{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:U2EG4SD7TAA3C43VDXXERV5XCI","short_pith_number":"pith:U2EG4SD7","canonical_record":{"source":{"id":"2601.10611","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-01-15T17:27:44Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"022f070504b46fd82cbb60fe0bbb437f4b6ac38bd99c2665129cc2fb9264b0a8","abstract_canon_sha256":"612d9d70fa3a6f07357f20ad70e1ab464b2cb9ad404f435ba2b2c6f3892b161e"},"schema_version":"1.0"},"canonical_sha256":"a6886e487f9801b173751dee48d7b71230f215da053978ff77cadc84343bbbef","source":{"kind":"arxiv","id":"2601.10611","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2601.10611","created_at":"2026-05-17T23:38:49Z"},{"alias_kind":"arxiv_version","alias_value":"2601.10611v4","created_at":"2026-05-17T23:38:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.10611","created_at":"2026-05-17T23:38:49Z"},{"alias_kind":"pith_short_12","alias_value":"U2EG4SD7TAA3","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"U2EG4SD7TAA3C43V","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"U2EG4SD7","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:U2EG4SD7TAA3C43VDXXERV5XCI","target":"record","payload":{"canonical_record":{"source":{"id":"2601.10611","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-01-15T17:27:44Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"022f070504b46fd82cbb60fe0bbb437f4b6ac38bd99c2665129cc2fb9264b0a8","abstract_canon_sha256":"612d9d70fa3a6f07357f20ad70e1ab464b2cb9ad404f435ba2b2c6f3892b161e"},"schema_version":"1.0"},"canonical_sha256":"a6886e487f9801b173751dee48d7b71230f215da053978ff77cadc84343bbbef","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:38:49.132445Z","signature_b64":"wo3eVm7P6oc+Y0xlffFDPDOgPkIJL/FIFlJ+JsVZUjzriECbKRQqGB1YU+64UVfurdisDaxNKidHbhw8zDiQDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a6886e487f9801b173751dee48d7b71230f215da053978ff77cadc84343bbbef","last_reissued_at":"2026-05-17T23:38:49.131776Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:38:49.131776Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2601.10611","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:38:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"j+4C+OWObLdcdL5n/1lDLePgzhMynuGkejmPRH4y6pTIWIRtbcvfdCOAK9ab6FwzolyQbYHL25B1tFxEKK0LDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T21:39:25.275383Z"},"content_sha256":"d446c1bacd633d5a7dcf25739d6d6436a050d44e0b2be01dd94fa6c4543244e2","schema_version":"1.0","event_id":"sha256:d446c1bacd633d5a7dcf25739d6d6436a050d44e0b2be01dd94fa6c4543244e2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:U2EG4SD7TAA3C43VDXXERV5XCI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Molmo2 releases new open video and multi-image datasets plus a training recipe that lets an 8B model outperform other open-weight VLMs on video tasks and beat some proprietary models on pixel grounding.","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Ali Farhadi, Chris Dongjoo Kim, Christopher Clark, George Stoica, Jae Sung Park, Jianrui Zhang, Jieyu Zhang, Jitesh Jain, Mohammadreza Salehi, Ranjay Krishna, Rohun Tripathi, Sangho Lee, Taira Anderson, Vincent Shao, Weikai Huang, Winson Han, Yinuo Yang, Yue Yang, Zhongzheng Ren, Ziqi Gao, Zixian Ma","submitted_at":"2026-01-15T17:27:44Z","abstract_excerpt":"Today's strongest video-language models (VLMs) remain proprietary. The strongest open-weight models either rely on synthetic data from proprietary VLMs, effectively distilling from them, or do not disclose their training data or recipe. As a result, the open-source community lacks the foundations needed to improve on the state-of-the-art video (and image) language models. Crucially, many downstream applications require more than just high-level video understanding; they require grounding -- either by pointing or by tracking in pixels. Even proprietary models lack this capability. We present Mo"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Our best-in-class 8B model outperforms others in the class of open weight and data models on short videos, counting, and captioning, and is competitive on long-videos. On video-grounding Molmo2 significantly outperforms existing open-weight models like Qwen3-VL (35.5 vs 29.6 accuracy on video counting) and surpasses proprietary models like Gemini 3 Pro on some tasks (38.4 vs 20.0 F1 on video pointing and 56.2 vs 41.1 J&F on video tracking).","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The newly collected datasets are high-quality, diverse, and free of leakage or bias that would inflate the reported performance gains over baselines.