arXiv:2601.21662v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) are typically deterministic in nature and lack intrinsic mechanisms to quantify epistemic uncertainty, which reflects the model's lack of knowledge or ignorance of its own representations. We theoretically motivate negative log-density of an embedding as a proxy for the epistemic uncertainty, where low-density regions signify model ignorance. The proposed method REPVLM computes the probability density on the hyperspherical manifold of the VLM embeddings using Riemannian Flow Matching. We empirically demonstrate t
The rapid advancement and deployment of Vision-Language Models (VLMs) necessitate improved methods for uncertainty quantification to enhance their reliability and safety in real-world applications.
Quantifying epistemic uncertainty in VLMs allows for more robust and trustworthy AI systems, crucial for deployment in sensitive domains where model errors can have significant consequences.
VLMs can now incorporate an intrinsic understanding of their own 'ignorance,' moving beyond deterministic outputs to provide confidence levels, which enhances explainability and reliability.
- · AI Safety Researchers
- · Developers of foundational AI models
- · Industries deploying AI in critical applications (e.g., healthcare, autonomous d
- · AI auditing and compliance firms
- · Companies relying on opaque, uninterpretable AI models
- · Methods that provide only point prediction outputs without uncertainty
VLMs will become more transparent and robust, reducing risks in deployment.
Increased trust in AI systems could accelerate adoption in regulated and high-stakes environments.
New regulatory frameworks and certification processes for AI models might emerge, focusing on uncertainty quantification among other factors.
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