{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:5BWLUGJFIWJUYIIVLYW3DPPQJP","short_pith_number":"pith:5BWLUGJF","canonical_record":{"source":{"id":"2605.16928","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-16T10:51:58Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"eb90e3c553c5f824f187993fb0f75f6152125c643a405384461d90826ccf1567","abstract_canon_sha256":"5503d7ff56dea961efbb7aa31cfe7fa14d7c260239051500288a33744cc2cb34"},"schema_version":"1.0"},"canonical_sha256":"e86cba192545934c21155e2db1bdf04bce33534c9fdd005f3e77d76b9e75406e","source":{"kind":"arxiv","id":"2605.16928","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16928","created_at":"2026-05-20T00:03:31Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16928v1","created_at":"2026-05-20T00:03:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16928","created_at":"2026-05-20T00:03:31Z"},{"alias_kind":"pith_short_12","alias_value":"5BWLUGJFIWJU","created_at":"2026-05-20T00:03:31Z"},{"alias_kind":"pith_short_16","alias_value":"5BWLUGJFIWJUYIIV","created_at":"2026-05-20T00:03:31Z"},{"alias_kind":"pith_short_8","alias_value":"5BWLUGJF","created_at":"2026-05-20T00:03:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:5BWLUGJFIWJUYIIVLYW3DPPQJP","target":"record","payload":{"canonical_record":{"source":{"id":"2605.16928","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-16T10:51:58Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"eb90e3c553c5f824f187993fb0f75f6152125c643a405384461d90826ccf1567","abstract_canon_sha256":"5503d7ff56dea961efbb7aa31cfe7fa14d7c260239051500288a33744cc2cb34"},"schema_version":"1.0"},"canonical_sha256":"e86cba192545934c21155e2db1bdf04bce33534c9fdd005f3e77d76b9e75406e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:03:31.257840Z","signature_b64":"C4hF0cAyKkJ7jAahtVGgYHOip94mYbMRYrnVhf6WAq+YcriZsJuyjVHTdKS+JQPfdV9k3WLBQC6CbyHnO8/RDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e86cba192545934c21155e2db1bdf04bce33534c9fdd005f3e77d76b9e75406e","last_reissued_at":"2026-05-20T00:03:31.256999Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:03:31.256999Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.16928","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":"2026-05-20T00:03:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uOvdZLhSXS0HL31EkrXirNbzQjk7CPDRPvFR2SD75Nq7ekoX57pJc/YXTVgi5Mx0BXdAQ/S13NyIcECoQY1UDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T21:47:25.385133Z"},"content_sha256":"972d77bffee3e87aa181dec7aa11320e33f67edb1f551a45b591eb35318475e8","schema_version":"1.0","event_id":"sha256:972d77bffee3e87aa181dec7aa11320e33f67edb1f551a45b591eb35318475e8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:5BWLUGJFIWJUYIIVLYW3DPPQJP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Full Attention Strikes Back: Transferring Full Attention into Sparse within Hundred Training Steps","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Full-attention LLMs already contain the structure to become highly sparse models after only a few hundred training steps.","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Hanlin Tang, Kan Liu, Lan Tao, Lin Qu, Maohua Li, Xiaoxing Ma, Yanke Zhou, Yiduo Li, Yuan Yao","submitted_at":"2026-05-16T10:51:58Z","abstract_excerpt":"Long-context inference in large language models is bottlenecked by the quadratic cost of full attention. Existing efficient alternatives often rely either on native sparse training or on heuristic token eviction, creating an undesirable trade-off among efficiency, training cost, and accuracy. In this work, we show that full-attention LLMs are already intrinsically sparse and can be transformed into highly sparse models with only minimal adaptation. Our approach is built on three observations: (1) only a small subset of attention heads truly requires full long-context processing; (2) long-range"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"full-attention LLMs are already intrinsically sparse and can be transformed into highly sparse models with only minimal adaptation","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"only a small subset of attention heads truly requires full long-context processing and long-range retrieval is governed primarily by a low-dimensional subspace","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"RTPurbo exploits intrinsic sparsity in full-attention LLMs to achieve near-lossless sparse inference after only a few hundred training steps via retrieval-head identification and a lightweight token indexer.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Full-attention LLMs already contain the structure to become highly sparse models after only a few hundred training steps.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"8a853c9dd9f01bc9c60c98712d7f86913bfd6ff6e99e099ed63aab1dc94ef1e1"},"source":{"id":"2605.16928","kind":"arxiv","version":1},"verdict":{"id":"a800a96d-e4d7-430c-996d-210eeb65b962","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T20:47:59.513839Z","strongest_claim":"full-attention LLMs are already intrinsically sparse and can be transformed into highly sparse models with only minimal adaptation","one_line_summary":"RTPurbo exploits intrinsic sparsity in full-attention LLMs to achieve near-lossless sparse inference after only a few hundred training steps via retrieval-head identification and a lightweight token indexer.