{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:ID6GNMMFY2BJVOKCJ2MR34QMNY","short_pith_number":"pith:ID6GNMMF","canonical_record":{"source":{"id":"2511.07686","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"physics.chem-ph","submitted_at":"2025-11-10T23:08:54Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"74e96718aac08fdbfe865d4f24f6d62478bdefc553d92d900392e2c40af412bc","abstract_canon_sha256":"476fe7d95c376a17fc988a854378854839088867e05640f66a6e5036d9d5c340"},"schema_version":"1.0"},"canonical_sha256":"40fc66b185c6829ab9424e991df20c6e372f8e7d37be2eb6415d1d356704f059","source":{"kind":"arxiv","id":"2511.07686","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2511.07686","created_at":"2026-05-17T23:39:17Z"},{"alias_kind":"arxiv_version","alias_value":"2511.07686v2","created_at":"2026-05-17T23:39:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.07686","created_at":"2026-05-17T23:39:17Z"},{"alias_kind":"pith_short_12","alias_value":"ID6GNMMFY2BJ","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"ID6GNMMFY2BJVOKC","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"ID6GNMMF","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:ID6GNMMFY2BJVOKCJ2MR34QMNY","target":"record","payload":{"canonical_record":{"source":{"id":"2511.07686","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"physics.chem-ph","submitted_at":"2025-11-10T23:08:54Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"74e96718aac08fdbfe865d4f24f6d62478bdefc553d92d900392e2c40af412bc","abstract_canon_sha256":"476fe7d95c376a17fc988a854378854839088867e05640f66a6e5036d9d5c340"},"schema_version":"1.0"},"canonical_sha256":"40fc66b185c6829ab9424e991df20c6e372f8e7d37be2eb6415d1d356704f059","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:17.157944Z","signature_b64":"uA48k4TwVIY69HGnkz9yr7fSe46EqXxRLVeLDA5o59HFgrzVloin+tWYgPSHiH+J4/fJEekoF/gv/riJ7XKsCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"40fc66b185c6829ab9424e991df20c6e372f8e7d37be2eb6415d1d356704f059","last_reissued_at":"2026-05-17T23:39:17.157329Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:17.157329Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2511.07686","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":"2026-05-17T23:39:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2Buy/Xp+b3np7SpDZcqOiDH6rNuDjxKv221CFq7fUVP2S31tUnZ/IPhabxrryLHbNJaOtgsIIn2kfzIP5f6FCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T04:59:18.080410Z"},"content_sha256":"a8522765905277c6d6df7e6924ea4614b0d3121845d75a938018fb9189a377bf","schema_version":"1.0","event_id":"sha256:a8522765905277c6d6df7e6924ea4614b0d3121845d75a938018fb9189a377bf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:ID6GNMMFY2BJVOKCJ2MR34QMNY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Kolmogorov-Arnold Chemical Reaction Neural Networks for learning pressure-dependent kinetic rate laws","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"Kolmogorov-Arnold Chemical Reaction Neural Networks learn pressure-dependent kinetic rates directly from data while preserving Arrhenius and mass-action structure.","cross_cats":["cs.LG"],"primary_cat":"physics.chem-ph","authors_text":"Benjamin C. Koenig, Sili Deng","submitted_at":"2025-11-10T23:08:54Z","abstract_excerpt":"Chemical Reaction Neural Networks (CRNNs) have emerged as an interpretable machine learning framework for discovering reaction kinetics directly from data, while strictly adhering to the Arrhenius and mass action laws. However, standard CRNNs cannot represent pressure-dependent or mixture-based rate behavior, which is critical in many combustion and chemical systems and typically requires empirical falloff formulations such as Troe or SRI, or data-based interpolation or polynomial fits such as PLOG or Chebyshev Polynomials. Here, we develop Kolmogorov-Arnold Chemical Reaction Neural Networks ("},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"KA-CRNNs enable assumption-free inference of global and collider-specific pressure effects directly from data while maintaining Arrhenius and mass action interpretability, achieving a 2.88x reduction in MSE compared to interpolative approaches on two proof-of-concept studies.