Look on Demand: A Cognitive Scheduling Framework for Visual Evidence Acquisition in Multimodal Reasoning
arXiv:2605.28160v1 Announce Type: new Abstract: Existing multimodal reasoning approaches predominantly follow two paradigms: converting visual inputs into text prior to reasoning, or performing end-to-end reasoning within a unified vision-language representation space. Despite their empirical progress, both paradigms suffer from fundamental structural limitations. The former relies on static visual-to-text conversion, which tends to compress and lose fine-grained visual details. The latter is prone to linguistic dominance induced by joint optimization and attention mechanisms, leading to syste
The paper outlines a new cognitive scheduling framework for visual evidence acquisition, addressing fundamental limitations in existing multimodal reasoning paradigms that either lose fine-grained visual details or suffer from linguistic dominance.
This research suggests a more robust approach to multimodal AI, potentially leading to more accurate and reliable agentic systems that can better interpret complex visual and textual information without bias.
The proposed 'Look on Demand' framework changes how AI systems might prioritize and integrate visual information, moving beyond static conversions or linguistically biased end-to-end reasoning.
- · AI agents developers
- · Robotics
- · Computer vision researchers
- · Enterprises deploying multimodal AI
- · Traditional multimodal AI approaches with static visual-to-text conversion
- · Systems heavily reliant on linguistic dominance in multimodal reasoning
Improved performance and reliability of AI systems requiring multimodal understanding.
Accelerated development of more sophisticated autonomous AI agents capable of complex decision-making in real-world environments.
Enhanced human-AI interaction through more nuanced conversational AI and visual understanding, potentially changing workflows across multiple industries.
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Read at arXiv cs.AI