arXiv:2511.16107v3 Announce Type: replace-cross Abstract: Visual in-context learning (VICL) solves visual tasks by conditioning on a few input-output demonstrations without any model training. Recent advances in large vision-language models (VLMs) have shown promising VICL capability when the demonstration pair and the query belong to the same vision task, but real use cases often provide mismatched examples, making it unclear whether a VLM should imitate the demonstrated transformation or infer a new one from the query. This raises a fundamental question: Can VLMs perform cross-task VICL wher
The paper addresses a current limitation in large vision-language models, specifically their ability to handle cross-task visual in-context learning, which is a key next step for more adaptable AI agents.
This research could significantly advance the versatility and intelligence of AI agents, making them more capable of solving complex, real-world problems without extensive re-training.
The ability of VLMs to perform cross-task in-context learning means AI systems can adapt to novel visual tasks with fewer examples and generalize more effectively.
- · AI software developers
- · Robotics
- · Computer vision applications
- · Generative AI platforms
- · Companies relying on highly specialized, single-task AI models
VLMs become more efficient and adaptable in diverse visual tasks.
This improved adaptability accelerates the development and deployment of more general-purpose AI agents in various industries.
The enhanced generalization capabilities could reduce the data and computational resources required for new AI applications, democratizing advanced AI development.
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Read at arXiv cs.AI