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the Kolmogorov-Arnold activations can accurately represent complex pressure and mixture dependencies across the full range of temperatures, pressures, and bath gases without violating physical constraints or requiring post-training adjustments.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"KA-CRNNs learn pressure-dependent and collider-specific kinetic rate laws from data using Kolmogorov-Arnold activations inside a CRNN framework, outperforming interpolative methods by 2.88x in MSE on two proof-of-concept reactions.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Kolmogorov-Arnold Chemical Reaction Neural Networks learn pressure-dependent kinetic rates directly from data while preserving Arrhenius and mass-action structure.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"c3e29134f1f33037e3cf1ab7a65af80374592f8332619ca793f9cce90cd8f160"},"source":{"id":"2511.07686","kind":"arxiv","version":2},"verdict":{"id":"9a91a0df-a0ae-4868-a7bc-e5148b53a6fb","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-17T23:18:49.178038Z","strongest_claim":"KA-CRNNs enable assumption-free inference of global and collider-specific pressure effects directly from data while maintaining Arrhenius and mass action interpretability, achieving a 2.88x reduction in MSE compared to interpolative approaches on two proof-of-concept studies.","one_line_summary":"KA-CRNNs learn pressure-dependent and collider-specific kinetic rate laws from data using Kolmogorov-Arnold activations inside a CRNN framework, outperforming interpolative methods by 2.88x in MSE on two proof-of-concept reactions.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the Kolmogorov-Arnold activations can accurately represent complex pressure and mixture dependencies across the full range of temperatures, pressures, and bath gases without violating physical constraints or requiring post-training adjustments.","pith_extraction_headline":"Kolmogorov-Arnold Chemical Reaction Neural Networks learn pressure-dependent kinetic rates directly from data while preserving Arrhenius and mass-action structure."},"references":{"count":34,"sample":[{"doi":"","year":null,"title":"of the KAN activation, ϕi (x) = NX j=1 wψ i,j ·ψ(||x−c j||) +w b i ·b(x),(5) ψ(r) = exp(− r2 2h2 ).(6) In this formulationNis the KAN basis function grid size, andw ψ i,j andw b j are the learnable ne","work_id":"4913248e-2d1e-4ccc-adf4-66c462a0a70f","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"ground truth","work_id":"966f278e-efcd-4a91-9407-4a3e5b6b7f07","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"W. Ji and S. Deng, Autonomous Discovery of Unknown Reaction Pathways from Data by Chemical Reaction Neural Network, J. Phys. Chem. A.125, 1082 (2021)","work_id":"14a201da-1c7b-4ddc-9ea4-eed5a6acd895","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2016,"title":"C. W. Gao, J. W. Allen, W. H. Green, and R. H. West, Reaction mechanism generator: Automatic construction of chemical kinetic mechanisms, Comput. Phys. Com- mun.203, 212 (2016)","work_id":"0a874512-3286-49a8-94dc-4f1314eb0a80","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"S. Deng, L. Wang, S. Kim, and B. C. Koenig, Scientific machine learning in combustion for discovery, simulation, and control, Proc. Combust. Inst.41, 105796 (2025)","work_id":"97a17f10-5db6-43a7-b66b-1d970616f729","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":34,"snapshot_sha256":"01fa1cd7f93f4ba47c1575f10604c38ae6b1107fd341610429e935b67d6ddb1a","internal_anchors":6},"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":"9a91a0df-a0ae-4868-a7bc-e5148b53a6fb"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:39:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/5t629sTTG6vrtW7xS3XNX8woZHI7bKD6R6MAY+6sso21jyp26oO3M/xgyA2OAvvYbzKrhF6bW6+rdvCeHzwAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T04:59:18.081470Z"},"content_sha256":"bcc278a6e7cf4a39c5d05e6ebc5aba8b6d3cd575e09b78cf55d65acce6d02a41","schema_version":"1.0","event_id":"sha256:bcc278a6e7cf4a39c5d05e6ebc5aba8b6d3cd575e09b78cf55d65acce6d02a41"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ID6GNMMFY2BJVOKCJ2MR34QMNY/bundle.json