arXiv:2510.01444v3 Announce Type: replace-cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has advanced reasoning capabilities in multimodal large language models. However, existing methods typically treat visual inputs as deterministic, overlooking the perceptual ambiguity inherent to the visual modality. Consequently, they fail to distinguish whether a model's uncertainty stems from complex reasoning or ambiguous perception, preventing the targeted allocation of exploration or learning signals. To address this gap, we introduce \textbf{DUPL}, a dual-uncertainty guided po
The continuous drive to improve AI reasoning capabilities, particularly in multimodal contexts, necessitates addressing fundamental limitations like perceptual uncertainty. This is a natural progression as AI models become more sophisticated and expected to handle real-world complexities.
Improving how AI models handle uncertainty in multimodal inputs, especially visual, is crucial for developing more robust, reliable, and trustworthy AI systems. This directly impacts the safety and efficacy of AI in critical applications.
AI models will move towards being able to distinguish between reasoning uncertainty and perceptual uncertainty, leading to more targeted learning and exploration strategies. This differentiation allows for more intelligent resource allocation within AI training and operation.
- · AI developers
- · Robotics
- · Autonomous systems
- · Computer vision
- · AI models relying solely on deterministic inputs
- · Current perception-limited AI applications
More accurate and reliable AI decision-making in complex, uncertain environments.
Accelerated development of AI agents capable of nuanced real-world interaction and adaptation.
Potentially reduced errors and increased human trust in AI systems deployed in sensitive domains.
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