{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:27DN7LKFWAMNURS6GWLH3LYHGR","short_pith_number":"pith:27DN7LKF","canonical_record":{"source":{"id":"2505.15436","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-21T12:18:15Z","cross_cats_sorted":[],"title_canon_sha256":"a80fbec4ce6d317a92f234a3616c6a232d3c1b558f3f374ee491958814765687","abstract_canon_sha256":"abe39e182ab37b9776c0f14ba01289b4ffa4a7e30c9201aff130a99ecf043ffb"},"schema_version":"1.0"},"canonical_sha256":"d7c6dfad45b018da465e35967daf073440596dff769c3488a4c615c21d0e7678","source":{"kind":"arxiv","id":"2505.15436","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.15436","created_at":"2026-05-17T23:38:14Z"},{"alias_kind":"arxiv_version","alias_value":"2505.15436v3","created_at":"2026-05-17T23:38:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.15436","created_at":"2026-05-17T23:38:14Z"},{"alias_kind":"pith_short_12","alias_value":"27DN7LKFWAMN","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"27DN7LKFWAMNURS6","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"27DN7LKF","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:27DN7LKFWAMNURS6GWLH3LYHGR","target":"record","payload":{"canonical_record":{"source":{"id":"2505.15436","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-21T12:18:15Z","cross_cats_sorted":[],"title_canon_sha256":"a80fbec4ce6d317a92f234a3616c6a232d3c1b558f3f374ee491958814765687","abstract_canon_sha256":"abe39e182ab37b9776c0f14ba01289b4ffa4a7e30c9201aff130a99ecf043ffb"},"schema_version":"1.0"},"canonical_sha256":"d7c6dfad45b018da465e35967daf073440596dff769c3488a4c615c21d0e7678","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:38:14.966598Z","signature_b64":"XuWATu1px2q+kekKVNEJ0Xu3GT6RKcRPhjPzssu/XHRodic24T1sZG9eAdb8GTZ/L8/BFW6zt8te0EeHVS2aAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d7c6dfad45b018da465e35967daf073440596dff769c3488a4c615c21d0e7678","last_reissued_at":"2026-05-17T23:38:14.965935Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:38:14.965935Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.15436","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:38:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"P5KNWoh6wE1HQ5uPmFEYEqJHuYn70M1u9Rojz3Qtlxa/yY0mf+RC+Y2NaWQ04mIsaRCHsCzFhsnEp47/HQaECg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T04:03:33.637671Z"},"content_sha256":"37c08c7660ba6ffec3125e2b818eb73a34cfa34f05dc21e785e3637069f18874","schema_version":"1.0","event_id":"sha256:37c08c7660ba6ffec3125e2b818eb73a34cfa34f05dc21e785e3637069f18874"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:27DN7LKFWAMNURS6GWLH3LYHGR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Adaptive Chain-of-Focus Reasoning via Dynamic Visual Search and Zooming for Efficient VLMs","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"VLMs can reason more efficiently by adaptively searching and zooming into key image regions via Chain-of-Focus training.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bofei Zhang, Pengxiang Li, Qing Li, Song-Chun Zhu, Tao Yuan, Xiaowen Zhang, Xintong Zhang, Yang Liu, Yunde Jia, Yuwei Wu, Zhi Gao","submitted_at":"2025-05-21T12:18:15Z","abstract_excerpt":"Vision language models (VLMs) have achieved impressive performance across a variety of computer vision tasks. However, the multimodal reasoning capability has not been fully explored in existing models. In this paper, we propose a Chain-of-Focus (CoF) method that allows VLMs to perform adaptive focusing and zooming in on key image regions based on obtained visual cues and the given questions, achieving efficient multimodal reasoning. To enable this CoF capability, we present a two-stage training pipeline, including supervised fine-tuning (SFT) and reinforcement learning (RL). In the SFT stage,"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"our model outperforms existing VLMs by 5% among 8 image resolutions ranging from 224 to 4K, demonstrating the effectiveness of the proposed CoF method and facilitating the more efficient deployment of VLMs in practical applications.