{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:CJP6YGLWRI2YS7KZWOMM2BBMZC","short_pith_number":"pith:CJP6YGLW","canonical_record":{"source":{"id":"2605.16966","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-16T12:41:36Z","cross_cats_sorted":[],"title_canon_sha256":"c42baf6b69628429fc79badf11c7be870d9a6335808543e8a7e1a84078e6674d","abstract_canon_sha256":"3aa843417a6454cdcf23c8c4ca4ba6713bc7885656780d45e3750c5adc860fdb"},"schema_version":"1.0"},"canonical_sha256":"125fec19768a35897d59b398cd042cc88ee17a0658fc0c665ccdf989d9731953","source":{"kind":"arxiv","id":"2605.16966","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16966","created_at":"2026-05-20T00:03:33Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16966v1","created_at":"2026-05-20T00:03:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16966","created_at":"2026-05-20T00:03:33Z"},{"alias_kind":"pith_short_12","alias_value":"CJP6YGLWRI2Y","created_at":"2026-05-20T00:03:33Z"},{"alias_kind":"pith_short_16","alias_value":"CJP6YGLWRI2YS7KZ","created_at":"2026-05-20T00:03:33Z"},{"alias_kind":"pith_short_8","alias_value":"CJP6YGLW","created_at":"2026-05-20T00:03:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:CJP6YGLWRI2YS7KZWOMM2BBMZC","target":"record","payload":{"canonical_record":{"source":{"id":"2605.16966","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-16T12:41:36Z","cross_cats_sorted":[],"title_canon_sha256":"c42baf6b69628429fc79badf11c7be870d9a6335808543e8a7e1a84078e6674d","abstract_canon_sha256":"3aa843417a6454cdcf23c8c4ca4ba6713bc7885656780d45e3750c5adc860fdb"},"schema_version":"1.0"},"canonical_sha256":"125fec19768a35897d59b398cd042cc88ee17a0658fc0c665ccdf989d9731953","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:03:33.451822Z","signature_b64":"ufSngrVbJ3dunsCsWlydNEu9iOmFh11d/Co6HnEtltnAw9/U4NXUqeTgbH/u835nYnncODHWAl17+9PKpY6XAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"125fec19768a35897d59b398cd042cc88ee17a0658fc0c665ccdf989d9731953","last_reissued_at":"2026-05-20T00:03:33.451045Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:03:33.451045Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.16966","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":"2026-05-20T00:03:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xbP+otnqVrblGWh6a4H9FXMSTUQAAZ6Htn1sc+8uDfgNd3RnaG6sON3LHqTjkVnuF6FlaumUI7KAU6piNTKCBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T21:41:41.699676Z"},"content_sha256":"3ba7e4aa938b4e9f06514dbfb81e78070c18589326b1eec00b3dfe7e6b940a4f","schema_version":"1.0","event_id":"sha256:3ba7e4aa938b4e9f06514dbfb81e78070c18589326b1eec00b3dfe7e6b940a4f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:CJP6YGLWRI2YS7KZWOMM2BBMZC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Harnessing AI for Inverse Partial Differential Equation Problems: Past, Present, and Prospects","license":"http://creativecommons.org/licenses/by/4.0/","headline":"AI methods are reshaping inverse PDE problems by organizing them into unified categories of inference, design, and control.","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Boyi Zou, Gang Bao, Mingsheng Long, Yi Yang, Yuze Hao, Zhentao Tan","submitted_at":"2026-05-16T12:41:36Z","abstract_excerpt":"Solving inverse partial differential equation (PDE) problems is a fundamental topic in scientific research due to its broad significance across a wide range of real-world applications. Inverse PDE problems arise across medical imaging, geophysics, materials science, and aerodynamics, where the goal is to infer hidden causes, design structures, or control physical states. In this paper, we provide a comprehensive review of recent advances in solving inverse PDE problems using artificial intelligence (AI). We first introduce the basic formulation, key challenges, and traditional numerical founda"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"This survey aims to provide the first unified and systematic perspective on AI for inverse PDE problems, demonstrating how modern learning-based methods are reshaping inverse problems, inverse design, and control problems in PDE-governed systems.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The assumption that the chosen methodological paradigms and representative state-of-the-art approaches from recent years, along with the three-category organization, sufficiently capture and structure the full breadth of advances in the field.