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CogGuide: Human-Like Guidance for Zero-Shot Omni-Modal Reasoning
Created by
Haebom
Author
Zhou-Peng Shou (NoDesk AI, Hangzhou, China, Zhejiang University, Hangzhou, China), Zhi-Qiang You (NoDesk AI, Hangzhou, China), Fang Wang (NoDesk AI, Hangzhou, China), Hai-Bo Liu (Independent Researcher, Hangzhou, China)
Outline
To address the "shortcut" problem and insufficient context understanding in complex cross-modal inference of large-scale multimodal models, this paper proposes a zero-shot multimodal inference component guided by a human-like cognitive strategy centered on "intention sketching." This component consists of a plug-and-play pipeline of three modules (intention receptor, strategy generator, and strategy selector) that explicitly configure the "comprehend-plan-select" cognitive process. By generating and filtering the "intention sketch" strategy to guide the final inference, cross-modal transfer is achieved solely through contextual engineering, eliminating the need for parameter tuning. Information-theoretic analysis demonstrates that this process can suppress unintended shortcuts by reducing conditional entropy and improving information utilization efficiency. Experiments on IntentBench, WorldSense, and Daily-Omni validate the generality and robust performance of this method. Compared to each baseline, the full "three-module" scheme achieves up to approximately 9.51% improvement across various inference engine and pipeline combinations, demonstrating the practical value and portability of the "intent sketch" inference component in zero-shot scenarios.
Takeaways, Limitations
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Takeaways:
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We present a novel approach to improve the accuracy and efficiency of multimodal inference in zero-shot settings.
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Effectively solve shortcut inference problems using a cognitive strategy based on "intention sketching."
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Provides modular, plug-and-play components applicable to various inference engines and pipelines.
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The effectiveness of the method is theoretically supported through information theoretic analysis.
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Limitations:
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A detailed description of the process of creating and filtering "intention sketches" may be lacking.
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Generalization performance may be limited for certain types of multimodal data or inference tasks.
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The experimental results may be limited to a specific dataset and further research may be needed to determine generalizability to other datasets.
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There may be a lack of analysis of the complexity and computational cost of the process of generating "intentional sketches."