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The visual agent used to generate the MM-CoF dataset reliably identifies task-relevant regions without introducing systematic biases that would limit generalization to real user queries or unseen image distributions.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Chain-of-Focus enables VLMs to adaptively search and zoom on important image areas via a two-stage SFT and RL pipeline on a custom 3K-sample dataset, yielding 5% gains on the V* benchmark across resolutions from 224 to 4K.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"VLMs can reason more efficiently by adaptively searching and zooming into key image regions via Chain-of-Focus training.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"4b57c49356ebfb0aed725424c7b4bfd4b090c3f4dde9153f21769f356d6365c0"},"source":{"id":"2505.15436","kind":"arxiv","version":3},"verdict":{"id":"912d8459-36ce-48b1-bf21-34a03ea79bb9","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-17T05:31:35.813719Z","strongest_claim":"our model outperforms existing VLMs by 5% among 8 image resolutions ranging from 224 to 4K, demonstrating the effectiveness of the proposed CoF method and facilitating the more efficient deployment of VLMs in practical applications.","one_line_summary":"Chain-of-Focus enables VLMs to adaptively search and zoom on important image areas via a two-stage SFT and RL pipeline on a custom 3K-sample dataset, yielding 5% gains on the V* benchmark across resolutions from 224 to 4K.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The visual agent used to generate the MM-CoF dataset reliably identifies task-relevant regions without introducing systematic biases that would limit generalization to real user queries or unseen image distributions.","pith_extraction_headline":"VLMs can reason more efficiently by adaptively searching and zooming into key image regions via Chain-of-Focus training."},"references":{"count":59,"sample":[{"doi":"","year":2019,"title":"Tallyqa: Answering complex counting ques- tions","work_id":"c3fed856-97d4-4af3-8ff2-5127ad399516","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training","work_id":"41c2802e-aff9-482f-b506-10955ff0838d","ref_index":2,"cited_arxiv_id":"2509.23661","is_internal_anchor":true},{"doi":"","year":2025,"title":"Claude 3.7 Sonnet.https://www.anthropic.com/claude/sonnet, 2025","work_id":"0c200492-6f3e-4cb6-89b5-7a9e20edf760","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond","work_id":"cbc2bb21-b6bb-46c0-80bf-107e195ffe10","ref_index":4,"cited_arxiv_id":"2308.12966","is_internal_anchor":true},{"doi":"","year":2025,"title":"Qwen2.5-VL Technical Report","work_id":"69dffacb-bfe8-442d-be86-48624c60426f","ref_index":5,"cited_arxiv_id":"2502.13923","is_internal_anchor":true}],"resolved_work":59,"snapshot_sha256":"4aaf253a0bcea7d55e001c04587d8ab099787d6b613c172ebd182ce53ee488d8","internal_anchors":21},"formal_canon":{"evidence_count":2,"snapshot_sha256":"8226ee5ab00d74ee1f58b3c0a9f23eadc818413624f9fb9acdb720e0d77aac23"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"912d8459-36ce-48b1-bf21-34a03ea79bb9"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:38:14Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"H2aWNe+OXFBfY9UJi13Y2eofHlQCc4aKJBZtvXawF3Jpv+O48QW18gWoSIdKRQ/e22CcpmrbZDaHWT5AeHIFDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T04:03:33.638193Z"},"content_sha256":"3d90d5d524cf84bd0f87a0f02ba70f3bcb7d7ff828f2ae9cb2c2f78434c27a3f","schema_version":"1.0","event_id":"sha256:3d90d5d524cf84bd0f87a0f02ba70f3bcb7d7ff828f2ae9cb2c2f78434c27a3f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/27DN7LKFWAMNURS6GWLH3LYHGR/bundle.json","state_url":"https://pith.science/pith/27DN