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Molmo2 delivers state-of-the-art open-weight video VLMs with new grounding datasets and training methods that outperform prior open models and match or exceed some proprietary ones on pointing and tracking tasks.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Molmo2 releases new open video and multi-image datasets plus a training recipe that lets an 8B model outperform other open-weight VLMs on video tasks and beat some proprietary models on pixel grounding.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"bdbcb84be687d40fd997c4a186c94b04641f2627bc9cd35c7b026f6f096c5eb2"},"source":{"id":"2601.10611","kind":"arxiv","version":4},"verdict":{"id":"aa985fbe-36a9-478a-8bed-c9a87f81397f","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-16T04:12:07.474713Z","strongest_claim":"Our best-in-class 8B model outperforms others in the class of open weight and data models on short videos, counting, and captioning, and is competitive on long-videos. On video-grounding Molmo2 significantly outperforms existing open-weight models like Qwen3-VL (35.5 vs 29.6 accuracy on video counting) and surpasses proprietary models like Gemini 3 Pro on some tasks (38.4 vs 20.0 F1 on video pointing and 56.2 vs 41.1 J&F on video tracking).","one_line_summary":"Molmo2 delivers state-of-the-art open-weight video VLMs with new grounding datasets and training methods that outperform prior open models and match or exceed some proprietary ones on pointing and tracking tasks.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The newly collected datasets are high-quality, diverse, and free of leakage or bias that would inflate the reported performance gains over baselines.","pith_extraction_headline":"Molmo2 releases new open video and multi-image datasets plus a training recipe that lets an 8B model outperform other open-weight VLMs on video tasks and beat some proprietary models on pixel grounding."},"references":{"count":187,"sample":[{"doi":"","year":2025,"title":"Gemini Robotics 1.5: Pushing the Frontier of Generalist Robots with Advanced Embodied Reasoning, Thinking, and Motion Transfer","work_id":"ffe7b7f4-997b-4957-b253-03cfbebf6f9a","ref_index":1,"cited_arxiv_id":"2510.03342","is_internal_anchor":true},{"doi":"","year":2019,"title":"M. Acharya, K. Kafle, and C. Kanan. TallyQA: Answering complex counting questions. InAAAI, 2019","work_id":"5d814cf0-ca72-46d0-9ad4-9160ff898462","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"Videomolmo: Spatio-temporal grounding meets pointing","work_id":"11238605-33ed-4866-a7b0-1df17e186797","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"The Llama 3 Herd of Models","work_id":"1549a635-88af-4ac1-acfe-51ae7bb53345","ref_index":4,"cited_arxiv_id":"2407.21783","is_internal_anchor":true},{"doi":"","year":2025,"title":"Claude sonnet 4.5 system card, 2025","work_id":"778b4be6-60c8-4ac2-99f9-0bea4eee47d4","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":187,"snapshot_sha256":"0dd5d113efb39a6e735a8af64c6e22a8e6c460c5fd00e879f868e661bb13355c","internal_anchors":27},"formal_canon":{"evidence_count":2,"snapshot_sha256":"09446e6cf4991e268627c96abd0fbde38ca28bc0733d713a1bee0fd8789685b1"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"aa985fbe-36a9-478a-8bed-c9a87f81397f"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:38:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7nK3AecRJ1OOad40o4dwAx/xyg3pGnuFlIcmDPZlN7y/3BvzoAdxeSQR3Fc5zdhJrA+cb2J3m9Hhf6pC3mU3BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T21:39:25.276357Z"},"content_sha256":"0aebd5b44ddf55668ef8084d757eacb1c6e30e3749d19baee21d5aed161d73df","schema_version":"1.0","event_id":"sha256:0aebd5b44ddf55668ef8084d757eacb1c6e30e3749d19baee21d5aed161d73df"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/U2EG4SD7TAA3C43VDXXERV5XCI/bundle.json","state_url":"https://pith.science/pith/U2EG4SD