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"only a small subset of attention heads truly requires full long-context processing and long-range retrieval is governed primarily by a low-dimensional subspace","pith_extraction_headline":"Full-attention LLMs already contain the structure to become highly sparse models after only a few hundred training steps."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.16928/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T21:01:19.145891Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T21:00:55.151225Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"cited_work_retraction","ran_at":"2026-05-19T20:22:26.443157Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T18:41:56.256340Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.337554Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"118f7c629435869cc70a9577923d0eb43776f0f87e34302a1cfcc4523d315135"},"references":{"count":34,"sample":[{"doi":"10.18653/v1/2024.acl-long.172","year":2024,"title":"In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Aug 2024).https://doi.org/10.18653/v1/2024.acl-long.172","work_id":"334e52dd-5eee-49da-94cd-26c0028afe5c","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"Matharena: Evaluating llms on uncontaminated math competitions, February 2025","work_id":"3130a6c6-5158-45d2-858b-1029c80a84d2","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities","work_id":"008df105-2fdd-45d8-857a-8e35868aecb6","ref_index":3,"cited_arxiv_id":"2507.06261","is_internal_anchor":true},{"doi":"","year":2024,"title":"Flashattention-2: Faster attention with better parallelism and work partitioning","work_id":"d5b497f2-cee7-428b-a014-c73669805b4e","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models","work_id":"07c85cc5-4086-4abc-823b-6d0f4ff784d0","ref_index":5,"cited_arxiv_id":"2512.02556","is_internal_anchor":true}],"resolved_work":34,"snapshot_sha256":"c966be9a80c34dd3ce947a11fb1261cb254d29cd3e99aded899dd30630de89c0","internal_anchors":9},"formal_canon":{"evidence_count":2,"snapshot_sha256":"2e367f193b1df7126583b953acdd1e20f4e892888551f1c71c1aa47dfcbd6b02"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"a800a96d-e4d7-430c-996d-210eeb65b962"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:03:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TNF2L98LXYy5Y+K5ed6bYG9A4GhPG5oDzVJZHPBrqdP8Kgfa1mVcgwgb0Y+y+7yUUOZFxS/EzxTre4Rr4hZLBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T21:47:25.385839Z"},"content_sha256":"6267f66b274c7af530dad984849ef0c63b0edfe96ce7ba560ffbfcc96ea79465","schema_version":"1.0","event_id":"sha256:6267f66b274c7af530dad984849ef0c63b0edfe96ce7ba560ffbfcc96ea79465"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5BWLUGJFIWJUYIIVLYW3DPPQJP/bundle.json","state_url":"https://pith.science/pith/5BWLUGJFIWJUYIIVLYW3DPPQJP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5BWLUGJFIWJUYIIVLYW3DPPQJP/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":"2026-05-27T21:47:25Z","links":{"resolver":"https://pith.science/pith/5BWLUGJFIWJUYIIVLYW3DPPQJP","bundle":"https://pith.science/pith/5BWLUGJFIWJUYIIVLYW3DPPQJP/bundle.json","state":"https://pith.science/pith/5BWLUGJFIWJUYIIVLYW3DPPQJP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5BWLUGJFIWJUYIIVLYW3DPPQJP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:5BWLUGJFIWJUYIIVLYW3DPPQJP","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":"5503d7ff56dea961efbb7aa31cfe7fa14d7c260239051500288a33744cc2cb34","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-16T10:51:58Z","title_canon_sha256":"eb90e3c553c5f824f187993fb0f75f6152125c643a405384461d90826ccf1567"},"schema_version":"1.0","source":{"id":"2605.16928","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16928","created_at":"2026-05-20T00:03:31Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16928v1","created_at":"2026-05-20T00:03:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16928","created_at":"2026-05-20T00:03:31Z"},{"alias_kind":"pith_short_12","alias_value":"5BWLUGJFIWJU","created_at":"2026-05-20T00:03:31Z"},{"alias_kind":"pith_short_16","alias_value":"5BWLUGJFIWJUYIIV","created_at":"2026-05-20T00:03:31Z"},{"alias_kind":"pith_short_8","alias_value":"5BWLUGJF","created_at":"2026-05-20T00:03:31Z"}],"graph_snapshots":[{"event_id":"sha256:6267f66b274c7af530dad984849ef0c63b0edfe96ce7ba560ffbfcc96ea79465","target":"graph","created_at":"2026-05-20T00:03:31Z","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":"full-attention LLMs are already intrinsically sparse and can be transformed into highly sparse models with only minimal adaptation"},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"only a small subset of attention heads truly requires full long-context processing and long-range retrieval is governed primarily by a low-dimensional subspace"},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"RTPurbo exploits intrinsic sparsity in full-attention LLMs to achieve near-lossless sparse inference after only a few hundred training steps via retrieval-head identification and a lightweight token indexer."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Full-attention LLMs already contain the structure to become highly sparse models after only a few hundred training steps."