7LKFWAMNURS6GWLH3LYHGR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/27DN7LKFWAMNURS6GWLH3LYHGR/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":"2026-06-02T04:03:33Z","links":{"resolver":"https://pith.science/pith/27DN7LKFWAMNURS6GWLH3LYHGR","bundle":"https://pith.science/pith/27DN7LKFWAMNURS6GWLH3LYHGR/bundle.json","state":"https://pith.science/pith/27DN7LKFWAMNURS6GWLH3LYHGR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/27DN7LKFWAMNURS6GWLH3LYHGR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:27DN7LKFWAMNURS6GWLH3LYHGR","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":"abe39e182ab37b9776c0f14ba01289b4ffa4a7e30c9201aff130a99ecf043ffb","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-21T12:18:15Z","title_canon_sha256":"a80fbec4ce6d317a92f234a3616c6a232d3c1b558f3f374ee491958814765687"},"schema_version":"1.0","source":{"id":"2505.15436","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.15436","created_at":"2026-05-17T23:38:14Z"},{"alias_kind":"arxiv_version","alias_value":"2505.15436v3","created_at":"2026-05-17T23:38:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.15436","created_at":"2026-05-17T23:38:14Z"},{"alias_kind":"pith_short_12","alias_value":"27DN7LKFWAMN","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"27DN7LKFWAMNURS6","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"27DN7LKF","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:3d90d5d524cf84bd0f87a0f02ba70f3bcb7d7ff828f2ae9cb2c2f78434c27a3f","target":"graph","created_at":"2026-05-17T23:38:14Z","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 model outperforms existing VLMs by 5% among 8 image resolutions ranging from 224 to 4K, demonstrating the effectiveness of the proposed CoF method and facilitating the more efficient deployment of VLMs in practical applications."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The visual agent used to generate the MM-CoF dataset reliably identifies task-relevant regions without introducing systematic biases that would limit generalization to real user queries or unseen image distributions."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Chain-of-Focus enables VLMs to adaptively search and zoom on important image areas via a two-stage SFT and RL pipeline on a custom 3K-sample dataset, yielding 5% gains on the V* benchmark across resolutions from 224 to 4K."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"VLMs can reason more efficiently by adaptively searching and zooming into key image regions via Chain-of-Focus training."}],"snapshot_sha256":"4b57c49356ebfb0aed725424c7b4bfd4b090c3f4dde9153f21769f356d6365c0"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"8226ee5ab00d74ee1f58b3c0a9f23eadc818413624f9fb9acdb720e0d77aac23"},"paper":{"abstract_excerpt":"Vision language models (VLMs) have achieved impressive performance across a variety of computer vision tasks. However, the multimodal reasoning capability has not been fully explored in existing models. In this paper, we propose a Chain-of-Focus (CoF) method that allows VLMs to perform adaptive focusing and zooming in on key image regions based on obtained visual cues and the given questions, achieving efficient multimodal reasoning. To enable this CoF capability, we present a two-stage training pipeline, including supervised fine-tuning (SFT) and reinforcement learning (RL). In the SFT stage,","authors_text":"Bofei Zhang, Pengxiang Li, Qing Li, Song-Chun Zhu, Tao Yuan, Xiaowen Zhang, Xintong Zhang, Yang Liu, Yunde Jia, Yuwei Wu, Zhi Gao","cross_cats":[],"headline":"VLMs can reason more efficiently by adaptively searching and zooming into key image regions via Chain-of-Focus training.","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-21T12:18:15Z","title":"Adaptive Chain-of-Focus Reasoning via Dynamic Visual Search and Zooming for Efficient