7TAA3C43VDXXERV5XCI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/U2EG4SD7TAA3C43VDXXERV5XCI/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":"2026-06-06T21:39:25Z","links":{"resolver":"https://pith.science/pith/U2EG4SD7TAA3C43VDXXERV5XCI","bundle":"https://pith.science/pith/U2EG4SD7TAA3C43VDXXERV5XCI/bundle.json","state":"https://pith.science/pith/U2EG4SD7TAA3C43VDXXERV5XCI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/U2EG4SD7TAA3C43VDXXERV5XCI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:U2EG4SD7TAA3C43VDXXERV5XCI","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":"612d9d70fa3a6f07357f20ad70e1ab464b2cb9ad404f435ba2b2c6f3892b161e","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-01-15T17:27:44Z","title_canon_sha256":"022f070504b46fd82cbb60fe0bbb437f4b6ac38bd99c2665129cc2fb9264b0a8"},"schema_version":"1.0","source":{"id":"2601.10611","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2601.10611","created_at":"2026-05-17T23:38:49Z"},{"alias_kind":"arxiv_version","alias_value":"2601.10611v4","created_at":"2026-05-17T23:38:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.10611","created_at":"2026-05-17T23:38:49Z"},{"alias_kind":"pith_short_12","alias_value":"U2EG4SD7TAA3","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"U2EG4SD7TAA3C43V","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"U2EG4SD7","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:0aebd5b44ddf55668ef8084d757eacb1c6e30e3749d19baee21d5aed161d73df","target":"graph","created_at":"2026-05-17T23:38:49Z","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 best-in-class 8B model outperforms others in the class of open weight and data models on short videos, counting, and captioning, and is competitive on long-videos. On video-grounding Molmo2 significantly outperforms existing open-weight models like Qwen3-VL (35.5 vs 29.6 accuracy on video counting) and surpasses proprietary models like Gemini 3 Pro on some tasks (38.4 vs 20.0 F1 on video pointing and 56.2 vs 41.1 J&F on video tracking)."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The newly collected datasets are high-quality, diverse, and free of leakage or bias that would inflate the reported performance gains over baselines."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Molmo2 delivers state-of-the-art open-weight video VLMs with new grounding datasets and training methods that outperform prior open models and match or exceed some proprietary ones on pointing and tracking tasks."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Molmo2 releases new open video and multi-image datasets plus a training recipe that lets an 8B model outperform other open-weight VLMs on video tasks and beat some proprietary models on pixel grounding."}],"snapshot_sha256":"bdbcb84be687d40fd997c4a186c94b04641f2627bc9cd35c7b026f6f096c5eb2"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"09446e6cf4991e268627c96abd0fbde38ca28bc0733d713a1bee0fd8789685b1"},"paper":{"abstract_excerpt":"Today's strongest video-language models (VLMs) remain proprietary. The strongest open-weight models either rely on synthetic data from proprietary VLMs, effectively distilling from them, or do not disclose their training data or recipe. As a result, the open-source community lacks the foundations needed to improve on the state-of-the-art video (and image) language models. Crucially, many downstream applications require more than just high-level video understanding; they require grounding -- either by pointing or by tracking in pixels. Even proprietary models lack this capability. We present Mo","authors_text":"Ali Farhadi, Chris Dongjoo Kim, Christopher Clark, George Stoica, Jae Sung Park, Jianrui Zhang, Jieyu Zhang, Jitesh Jain, Mohammadreza Salehi, Ranjay Krishna, Rohun Tripathi, Sangho Lee, Taira Anderson, Vincent Shao, Weikai Huang, Winson Han, Yinuo Yang, Yue Yang, Zhongzheng Ren, Ziqi Gao, Zixian Ma","cross_cats":["cs.AI"],"headline":"Molmo2 releases new open video and multi-image datasets plus a training recipe that lets an 8B model outperform other open-weight VLMs on video tasks and beat some proprietary models on pixel grounding.