Logics-Parsing-Omni Technical Report
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910111737315328 |
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| author | An, Xin Cai, Jingyi Chen, Xiangyang Liu, Huayao Liu, Peiting Wang, Peng Yang, Bei Zhu, Xiuwen Chen, Yongfan Gao, Yan Gao, Yuan Hou, Baoyu Hu, Guangzheng Li, Shuzhao Qiao, Weixu Ren, Weidong Wang, Yanan Yang, Boyu Yang, Fan Zhang, Jiangtao Zhang, Lixin Qu, Lin Wei, Hu Xu, Xiaoxiao Zhao, Bing |
| author_facet | An, Xin Cai, Jingyi Chen, Xiangyang Liu, Huayao Liu, Peiting Wang, Peng Yang, Bei Zhu, Xiuwen Chen, Yongfan Gao, Yan Gao, Yuan Hou, Baoyu Hu, Guangzheng Li, Shuzhao Qiao, Weixu Ren, Weidong Wang, Yanan Yang, Boyu Yang, Fan Zhang, Jiangtao Zhang, Lixin Qu, Lin Wei, Hu Xu, Xiaoxiao Zhao, Bing |
| contents | Addressing the challenges of fragmented task definitions and the heterogeneity of unstructured data in multimodal parsing, this paper proposes the Omni Parsing framework. This framework establishes a Unified Taxonomy covering documents, images, and audio-visual streams, introducing a progressive parsing paradigm that bridges perception and cognition. Specifically, the framework integrates three hierarchical levels: 1) Holistic Detection, which achieves precise spatial-temporal grounding of objects or events to establish a geometric baseline for perception; 2) Fine-grained Recognition, which performs symbolization (e.g., OCR/ASR) and attribute extraction on localized objects to complete structured entity parsing; and 3) Multi-level Interpreting, which constructs a reasoning chain from local semantics to global logic. A pivotal advantage of this framework is its evidence anchoring mechanism, which enforces a strict alignment between high-level semantic descriptions and low-level facts. This enables ``evidence-based'' logical induction, transforming unstructured signals into standardized knowledge that is locatable, enumerable, and traceable. Building on this foundation, we constructed a standardized dataset and released the Logics-Parsing-Omni model, which successfully converts complex audio-visual signals into machine-readable structured knowledge. Experiments demonstrate that fine-grained perception and high-level cognition are synergistic, effectively enhancing model reliability. Furthermore, to quantitatively evaluate these capabilities, we introduce OmniParsingBench. Code, models and the benchmark are released at https://github.com/alibaba/Logics-Parsing/tree/master/Logics-Parsing-Omni. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_09677 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Logics-Parsing-Omni Technical Report An, Xin Cai, Jingyi Chen, Xiangyang Liu, Huayao Liu, Peiting Wang, Peng Yang, Bei Zhu, Xiuwen Chen, Yongfan Gao, Yan Gao, Yuan Hou, Baoyu Hu, Guangzheng Li, Shuzhao Qiao, Weixu Ren, Weidong Wang, Yanan Yang, Boyu Yang, Fan Zhang, Jiangtao Zhang, Lixin Qu, Lin Wei, Hu Xu, Xiaoxiao Zhao, Bing Artificial Intelligence Addressing the challenges of fragmented task definitions and the heterogeneity of unstructured data in multimodal parsing, this paper proposes the Omni Parsing framework. This framework establishes a Unified Taxonomy covering documents, images, and audio-visual streams, introducing a progressive parsing paradigm that bridges perception and cognition. Specifically, the framework integrates three hierarchical levels: 1) Holistic Detection, which achieves precise spatial-temporal grounding of objects or events to establish a geometric baseline for perception; 2) Fine-grained Recognition, which performs symbolization (e.g., OCR/ASR) and attribute extraction on localized objects to complete structured entity parsing; and 3) Multi-level Interpreting, which constructs a reasoning chain from local semantics to global logic. A pivotal advantage of this framework is its evidence anchoring mechanism, which enforces a strict alignment between high-level semantic descriptions and low-level facts. This enables ``evidence-based'' logical induction, transforming unstructured signals into standardized knowledge that is locatable, enumerable, and traceable. Building on this foundation, we constructed a standardized dataset and released the Logics-Parsing-Omni model, which successfully converts complex audio-visual signals into machine-readable structured knowledge. Experiments demonstrate that fine-grained perception and high-level cognition are synergistic, effectively enhancing model reliability. Furthermore, to quantitatively evaluate these capabilities, we introduce OmniParsingBench. Code, models and the benchmark are released at https://github.com/alibaba/Logics-Parsing/tree/master/Logics-Parsing-Omni. |
| title | Logics-Parsing-Omni Technical Report |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2603.09677 |