{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:UKKBYDNJF5SZAW3DCGCYIEGUJT","short_pith_number":"pith:UKKBYDNJ","canonical_record":{"source":{"id":"2605.14309","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-14T03:22:12Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"4d0b7cc78c52ec7a3ca78468448974d19ad064808de7e286312404d4b3a69c8a","abstract_canon_sha256":"99c9dc26a4ca38913bd9b44aebe2d10028cfc4139aff60b8fa3bda69419b7327"},"schema_version":"1.0"},"canonical_sha256":"a2941c0da92f65905b6311858410d44cc8efdce34fef30be70d3bcac65a4ed5c","source":{"kind":"arxiv","id":"2605.14309","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.14309","created_at":"2026-05-17T23:39:10Z"},{"alias_kind":"arxiv_version","alias_value":"2605.14309v1","created_at":"2026-05-17T23:39:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.14309","created_at":"2026-05-17T23:39:10Z"},{"alias_kind":"pith_short_12","alias_value":"UKKBYDNJF5SZ","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"UKKBYDNJF5SZAW3D","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"UKKBYDNJ","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:UKKBYDNJF5SZAW3DCGCYIEGUJT","target":"record","payload":{"canonical_record":{"source":{"id":"2605.14309","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-14T03:22:12Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"4d0b7cc78c52ec7a3ca78468448974d19ad064808de7e286312404d4b3a69c8a","abstract_canon_sha256":"99c9dc26a4ca38913bd9b44aebe2d10028cfc4139aff60b8fa3bda69419b7327"},"schema_version":"1.0"},"canonical_sha256":"a2941c0da92f65905b6311858410d44cc8efdce34fef30be70d3bcac65a4ed5c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:10.015270Z","signature_b64":"EZPe3subbIfRUIZv7dmCRAyBdLl1GrEAGVZFSjrTYrmT2gZ/WQxIunnIvakdjqpWuCilZs4amW6rSSqgplQgAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a2941c0da92f65905b6311858410d44cc8efdce34fef30be70d3bcac65a4ed5c","last_reissued_at":"2026-05-17T23:39:10.014658Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:10.014658Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.14309","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":"2026-05-17T23:39:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"75aGIY0o3toq4yDhRWbiwd6fBQ0zE4Vcx3x/NvVewa6VNRnkpphYd5UUr/ViTqcK0DxP6S8frUUMji8iKdwLDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T02:46:30.247787Z"},"content_sha256":"76e47334e4fe6a7665499b33aab878ec1515b6f30967f114276bcc60d297e955","schema_version":"1.0","event_id":"sha256:76e47334e4fe6a7665499b33aab878ec1515b6f30967f114276bcc60d297e955"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:UKKBYDNJF5SZAW3DCGCYIEGUJT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ICED: Concept-level Machine Unlearning via Interpretable Concept Decomposition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Vision-language models can unlearn specific concepts by decomposing images into sparse semantic combinations and suppressing only the targets.","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Jing Lin, Junhao Dong, Li Xu, Piotr Koniusz, Shen Lin","submitted_at":"2026-05-14T03:22:12Z","abstract_excerpt":"Machine unlearning in Vision-Language Models (VLMs) is typically performed at the image or instance level, making it difficult to precisely remove target knowledge without affecting unrelated semantics. This issue is especially pronounced since a single image often contains multiple entangled concepts, including both target concepts to be forgotten and contextual information that should be preserved. In this paper, we propose an interpretable concept-level unlearning framework for VLMs, which constructs a compact task-specific concept vocabulary from the forgetting set using a multimodal large"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Based on this decomposition, our method formulates unlearning as concept-level optimization, where target concepts are selectively suppressed while intra-instance non-target semantics and global cross-modal knowledge are preserved. Extensive experiments across both in-domain and out-of-domain forgetting settings demonstrate that our method enables more comprehensive target forgetting, better preserves non-target knowledge within the same image, and maintains competitive model utility compared with existing VLM unlearning methods.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"Visual representations can be decomposed into sparse, nonnegative combinations of semantic concepts from a compact task-specific vocabulary, providing an explicit interface for fine-grained knowledge manipulation without affecting unrelated semantics.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"ICED decomposes visual features into interpretable concepts to enable selective unlearning of target knowledge in VLMs while preserving non-target semantics and model utility.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Vision-language models can unlearn specific concepts by decomposing images into sparse semantic combinations and suppressing only the targets.