21T02:21:25.912889Z","signed_message":"canonical_sha256_bytes"},"source_id":"2511.15068","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:903b9bc7dd292f2e1896c600f0ab2a3b069041b7599b927e28545e8da3b7a203","sha256:ba13f052e5149a0b1cf3a78ad45c10f4656369c37b8da73a3cd499318fe8d14f"],"state_sha256":"e5780199b78a20fe5c29fffa23c4b4a87694c1c05c10e9eedb8a67e75d06c5c3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"U7t9BMmw34dzXpPbFe3Zmv9vTu8e1rrg3mUnTUlRN2Z6MXztTHZNbGsh5uYdsAGXSd2ad5b35aeyS6h7PnX3Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T12:57:40.033096Z","bundle_sha256":"8b327cf5406ff86797d22c70c0d76a07cc7f663bd509d17c000363575d7f89d7"}}