arXiv:2607.07395v1 Announce Type: cross Abstract: Reliable confidence estimation remains a key limitation of test-time adaptation in vision-language models (VLMs), where prompt tuning improves zero-shot accuracy but often degrades calibration due to entropy-driven overconfidence. Prior approaches mitigate this using LLM-derived class attributes and contrastive regularization, yet treat attributes independently, ignoring their relational structure. We propose ARGTCA, which represents (class, attribute) pairs as nodes in a Symbolic Attribute Graph and trains a Graph Attention Network (GAT) using
The proliferation of Vision-Language Models (VLMs) and the increasing demand for reliable AI systems are pushing towards more sophisticated calibration techniques.
This research addresses a critical limitation of VLMs by improving confidence estimation, which is essential for deploying these models in high-stakes applications.
The explicit incorporation of relational structure among attributes, rather than treating them independently, offers a more robust framework for VLM calibration.
- · AI developers
- · Companies deploying VLMs
- · Researchers in interpretability
- · Approaches relying solely on entropy-driven calibration
- · Current prompt tuning methods without structural considerations
More reliable and trustworthy Vision-Language Models for various downstream tasks.
Accelerated adoption of VLMs in sensitive domains requiring high calibration, such as medical imaging or autonomous driving.
Potential for new benchmarks and evaluation metrics that specifically assess attribute-based reasoning and calibration in AI models.
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Read at arXiv cs.LG