}],"snapshot_sha256":"8a853c9dd9f01bc9c60c98712d7f86913bfd6ff6e99e099ed63aab1dc94ef1e1"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"2e367f193b1df7126583b953acdd1e20f4e892888551f1c71c1aa47dfcbd6b02"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T21:01:19.145891Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T21:00:55.151225Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"cited_work_retraction","ran_at":"2026-05-19T20:22:26.443157Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T18:41:56.256340Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.337554Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.16928/integrity.json","findings":[],"snapshot_sha256":"118f7c629435869cc70a9577923d0eb43776f0f87e34302a1cfcc4523d315135","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Long-context inference in large language models is bottlenecked by the quadratic cost of full attention. Existing efficient alternatives often rely either on native sparse training or on heuristic token eviction, creating an undesirable trade-off among efficiency, training cost, and accuracy. In this work, we show that full-attention LLMs are already intrinsically sparse and can be transformed into highly sparse models with only minimal adaptation. Our approach is built on three observations: (1) only a small subset of attention heads truly requires full long-context processing; (2) long-range","authors_text":"Hanlin Tang, Kan Liu, Lan Tao, Lin Qu, Maohua Li, Xiaoxing Ma, Yanke Zhou, Yiduo Li, Yuan Yao","cross_cats":["cs.AI"],"headline":"Full-attention LLMs already contain the structure to become highly sparse models after only a few hundred training steps.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-16T10:51:58Z","title":"Full Attention Strikes Back: Transferring Full Attention into Sparse within Hundred Training Steps"},"references":{"count":34,"internal_anchors":9,"resolved_work":34,"sample":[{"cited_arxiv_id":"","doi":"10.18653/v1/2024.acl-long.172","is_internal_anchor":false,"ref_index":1,"title":"In: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Aug 2024).https://doi.org/10.18653/v1/2024.acl-long.172","work_id":"334e52dd-5eee-49da-94cd-26c0028afe5c","year":2024},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Matharena: Evaluating llms on uncontaminated math competitions, February 2025","work_id":"3130a6c6-5158-45d2-858b-1029c80a84d2","year":2025},{"cited_arxiv_id":"2507.06261","doi":"","is_internal_anchor":true,"ref_index":3,"title":"Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities","work_id":"008df105-2fdd-45d8-857a-8e35868aecb6","year":2025},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Flashattention-2: Faster attention with better parallelism and work partitioning","work_id":"d5b497f2-cee7-428b-a014-c73669805b4e","year":2024},{"cited_arxiv_id":"2512.02556","doi":"","is_internal_anchor":true,"ref_index":5,"title":"DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models","work_id":"07c85cc5-4086-4abc-823b-6d0f4ff784d0","year":2025}],"snapshot_sha256":"c966be9a80c34dd3ce947a11fb1261cb254d29cd3e99aded899dd30630de89c0"},"source":{"id":"2605.16928","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T20:47:59.513839Z","id":"a800a96d-e4d7-430c-996d-210eeb65b962","model_set":{"reader":"grok-4.3"},"one_line_summary":"RTPurbo exploits intrinsic sparsity in full-attention LLMs to achieve near-lossless sparse inference after only a few hundred training steps via retrieval-head identification and a lightweight token indexer.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Full-attention LLMs already contain the structure to become highly sparse models after only a few hundred training steps.","strongest_claim":"full-attention LLMs are already intrinsically sparse and can be transformed into highly sparse models with only minimal adaptation","weakest_assumption":"only a small subset of attention heads truly requires full long-context processing and long-range retrieval is governed primarily by a low-dimensional subspace"}},"verdict_id":"a800a96d-e4d7-430c-996d-210eeb65b962"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:972d77bffee3e87aa181dec7aa11320e33f67edb1f551a45b591eb35318475e8","target":"record","created_at":"2026-05-20T00:03:31Z","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":"5503d7ff56dea961efbb7aa31cfe7fa14d7c260239051500288a33744cc2cb34","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-16T10:51:58Z","title_canon_sha256":"eb90e3c553c5f824f187993fb0f75f6152125c643a405384461d90826ccf1567"},"schema_version":"1.0","source":{"id":"2605.16928","kind":"arxiv","version":1}},"canonical_sha256":"e86cba192545934c21155e2db1bdf04bce33534c9fdd005f3e77d76b9e75406e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e86cba192545934c21155e2db1bdf04bce33534c9fdd005f3e77d76b9e75406e","first_computed_at":"2026-05-20T00:03:31.256999Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:03:31.256999Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"C4hF0cAyKkJ7jAahtVGgYHOip94mYbMRYrnVhf6WAq+YcriZsJuyjVHTdKS+JQPfdV9k3WLBQC6CbyHnO8/RDQ==","signature_status":"signed_v1","signed_at":"2026-05-20T00:03:31.257840Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.16928","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:972d77bffee3e87aa181dec7aa11320e33f67edb1f551a45b591eb35318475e8","sha256:6267f66b274c7af530dad984849ef0c63b0edfe96ce7ba560ffbfcc96ea79465"],"state_sha256":"8e49cd3f8852baacbf320b82782fd0e80f81d2ebad888e2b59067e7a62415002"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wUNMPJ0ARmOkvJrmOWXU4K3vHsfkVTV/V55otjSbdzuvqkVMtwgU01rHLuVGp30Mdr0g29Eae74r1cNgMCaiDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T21:47:25.389109Z","bundle_sha256":"3b525260c5af86163b3e4eeea26e1b142457b5c3d6fb970b21c23f922cd45189"}}