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-01-15T17:27:44Z","title":"Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding"},"references":{"count":187,"internal_anchors":27,"resolved_work":187,"sample":[{"cited_arxiv_id":"2510.03342","doi":"","is_internal_anchor":true,"ref_index":1,"title":"Gemini Robotics 1.5: Pushing the Frontier of Generalist Robots with Advanced Embodied Reasoning, Thinking, and Motion Transfer","work_id":"ffe7b7f4-997b-4957-b253-03cfbebf6f9a","year":2025},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"M. Acharya, K. Kafle, and C. Kanan. TallyQA: Answering complex counting questions. InAAAI, 2019","work_id":"5d814cf0-ca72-46d0-9ad4-9160ff898462","year":2019},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Videomolmo: Spatio-temporal grounding meets pointing","work_id":"11238605-33ed-4866-a7b0-1df17e186797","year":2025},{"cited_arxiv_id":"2407.21783","doi":"","is_internal_anchor":true,"ref_index":4,"title":"The Llama 3 Herd of Models","work_id":"1549a635-88af-4ac1-acfe-51ae7bb53345","year":2024},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Claude sonnet 4.5 system card, 2025","work_id":"778b4be6-60c8-4ac2-99f9-0bea4eee47d4","year":2025}],"snapshot_sha256":"0dd5d113efb39a6e735a8af64c6e22a8e6c460c5fd00e879f868e661bb13355c"},"source":{"id":"2601.10611","kind":"arxiv","version":4},"verdict":{"created_at":"2026-05-16T04:12:07.474713Z","id":"aa985fbe-36a9-478a-8bed-c9a87f81397f","model_set":{"reader":"grok-4.3"},"one_line_summary":"Molmo2 delivers state-of-the-art open-weight video VLMs with new grounding datasets and training methods that outperform prior open models and match or exceed some proprietary ones on pointing and tracking tasks.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Molmo2 releases new open video and multi-image datasets plus a training recipe that lets an 8B model outperform other open-weight VLMs on video tasks and beat some proprietary models on pixel grounding.","strongest_claim":"Our best-in-class 8B model outperforms others in the class of open weight and data models on short videos, counting, and captioning, and is competitive on long-videos. On video-grounding Molmo2 significantly outperforms existing open-weight models like Qwen3-VL (35.5 vs 29.6 accuracy on video counting) and surpasses proprietary models like Gemini 3 Pro on some tasks (38.4 vs 20.0 F1 on video pointing and 56.2 vs 41.1 J&F on video tracking).","weakest_assumption":"The newly collected datasets are high-quality, diverse, and free of leakage or bias that would inflate the reported performance gains over baselines."}},"verdict_id":"aa985fbe-36a9-478a-8bed-c9a87f81397f"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:d446c1bacd633d5a7dcf25739d6d6436a050d44e0b2be01dd94fa6c4543244e2","target":"record","created_at":"2026-05-17T23:38:49Z","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":"612d9d70fa3a6f07357f20ad70e1ab464b2cb9ad404f435ba2b2c6f3892b161e","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-01-15T17:27:44Z","title_canon_sha256":"022f070504b46fd82cbb60fe0bbb437f4b6ac38bd99c2665129cc2fb9264b0a8"},"schema_version":"1.0","source":{"id":"2601.10611","kind":"arxiv","version":4}},"canonical_sha256":"a6886e487f9801b173751dee48d7b71230f215da053978ff77cadc84343bbbef","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a6886e487f9801b173751dee48d7b71230f215da053978ff77cadc84343bbbef","first_computed_at":"2026-05-17T23:38:49.131776Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:38:49.131776Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wo3eVm7P6oc+Y0xlffFDPDOgPkIJL/FIFlJ+JsVZUjzriECbKRQqGB1YU+64UVfurdisDaxNKidHbhw8zDiQDQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:38:49.132445Z","signed_message":"canonical_sha256_bytes"},"source_id":"2601.10611","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d446c1bacd633d5a7dcf25739d6d6436a050d44e0b2be01dd94fa6c4543244e2","sha256:0aebd5b44ddf55668ef8084d757eacb1c6e30e3749d19baee21d5aed161d73df"],"state_sha256":"4fa4fff49507184c45a3ea5675db60ae1c26af6309d47581ab791ef7663eb14c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IRGabb9Mjy+EJidze7KSrdaCLm7wdccoJ9zHZcjVGJ3dMr26xrtrq5JEYoTJ/PIk4HBhPnwaDgptla7J4XOJAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T21:39:25.280745Z","bundle_sha256":"9944c9078035b68446a1400bbce705bd9305a568b5f0f6a18f484b478b8f60f2"}}