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Logics-Parsing-Omni Technical Report

arXiv:2603.09677v1 Announce Type: new Abstract: 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-l

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Xin An, Jingyi Cai, Xiangyang Chen, Huayao Liu, Peiting Liu, Peng Wang, Bei Yang, Xiuwen Zhu, Yongfan Chen, Baoyu Hou, Shuzhao Li, Weidong Ren, Fan Yang, Jiangtao Zhang, Xiaoxiao Xu, Lin Qu · · 1 min read · 9 views
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arXiv:2603.09677v1 Announce Type: new Abstract: 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.

Executive Summary

This article proposes the Omni Parsing framework, a unified taxonomy for multimodal parsing that integrates three hierarchical levels: Holistic Detection, Fine-grained Recognition, and Multi-level Interpreting. The framework's evidence anchoring mechanism enables evidence-based logical induction, transforming unstructured signals into standardized knowledge. Experiments demonstrate the synergistic effect of fine-grained perception and high-level cognition on model reliability. The article introduces OmniParsingBench, a standardized dataset and benchmark for evaluating the framework's capabilities. The Logics-Parsing-Omni model successfully converts complex audio-visual signals into machine-readable structured knowledge. This work has significant implications for multimodal parsing, AI, and data analysis, with potential applications in various industries, including healthcare, finance, and education.

Key Points

  • ▸ The Omni Parsing framework establishes a unified taxonomy for multimodal parsing.
  • ▸ The framework integrates three hierarchical levels: Holistic Detection, Fine-grained Recognition, and Multi-level Interpreting.
  • ▸ The evidence anchoring mechanism enables evidence-based logical induction.

Merits

Strength

The Omni Parsing framework provides a comprehensive and unified approach to multimodal parsing, addressing the challenges of fragmented task definitions and heterogeneity of unstructured data.

Strength

The framework's evidence anchoring mechanism enables the transformation of unstructured signals into standardized knowledge, enhancing model reliability.

Demerits

Limitation

The framework's complexity may pose challenges for implementation and scalability, particularly in resource-constrained environments.

Limitation

The article does not provide a thorough evaluation of the framework's performance in real-world applications, which may limit its practical adoption.

Expert Commentary

The Omni Parsing framework presents a significant contribution to the field of multimodal parsing, addressing the challenges of fragmented task definitions and heterogeneity of unstructured data. The framework's evidence anchoring mechanism is a particularly innovative aspect, enabling the transformation of unstructured signals into standardized knowledge. However, the framework's complexity and potential limitations in real-world applications require careful consideration and further research. As AI and data analysis continue to play increasingly important roles in various industries, the Omni Parsing framework's implications for practical and policy considerations cannot be overstated.

Recommendations

  • ✓ Further research is necessary to evaluate the framework's performance in real-world applications and address potential limitations in implementation and scalability.
  • ✓ The framework's applications in AI and data analysis require careful consideration of policy issues related to data privacy, security, and bias.

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