","state_url":"https://pith.science/pith/ID6GNMMFY2BJVOKCJ2MR34QMNY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ID6GNMMFY2BJVOKCJ2MR34QMNY/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":"2026-06-05T04:59:18Z","links":{"resolver":"https://pith.science/pith/ID6GNMMFY2BJVOKCJ2MR34QMNY","bundle":"https://pith.science/pith/ID6GNMMFY2BJVOKCJ2MR34QMNY/bundle.json","state":"https://pith.science/pith/ID6GNMMFY2BJVOKCJ2MR34QMNY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ID6GNMMFY2BJVOKCJ2MR34QMNY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:ID6GNMMFY2BJVOKCJ2MR34QMNY","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":"476fe7d95c376a17fc988a854378854839088867e05640f66a6e5036d9d5c340","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"physics.chem-ph","submitted_at":"2025-11-10T23:08:54Z","title_canon_sha256":"74e96718aac08fdbfe865d4f24f6d62478bdefc553d92d900392e2c40af412bc"},"schema_version":"1.0","source":{"id":"2511.07686","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2511.07686","created_at":"2026-05-17T23:39:17Z"},{"alias_kind":"arxiv_version","alias_value":"2511.07686v2","created_at":"2026-05-17T23:39:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.07686","created_at":"2026-05-17T23:39:17Z"},{"alias_kind":"pith_short_12","alias_value":"ID6GNMMFY2BJ","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"ID6GNMMFY2BJVOKC","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"ID6GNMMF","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:bcc278a6e7cf4a39c5d05e6ebc5aba8b6d3cd575e09b78cf55d65acce6d02a41","target":"graph","created_at":"2026-05-17T23:39:17Z","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":"KA-CRNNs enable assumption-free inference of global and collider-specific pressure effects directly from data while maintaining Arrhenius and mass action interpretability, achieving a 2.88x reduction in MSE compared to interpolative approaches on two proof-of-concept studies."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the Kolmogorov-Arnold activations can accurately represent complex pressure and mixture dependencies across the full range of temperatures, pressures, and bath gases without violating physical constraints or requiring post-training adjustments."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"KA-CRNNs learn pressure-dependent and collider-specific kinetic rate laws from data using Kolmogorov-Arnold activations inside a CRNN framework, outperforming interpolative methods by 2.88x in MSE on two proof-of-concept reactions."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Kolmogorov-Arnold Chemical Reaction Neural Networks learn pressure-dependent kinetic rates directly from data while preserving Arrhenius and mass-action structure."}],"snapshot_sha256":"c3e29134f1f33037e3cf1ab7a65af80374592f8332619ca793f9cce90cd8f160"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Chemical Reaction Neural Networks (CRNNs) have emerged as an interpretable machine learning framework for discovering reaction kinetics directly from data, while strictly adhering to the Arrhenius and mass action laws. However, standard CRNNs cannot represent pressure-dependent or mixture-based rate behavior, which is critical in many combustion and chemical systems and typically requires empirical falloff formulations such as Troe or SRI, or data-based interpolation or polynomial fits such as PLOG or Chebyshev Polynomials. Here, we develop Kolmogorov-Arnold Chemical Reaction Neural Networks (","authors_text":"Benjamin C. Koenig, Sili Deng","cross_cats":["cs.LG"],"headline":"Kolmogorov-Arnold Chemical Reaction Neural Networks learn pressure-dependent kinetic rates directly from data while preserving Arrhenius and mass-action structure.","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"physics.chem-ph","submitted_at":"2025-11-10T23:08:54Z","title":"Kolmogorov-Arnold Chemical Reaction Neural Networks for learning pressure-dependent kinetic rate laws"},"references":{"count":34,"internal_anchors":6,"resolved_work":34,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"of the KAN activation, ϕi (x) = NX j=1 wψ i,j ·ψ(||x−c j||) +w b i ·b(x),(5) ψ(r) = exp(− r2 2h2 ).