ADEPT: An Entropy-Driven Dual-Strategy Agent for Interactive Video Retrieval

• URL: http://arxiv.org/abs/2606.28326v1
• Date: Thu, 07 May 2026 14:00:16 GMT
• Title: ADEPT: An Entropy-Driven Dual-Strategy Agent for Interactive Video Retrieval
• Abstract summary: This research aims to solve the challenge of video retrieval from massive datasets, caused by ambiguous user queries.<n>The root cause is the "Intent-Query Gap", where users' intent cannot be captured by a simple text query.<n>We propose the ADEPT framework: a training-free agent that pioneers an entropy-driven decision engine.
• Score: 19.132505747414
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 between ASK and REFINE strategies. Experiments on two challenging datasets demonstrate that ADEPT significantly outperforms all non-interactive, heuristic, and Video-LLM baselines. The core contribution of this work is an efficient and interpretable entropy-driven interactive strategy that sets a new performance benchmark for the field of interactive video retrieval.

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