arXiv:2606.28326v1 Announce Type: cross Abstract: This research aims to solve the challenge of video retrieval from massive datasets, caused by ambiguous user queries. Prevailing single-round retrieval paradigms face a performance bottleneck, as they lack effective feedback mechanisms to handle complex search intentions. The root cause is the "Intent-Query Gap", where users' intent cannot be captured by a simple text query. To solve this, we propose the ADEPT framework: a training-free agent that pioneers an entropy-driven decision engine to efficiently guide dialogue by dynamically selecting
The proliferation of massive datasets and the inherent ambiguity of single-round queries in video retrieval necessitates more sophisticated, interactive solutions, making agentic approaches increasingly relevant.
This research introduces a novel, training-free approach to interactive AI agents that can rapidly improve search efficacy, directly impacting efficiency in vast data environments and potentially setting new benchmarks for retrieval systems.
The shift from single-round queries to entropy-driven, dynamically guided dialogue agents for information retrieval changes how users interact with and extract value from large video datasets, making searches more precise and efficient.
- · AI developers
- · Cloud infrastructure providers
- · Media and entertainment companies
- · Researchers dependent on video data
- · Legacy search algorithm developers
- · Companies with large, unstructured video archives
More efficient and accurate video retrieval will accelerate research and development in fields relying on visual data.
Improved interactive retrieval paradigms could extend to other data types, making 'intelligent agent' features standard across enterprise software.
The success of training-free, entropy-driven agents could shift focus from large-scale model pre-training towards more dynamic, adaptive agent architectures, impacting compute resource allocation and AI research directions.
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Read at arXiv cs.AI