(6) In this formulationNis the KAN basis function grid size, andw ψ i,j andw b j are the learnable ne","work_id":"4913248e-2d1e-4ccc-adf4-66c462a0a70f","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"ground truth","work_id":"966f278e-efcd-4a91-9407-4a3e5b6b7f07","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"W. Ji and S. Deng, Autonomous Discovery of Unknown Reaction Pathways from Data by Chemical Reaction Neural Network, J. Phys. Chem. A.125, 1082 (2021)","work_id":"14a201da-1c7b-4ddc-9ea4-eed5a6acd895","year":2021},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"C. W. Gao, J. W. Allen, W. H. Green, and R. H. West, Reaction mechanism generator: Automatic construction of chemical kinetic mechanisms, Comput. Phys. Com- mun.203, 212 (2016)","work_id":"0a874512-3286-49a8-94dc-4f1314eb0a80","year":2016},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"S. Deng, L. Wang, S. Kim, and B. C. Koenig, Scientific machine learning in combustion for discovery, simulation, and control, Proc. Combust. Inst.41, 105796 (2025)","work_id":"97a17f10-5db6-43a7-b66b-1d970616f729","year":2025}],"snapshot_sha256":"01fa1cd7f93f4ba47c1575f10604c38ae6b1107fd341610429e935b67d6ddb1a"},"source":{"id":"2511.07686","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-17T23:18:49.178038Z","id":"9a91a0df-a0ae-4868-a7bc-e5148b53a6fb","model_set":{"reader":"grok-4.3"},"one_line_summary":"KA-CRNNs learn pressure-dependent and collider-specific kinetic rate laws from data using Kolmogorov-Arnold activations inside a CRNN framework, outperforming interpolative methods by 2.88x in MSE on two proof-of-concept reactions.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Kolmogorov-Arnold Chemical Reaction Neural Networks learn pressure-dependent kinetic rates directly from data while preserving Arrhenius and mass-action structure.","strongest_claim":"KA-CRNNs enable assumption-free inference of global and collider-specific pressure effects directly from data while maintaining Arrhenius and mass action interpretability, achieving a 2.88x reduction in MSE compared to interpolative approaches on two proof-of-concept studies.","weakest_assumption":"That the Kolmogorov-Arnold activations can accurately represent complex pressure and mixture dependencies across the full range of temperatures, pressures, and bath gases without violating physical constraints or requiring post-training adjustments."}},"verdict_id":"9a91a0df-a0ae-4868-a7bc-e5148b53a6fb"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:a8522765905277c6d6df7e6924ea4614b0d3121845d75a938018fb9189a377bf","target":"record","created_at":"2026-05-17T23:39:17Z","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":"476fe7d95c376a17fc988a854378854839088867e05640f66a6e5036d9d5c340","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"physics.chem-ph","submitted_at":"2025-11-10T23:08:54Z","title_canon_sha256":"74e96718aac08fdbfe865d4f24f6d62478bdefc553d92d900392e2c40af412bc"},"schema_version":"1.0","source":{"id":"2511.07686","kind":"arxiv","version":2}},"canonical_sha256":"40fc66b185c6829ab9424e991df20c6e372f8e7d37be2eb6415d1d356704f059","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"40fc66b185c6829ab9424e991df20c6e372f8e7d37be2eb6415d1d356704f059","first_computed_at":"2026-05-17T23:39:17.157329Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:39:17.157329Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uA48k4TwVIY69HGnkz9yr7fSe46EqXxRLVeLDA5o59HFgrzVloin+tWYgPSHiH+J4/fJEekoF/gv/riJ7XKsCg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:39:17.157944Z","signed_message":"canonical_sha256_bytes"},"source_id":"2511.07686","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a8522765905277c6d6df7e6924ea4614b0d3121845d75a938018fb9189a377bf","sha256:bcc278a6e7cf4a39c5d05e6ebc5aba8b6d3cd575e09b78cf55d65acce6d02a41"],"state_sha256":"5b9e5b2c09be2cf0c0b8688b1d4776cb7f1f50fefcc7b54dadd3843cf33baa7b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vtyGR2ImdsHRZu2rktv22LCCZPxCpx1iVbCE+MVgxvvLXuxVlMhb3Abi5kpJl/UCotbObQbmfEhOOtJcMvs/Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T04:59:18.085614Z","bundle_sha256":"9053a1d9032bc22aec6092c2fdeba3d744876e5af1af5e632d9cb8151e5b484a"}}