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"db919c7e5a568620f817eb08713d7c12cba22118ba29b425831a2cb1ceaf87ca"},"source":{"id":"2605.14309","kind":"arxiv","version":1},"verdict":{"id":"23c34ff8-855c-49c4-8a73-2293c2ef291b","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T02:32:10.764681Z","strongest_claim":"Based on this decomposition, our method formulates unlearning as concept-level optimization, where target concepts are selectively suppressed while intra-instance non-target semantics and global cross-modal knowledge are preserved. Extensive experiments across both in-domain and out-of-domain forgetting settings demonstrate that our method enables more comprehensive target forgetting, better preserves non-target knowledge within the same image, and maintains competitive model utility compared with existing VLM unlearning methods.","one_line_summary":"ICED decomposes visual features into interpretable concepts to enable selective unlearning of target knowledge in VLMs while preserving non-target semantics and model utility.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"Visual representations can be decomposed into sparse, nonnegative combinations of semantic concepts from a compact task-specific vocabulary, providing an explicit interface for fine-grained knowledge manipulation without affecting unrelated semantics.","pith_extraction_headline":"Vision-language models can unlearn specific concepts by decomposing images into sparse semantic combinations and suppressing only the targets."},"references":{"count":36,"sample":[{"doi":"","year":2021,"title":"Learning transferable visual models from natural language supervi- sion,","work_id":"07a68397-0cae-4b42-869b-feb434d4e372","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Trustworthy ai: From principles to practices,","work_id":"9cd10867-cc25-4e72-9c4b-3fda01197bb4","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2026,"title":"Allies teach better than enemies: Inverse adversaries for robust knowledge distillation,","work_id":"99e74045-d1e0-4c83-bb1b-2126f4f62a06","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2026,"title":"Tug-of-war no more: Harmonizing accuracy and robustness in vision-language models via stability-aware task vector merging,","work_id":"60817445-b214-4c28-94e2-95d4fe5a95af","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Can bad teaching induce forgetting? unlearning in deep networks using an incompetent teacher,","work_id":"27395386-03c5-4bf5-a86e-72ffb49e0842","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":36,"snapshot_sha256":"16be0492b35438dfacab3605b2dddac45c89e69c539b310d148263b0ac3a14b2","internal_anchors":0},"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":"23c34ff8-855c-49c4-8a73-2293c2ef291b"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:39:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NQ4crjcamXIFy/8nMIW4bFVdoHlRvBpxK1hONISx/KXzR86Z5i++8OQT8qGTnONeZ1ev7Jn8pquCWa1RoeB/Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T02:46:30.248701Z"},"content_sha256":"e187697a1de49d8f3f41f833cf0b67f365b0d89c23bc175290d378e5288cde9a","schema_version":"1.0","event_id":"sha256:e187697a1de49d8f3f41f833cf0b67f365b0d89c23bc175290d378e5288cde9a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UKKBYDNJF5SZAW3DCGCYIEGUJT/bundle.json","state_url":"https://pith.science/pith/UKKBYDNJF5SZAW3DCGCYIEGUJT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UKKBYDNJF5SZAW3DCGCYIEGUJT/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":"2026-05-25T02:46:30Z","links":{"resolver":"https://pith.science/pith/UKKBYDNJF5SZAW3DCGCYIEGUJT","bundle":"https://pith.science/pith/UKKBYDNJF5SZAW3DCGCYIEGUJT/bundle.json","state":"https://pith.science/pith/UKKBYDNJF5SZAW3DCGCYIEGUJT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UKKBYDNJF5SZAW3DCGCYIEGUJT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:UKKBYDNJF5SZAW3DCGCYIEGUJT","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":"99c9dc26a4ca38913bd9b44aebe2d10028cfc4139aff60b8fa3bda69419b7327","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-14T03:22:12Z","title_canon_sha256":"4d0b7cc78c52ec7a3ca78468448974d19ad064808de7e286312404d4b3a69c8a"},"schema_version":"1.0","source":{"id":"2605.14309","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.14309","created_at":"2026-05-17T23:39:10Z"},{"alias_kind":"arxiv_version","alias_value":"2605.14309v1","created_at":"2026-05-17T23:39:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.14309","created_at":"2026-05-17T23:39:10Z"},{"alias_kind":"pith_short_12","alias_value":"UKKBYDNJF5SZ","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"UKKBYDNJF5SZAW3D","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"UKKBYDNJ","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:e187697a1de49d8f3f41f833cf0b67f365b0d89c23bc175290d378e5288cde9a","target":"graph","created_at":"2026-05-17T23:39:10Z","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":"Based on this decomposition, our method formulates unlearning as concept-level optimization, where target concepts are selectively suppressed while intra-instance non-target semantics and global cross-modal knowledge are preserved. Extensive experiments across both in-domain and out-of-domain forgetting settings demonstrate that our method enables more comprehensive target forgetting, better preserves non-target knowledge within the same image, and maintains competitive model utility compared with existing VLM unlearning methods."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"Visual representations can be decomposed into sparse, nonnegative combinations of semantic concepts from a compact task-specific vocabulary, providing an explicit interface for fine-grained knowledge manipulation without affecting unrelated semantics."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"ICED decomposes visual features into interpretable concepts to enable selective unlearning of target knowledge in VLMs while preserving non-target semantics and model utility."