VLMs"},"references":{"count":59,"internal_anchors":21,"resolved_work":59,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Tallyqa: Answering complex counting ques- tions","work_id":"c3fed856-97d4-4af3-8ff2-5127ad399516","year":2019},{"cited_arxiv_id":"2509.23661","doi":"","is_internal_anchor":true,"ref_index":2,"title":"LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training","work_id":"41c2802e-aff9-482f-b506-10955ff0838d","year":2025},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Claude 3.7 Sonnet.https://www.anthropic.com/claude/sonnet, 2025","work_id":"0c200492-6f3e-4cb6-89b5-7a9e20edf760","year":2025},{"cited_arxiv_id":"2308.12966","doi":"","is_internal_anchor":true,"ref_index":4,"title":"Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond","work_id":"cbc2bb21-b6bb-46c0-80bf-107e195ffe10","year":2023},{"cited_arxiv_id":"2502.13923","doi":"","is_internal_anchor":true,"ref_index":5,"title":"Qwen2.5-VL Technical Report","work_id":"69dffacb-bfe8-442d-be86-48624c60426f","year":2025}],"snapshot_sha256":"4aaf253a0bcea7d55e001c04587d8ab099787d6b613c172ebd182ce53ee488d8"},"source":{"id":"2505.15436","kind":"arxiv","version":3},"verdict":{"created_at":"2026-05-17T05:31:35.813719Z","id":"912d8459-36ce-48b1-bf21-34a03ea79bb9","model_set":{"reader":"grok-4.3"},"one_line_summary":"Chain-of-Focus enables VLMs to adaptively search and zoom on important image areas via a two-stage SFT and RL pipeline on a custom 3K-sample dataset, yielding 5% gains on the V* benchmark across resolutions from 224 to 4K.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"VLMs can reason more efficiently by adaptively searching and zooming into key image regions via Chain-of-Focus training.","strongest_claim":"our model outperforms existing VLMs by 5% among 8 image resolutions ranging from 224 to 4K, demonstrating the effectiveness of the proposed CoF method and facilitating the more efficient deployment of VLMs in practical applications.","weakest_assumption":"The visual agent used to generate the MM-CoF dataset reliably identifies task-relevant regions without introducing systematic biases that would limit generalization to real user queries or unseen image distributions."}},"verdict_id":"912d8459-36ce-48b1-bf21-34a03ea79bb9"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:37c08c7660ba6ffec3125e2b818eb73a34cfa34f05dc21e785e3637069f18874","target":"record","created_at":"2026-05-17T23:38:14Z","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":"abe39e182ab37b9776c0f14ba01289b4ffa4a7e30c9201aff130a99ecf043ffb","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-05-21T12:18:15Z","title_canon_sha256":"a80fbec4ce6d317a92f234a3616c6a232d3c1b558f3f374ee491958814765687"},"schema_version":"1.0","source":{"id":"2505.15436","kind":"arxiv","version":3}},"canonical_sha256":"d7c6dfad45b018da465e35967daf073440596dff769c3488a4c615c21d0e7678","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d7c6dfad45b018da465e35967daf073440596dff769c3488a4c615c21d0e7678","first_computed_at":"2026-05-17T23:38:14.965935Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:38:14.965935Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XuWATu1px2q+kekKVNEJ0Xu3GT6RKcRPhjPzssu/XHRodic24T1sZG9eAdb8GTZ/L8/BFW6zt8te0EeHVS2aAg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:38:14.966598Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.15436","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:37c08c7660ba6ffec3125e2b818eb73a34cfa34f05dc21e785e3637069f18874","sha256:3d90d5d524cf84bd0f87a0f02ba70f3bcb7d7ff828f2ae9cb2c2f78434c27a3f"],"state_sha256":"986930239a1621b412adfa5ac4d05837712e182c0ba89cce131f71487226033c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bmvRUO/rQihrmd8F8Txyf+5M5h4DTaXrkV6q7UNZdyxtGnlj4bqUCq0IIN7zmgzPQkb6/BAAJuFvt+RZjvwvDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T04:03:33.640631Z","bundle_sha256":"13d009889452743d67c6f8d170d1162a9c47ea1a4e4c87e9c497dacd52288e17"}}