ReCoVR: Closing the Loop in Interactive Composed Video Retrieval

Composed video retrieval (CoVR) retrieves a target video from a reference video and a modification text, yet a single query often leaves the target underspecified and existing methods offer no way to refine it. We formalize *interactive composed video retrieval* (ICoVR), a multi-turn extension of CoVR in which users refine their intent through natural-language feedback. Adapting interactive text-to-video retrieval methods to ICoVR exposes two structural weaknesses: they route all feedback through a *single retrieval channel*, and they operate *open-loop*, updating the query from feedback without checking whether their own rankings are drifting or stagnating. After a turn that fails to improve the target rank, the strongest adapted baseline improves on the next turn in only 30.12% of cases. We propose **ReCoVR**, a training-free framework that closes this loop by treating the system's own ranking trajectory as evidence alongside user feedback. An Intent Pathway routes each feedback turn to text-to-video and composed retrieval channels, and a Reflection Pathway accumulates constraints from feedback and uses ranking history to demote rejected or stagnant results. On WebVid-CoVR-Test, ReCoVR reaches 74.30% Recall at 1 (R@1) after one feedback turn and 90.22% after five, and its margin over the strongest baseline widens from 7.2 to 15.2 points. The gains carry over to Dense-WebVid-CoVR and FineCVR, and evaluation with real human feedback shows the same improvement trend.