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A survey organizing AI methods for inverse PDE problems into inverse problems, inverse design, and control categories, covering applications and future challenges like physics-informed models and uncertainty quantification.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"AI methods are reshaping inverse PDE problems by organizing them into unified categories of inference, design, and control.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"e1ca5b362d04e9f8847cf723728feb8b7f7b10a67e9eeb0b55ae608ed6be7b16"},"source":{"id":"2605.16966","kind":"arxiv","version":1},"verdict":{"id":"46dbde80-5fa3-44e6-b0ab-ec48f5a82824","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T20:24:42.551964Z","strongest_claim":"This survey aims to provide the first unified and systematic perspective on AI for inverse PDE problems, demonstrating how modern learning-based methods are reshaping inverse problems, inverse design, and control problems in PDE-governed systems.","one_line_summary":"A survey organizing AI methods for inverse PDE problems into inverse problems, inverse design, and control categories, covering applications and future challenges like physics-informed models and uncertainty quantification.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The assumption that the chosen methodological paradigms and representative state-of-the-art approaches from recent years, along with the three-category organization, sufficiently capture and structure the full breadth of advances in the field.","pith_extraction_headline":"AI methods are reshaping inverse PDE problems by organizing them into unified categories of inference, design, and control."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.16966/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_compliance","ran_at":"2026-05-19T20:31:34.712990Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T20:31:19.065208Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"cited_work_retraction","ran_at":"2026-05-19T19:51:57.629363Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"citation_quote_validity","ran_at":"2026-05-19T19:50:12.037212Z","status":"skipped","version":"0.1.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T18:41:56.226496Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.312333Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"43877f2a32cb4d2746a5eb0e4d6233da50de7e5c73a4194c76b1884cc0e12318"},"references":{"count":278,"sample":[{"doi":"","year":2016,"title":"TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems","work_id":"91f3c09e-dae6-48ca-80c0-463dd1b1f6e1","ref_index":1,"cited_arxiv_id":"1603.04467","is_internal_anchor":true},{"doi":"","year":1993,"title":"Robert Acar. 1993. Identification of the coefficient in elliptic equations.SIAM journal on control and optimization31, 5 (1993), 1221–1244","work_id":"f2b05fdd-49c7-442c-ac72-6646091dafcd","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2020,"title":"Gabriel Achour, Woong Je Sung, Olivia J Pinon-Fischer, and Dimitri N Mavris. 2020. Development of a conditional generative adversarial network for airfoil shape optimization. InAIAA Scitech 2020 Forum","work_id":"c6ca3d1d-d2e6-4f6c-9680-39650e5bb7fc","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2004,"title":"Grégoire Allaire, François Jouve, and Anca-Maria Toader. 2004. Structural optimization using sensitivity analysis and a level-set method.Journal of computational physics194, 1 (2004), 363–393","work_id":"e27a202d-871f-4e18-a5b6-15c644d74f81","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Kelsey Allen, Tatiana Lopez-Guevara, Kimberly L Stachenfeld, Alvaro Sanchez Gonzalez, Peter Battaglia, Jessica B Hamrick, and Tobias Pfaff","work_id":"efbccf43-8192-41d6-b9ff-9d70f91cdf10","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":278,"snapshot_sha256":"f4ecf9ee2e7b9d3e5a8e662c23511eb74331cedeb34fe79c7ac4cf1695d83ffc","internal_anchors":8},"formal_canon":{"evidence_count":1,"snapshot_sha256":"814523e2f1f25a11517bbeda5155d1552dd4a13cba822d9f19ffedf9aeebfee9"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"46dbde80-5fa3-44e6-b0ab-ec48f5a82824"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:03:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qNSs8ImbQE8fTtsdW5Hn8+PS0ymDZNAxNDb7/oBWF17EsXSLemkQs7hpiMy7HLjV8PDhFfsOY3vq+RibMozGCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T21:41:41.701055Z"},"content_sha256":"70536e5c3d40dc44a77e7fb0557132b8c899145316a1eb985332e02659c6fea4","schema_version":"1.0","event_id":"sha256:70536e5c3d40dc44a77e7fb0557132b8c899145316a1eb985332e02659c6fea4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CJP6YGLWRI2YS7KZWOMM2BBMZC/bundle.json","state_url":"https://pith.science/pith/CJP6YGLWRI2YS7KZWOMM2BBMZC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CJP6YGLWRI2YS7KZWOMM2BBMZC/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":"2026-05-21T21:41:41Z","links":{"resolver":"https://pith.science/pith/CJP6YGLWRI2YS7KZWOMM2BBMZC","bundle":"https://pith.science/pith/CJP6YGLWRI2YS7KZWOMM2BBMZC/bundle.json","state":"https://pith.science/pith/CJP6YGLWRI2YS7KZWOMM2BBMZC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CJP6YGLWRI2YS7KZWOMM2BBMZC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:CJP6YGLWRI2YS7KZWOMM2BBMZC","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":"3aa843417a6454cdcf23c8c4ca4ba6713bc7885656780d45e3750c5adc860fdb","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-16T12:41:36Z","title_canon_sha256":"c42baf6b69628429fc79badf11c7be870d9a6335808543e8a7e1a84078e6674d"},"schema_version":"1.0","source":{"id":"2605.16966","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16966","created_at":"2026-05-20T00:03:33Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16966v1","created_at":"2026-05-20T00:03:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16966","created_at":"2026-05-20T00:03:33Z"},{"alias_kind":"pith_short_12","alias_value":"CJP6YGLWRI2Y","created_at":"2026-05-20T00:03:33Z"},{"alias_kind":"pith_short_16","alias_value":"CJP6YGLWRI2YS7KZ","created_at":"2026-05-20T00:03:33Z"},{"alias_kind":"pith_short_8","alias_value":"CJP6YGLW","created_at":"2026-05-20T00:03:33Z"}],"graph_snapshots":[{"event_id":"sha256:70536e5c3d40dc44a77e7fb0557132b8c899145316a1eb985332e02659c6fea4","target":"graph","created_at":"2026-05-20T00:03:33Z","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":"This survey aims to provide the first unified and systematic perspective on AI for inverse PDE problems, demonstrating how modern learning-based methods are reshaping inverse problems, inverse design, and control problems in PDE-governed systems."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The assumption that the chosen methodological paradigms and representative state-of-the-art approaches from recent years, along with the three-category organization, sufficiently capture and structure the full breadth of advances in the field."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A survey organizing AI methods for inverse PDE problems into inverse problems, inverse design, and control categories, covering applications and future challenges like physics-informed models and uncertainty quantification."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"AI methods are reshaping inverse PDE problems by organizing them into unified categories of inference, design, and control."}],"snapshot_sha256":"e1ca5b362d04e9f8847cf723728feb8b7f7b10a67e9eeb0b55ae608ed6be7b16"},"formal_canon":{"evidence_count":1,"snapshot_sha256":"814523e2f1f25a11517bbeda5155d1552dd4a13cba822d9f19ffedf9aeebfee9"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T20:31:34.712990Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T20:31:19.065208Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"cited_work_retraction","ran_at":"2026-05-19T19:51:57.629363Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"citation_quote_validity","ran_at":"2026-05-19T19:50:12.037212Z","status":"skipped","version":"0.1.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T18:41:56.226496Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.312333Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.16966/integrity.json","findings":[],"snapshot_sha256":"43877f2a32cb4d2746a5eb0e4d6233da50de7e5c73a4194c76b1884cc0e12318","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Solving inverse partial differential equation (PDE) problems is a fundamental topic in scientific research due to its broad significance across a wide range of real-world applications. Inverse PDE problems arise across medical imaging, geophysics, materials science, and aerodynamics, where the goal is to infer hidden causes, design structures, or control physical states. In this paper, we provide a comprehensive review of recent advances in solving inverse PDE problems using artificial intelligence (AI). We first introduce the basic formulation, key challenges, and traditional numerical founda","authors_text":"Boyi Zou, Gang Bao, Mingsheng Long, Yi Yang, Yuze Hao, Zhentao Tan","cross_cats":[],"headline":"AI methods are reshaping inverse PDE problems by organizing them into unified categories of inference, design, and control.