aiqbench analysis
AiSearch introduces an interactive multimodal retrieval framework that utilizes Vision Language Models (VLMs) to enable real-time refinement of image and video search results based on user feedback. The system facilitates visual benchmarking across different VLMs, allowing practitioners to select the most appropriate model for specific intent-driven tasks without requiring fine-tuning. This approach addresses the need for automated yet adaptable retrieval systems in modern multimodal applications.
AiSearch: Interactive Multi-Modal Search with VLMs
arXiv:2610.01389v1 Announce Type: new Abstract: Modern retrieval systems must both be automated and interactive, allowing users to search and refine results in real time. We present AiSearch, a flexible multimodal retrieval framework that leverages the zero shot capabilities of Vision Language Models (VLMs) for natural language search over images and videos. AiSearch supports interactive search refinement through user feedback to tailor results to the user's intent, and allows visual benchmarking across multiple VLMs, enabling users to select the most suitable model for their task.
Abstract:Modern retrieval systems must both be automated and interactive, allowing users to search and refine results in real time. We present AiSearch, a flexible multimodal retrieval framework that leverages the zero shot capabilities of Vision Language Models (VLMs) for natural language search over images and videos. AiSearch supports interactive search refinement through user feedback to tailor results to the user's intent, and allows visual benchmarking across multiple VLMs, enabling users to select the most suitable model for their task.
Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2610.01389 [cs.AI]
(or arXiv:2610.01389v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.01389 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 179 words
Related coverage
Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers
Hugging Face · Aug 18, 2026
FronTalk: Benchmarking Front-End Development as Conversational Code Generation with Multi-Modal Feedback
arXiv · Jun 12, 2026
Amazon is investing in the Lean Focused Research Organization
Amazon Science · Jul 26, 2026
Template-Search Domain Adaptation via Multi-Stage Feature Alignment for Cross-Modal Object Tracking
arXiv · Oct 1, 2026