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Vision-language models can unlearn specific concepts by decomposing images into sparse semantic combinations and suppressing only the targets."}],"snapshot_sha256":"db919c7e5a568620f817eb08713d7c12cba22118ba29b425831a2cb1ceaf87ca"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Machine unlearning in Vision-Language Models (VLMs) is typically performed at the image or instance level, making it difficult to precisely remove target knowledge without affecting unrelated semantics. This issue is especially pronounced since a single image often contains multiple entangled concepts, including both target concepts to be forgotten and contextual information that should be preserved. In this paper, we propose an interpretable concept-level unlearning framework for VLMs, which constructs a compact task-specific concept vocabulary from the forgetting set using a multimodal large","authors_text":"Jing Lin, Junhao Dong, Li Xu, Piotr Koniusz, Shen Lin","cross_cats":["cs.AI","cs.LG"],"headline":"Vision-language models can unlearn specific concepts by decomposing images into sparse semantic combinations and suppressing only the targets.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-14T03:22:12Z","title":"ICED: Concept-level Machine Unlearning via Interpretable Concept Decomposition"},"references":{"count":36,"internal_anchors":0,"resolved_work":36,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Learning transferable visual models from natural language supervi- sion,","work_id":"07a68397-0cae-4b42-869b-feb434d4e372","year":2021},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Trustworthy ai: From principles to practices,","work_id":"9cd10867-cc25-4e72-9c4b-3fda01197bb4","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Allies teach better than enemies: Inverse adversaries for robust knowledge distillation,","work_id":"99e74045-d1e0-4c83-bb1b-2126f4f62a06","year":2026},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Tug-of-war no more: Harmonizing accuracy and robustness in vision-language models via stability-aware task vector merging,","work_id":"60817445-b214-4c28-94e2-95d4fe5a95af","year":2026},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Can bad teaching induce forgetting? unlearning in deep networks using an incompetent teacher,","work_id":"27395386-03c5-4bf5-a86e-72ffb49e0842","year":2023}],"snapshot_sha256":"16be0492b35438dfacab3605b2dddac45c89e69c539b310d148263b0ac3a14b2"},"source":{"id":"2605.14309","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-15T02:32:10.764681Z","id":"23c34ff8-855c-49c4-8a73-2293c2ef291b","model_set":{"reader":"grok-4.3"},"one_line_summary":"ICED decomposes visual features into interpretable concepts to enable selective unlearning of target knowledge in VLMs while preserving non-target semantics and model utility.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Vision-language models can unlearn specific concepts by decomposing images into sparse semantic combinations and suppressing only the targets.","strongest_claim":"Based on this decomposition, our method formulates unlearning as concept-level optimization, where target concepts are selectively suppressed while intra-instance non-target semantics and global cross-modal knowledge are preserved. Extensive experiments across both in-domain and out-of-domain forgetting settings demonstrate that our method enables more comprehensive target forgetting, better preserves non-target knowledge within the same image, and maintains competitive model utility compared with existing VLM unlearning methods.","weakest_assumption":"Visual representations can be decomposed into sparse, nonnegative combinations of semantic concepts from a compact task-specific vocabulary, providing an explicit interface for fine-grained knowledge manipulation without affecting unrelated semantics."}},"verdict_id":"23c34ff8-855c-49c4-8a73-2293c2ef291b"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:76e47334e4fe6a7665499b33aab878ec1515b6f30967f114276bcc60d297e955","target":"record","created_at":"2026-05-17T23:39:10Z","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":"99c9dc26a4ca38913bd9b44aebe2d10028cfc4139aff60b8fa3bda69419b7327","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-14T03:22:12Z","title_canon_sha256":"4d0b7cc78c52ec7a3ca78468448974d19ad064808de7e286312404d4b3a69c8a"},"schema_version":"1.0","source":{"id":"2605.14309","kind":"arxiv","version":1}},"canonical_sha256":"a2941c0da92f65905b6311858410d44cc8efdce34fef30be70d3bcac65a4ed5c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a2941c0da92f65905b6311858410d44cc8efdce34fef30be70d3bcac65a4ed5c","first_computed_at":"2026-05-17T23:39:10.014658Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:39:10.014658Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EZPe3subbIfRUIZv7dmCRAyBdLl1GrEAGVZFSjrTYrmT2gZ/WQxIunnIvakdjqpWuCilZs4amW6rSSqgplQgAg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:39:10.015270Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.14309","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:76e47334e4fe6a7665499b33aab878ec1515b6f30967f114276bcc60d297e955","sha256:e187697a1de49d8f3f41f833cf0b67f365b0d89c23bc175290d378e5288cde9a"],"state_sha256":"d5f0e9304d000d066e208954344f3de404942335a324edbe53619c278f999bd5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"usex0lwbUSAGueo/euF/h4kUIxjTLtx4I5vFRH8ur3Y5fhnwETlZDcC1UYxlJ0EQeiVdt1Z8qqz/+Qcaff6GDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T02:46:30.252632Z","bundle_sha256":"b3670f87491f565a57bc3d566922a3faae60ef74413f595409a61521aa4517cc"}}