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-16T12:41:36Z","title":"Harnessing AI for Inverse Partial Differential Equation Problems: Past, Present, and Prospects"},"references":{"count":278,"internal_anchors":8,"resolved_work":278,"sample":[{"cited_arxiv_id":"1603.04467","doi":"","is_internal_anchor":true,"ref_index":1,"title":"TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems","work_id":"91f3c09e-dae6-48ca-80c0-463dd1b1f6e1","year":2016},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Robert Acar. 1993. Identification of the coefficient in elliptic equations.SIAM journal on control and optimization31, 5 (1993), 1221–1244","work_id":"f2b05fdd-49c7-442c-ac72-6646091dafcd","year":1993},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Gabriel Achour, Woong Je Sung, Olivia J Pinon-Fischer, and Dimitri N Mavris. 2020. Development of a conditional generative adversarial network for airfoil shape optimization. InAIAA Scitech 2020 Forum","work_id":"c6ca3d1d-d2e6-4f6c-9680-39650e5bb7fc","year":2020},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Grégoire Allaire, François Jouve, and Anca-Maria Toader. 2004. Structural optimization using sensitivity analysis and a level-set method.Journal of computational physics194, 1 (2004), 363–393","work_id":"e27a202d-871f-4e18-a5b6-15c644d74f81","year":2004},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Kelsey Allen, Tatiana Lopez-Guevara, Kimberly L Stachenfeld, Alvaro Sanchez Gonzalez, Peter Battaglia, Jessica B Hamrick, and Tobias Pfaff","work_id":"efbccf43-8192-41d6-b9ff-9d70f91cdf10","year":null}],"snapshot_sha256":"f4ecf9ee2e7b9d3e5a8e662c23511eb74331cedeb34fe79c7ac4cf1695d83ffc"},"source":{"id":"2605.16966","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T20:24:42.551964Z","id":"46dbde80-5fa3-44e6-b0ab-ec48f5a82824","model_set":{"reader":"grok-4.3"},"one_line_summary":"A survey organizing AI methods for inverse PDE problems into inverse problems, inverse design, and control categories, covering applications and future challenges like physics-informed models and uncertainty quantification.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"AI methods are reshaping inverse PDE problems by organizing them into unified categories of inference, design, and control.","strongest_claim":"This survey aims to provide the first unified and systematic perspective on AI for inverse PDE problems, demonstrating how modern learning-based methods are reshaping inverse problems, inverse design, and control problems in PDE-governed systems.","weakest_assumption":"The assumption that the chosen methodological paradigms and representative state-of-the-art approaches from recent years, along with the three-category organization, sufficiently capture and structure the full breadth of advances in the field."}},"verdict_id":"46dbde80-5fa3-44e6-b0ab-ec48f5a82824"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3ba7e4aa938b4e9f06514dbfb81e78070c18589326b1eec00b3dfe7e6b940a4f","target":"record","created_at":"2026-05-20T00:03:33Z","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":"3aa843417a6454cdcf23c8c4ca4ba6713bc7885656780d45e3750c5adc860fdb","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-16T12:41:36Z","title_canon_sha256":"c42baf6b69628429fc79badf11c7be870d9a6335808543e8a7e1a84078e6674d"},"schema_version":"1.0","source":{"id":"2605.16966","kind":"arxiv","version":1}},"canonical_sha256":"125fec19768a35897d59b398cd042cc88ee17a0658fc0c665ccdf989d9731953","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"125fec19768a35897d59b398cd042cc88ee17a0658fc0c665ccdf989d9731953","first_computed_at":"2026-05-20T00:03:33.451045Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:03:33.451045Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ufSngrVbJ3dunsCsWlydNEu9iOmFh11d/Co6HnEtltnAw9/U4NXUqeTgbH/u835nYnncODHWAl17+9PKpY6XAQ==","signature_status":"signed_v1","signed_at":"2026-05-20T00:03:33.451822Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.16966","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3ba7e4aa938b4e9f06514dbfb81e78070c18589326b1eec00b3dfe7e6b940a4f","sha256:70536e5c3d40dc44a77e7fb0557132b8c899145316a1eb985332e02659c6fea4"],"state_sha256":"4b55f00b3cf7b1a07cbd47a0c70dec2301fda91ef4ace0735e3bfe80920e072c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ahaT/BhYcXpFP+diih1QcMRk6OhDiNCTTB/E0JDIiCw/ROtC3Ej8/q9VPrDEeyhiHbjOwU1JCw2NbRXZ9/+5AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-21T21:41:41.704862Z","bundle_sha256":"97059e78dc5bec3a3328cf9e3b3f5f875c377f37e357581b36f3668b58244b7d"}}