CoVR-R:Reason-Aware Composed Video Retrieval
| Authors | Omkar Thawakar et al. |
| Year | 2026 |
| Field | Computer Vision |
| arXiv | 2603.20190 |
| Download | |
| Categories | cs.CV |
Abstract
Composed Video Retrieval (CoVR) aims to find a target video given a reference video and a textual modification. Prior work assumes the modification text fully specifies the visual changes, overlooking after-effects and implicit consequences (e.g., motion, state transitions, viewpoint or duration cues) that emerge from the edit. We argue that successful CoVR requires reasoning about these after-effects. We introduce a reasoning-first, zero-shot approach that leverages large multimodal models to (i) infer causal and temporal consequences implied by the edit, and (ii) align the resulting reasoned queries to candidate videos without task-specific finetuning. To evaluate reasoning in CoVR, we also propose CoVR-Reason, a benchmark that pairs each (reference, edit, target) triplet with structured internal reasoning traces and challenging distractors that require predicting after-effects rather than keyword matching. Experiments show that our zero-shot method outperforms strong retrieval baselines on recall at K and particularly excels on implicit-effect subsets. Our automatic and human analysis confirm higher step consistency and effect factuality in our retrieved results. Our findings show that incorporating reasoning into general-purpose multimodal models enables effective CoVR by explicitly accounting for causal and temporal after-effects. This reduces dependence on task-specific supervision, improves generalization to challenging implicit-effect cases, and enhances interpretability of retrieval outcomes. These results point toward a scalable and principled framework for explainable video search. The model, code, and benchmark are available at https://github.com/mbzuai-oryx/CoVR-R.
Engineering Breakdown
Plain English
This paper tackles Composed Video Retrieval (CoVR), where you need to find a target video given a reference video plus a text description of desired changes. Prior work treated the text as a complete specification of visual changes, but this paper argues that successful retrieval requires reasoning about implied consequences—things like how motion patterns change, how object states transition, or how viewpoint and duration shift as a result of the edit. The authors propose a zero-shot approach using large multimodal models to first infer causal and temporal consequences from the edit text, then align these reasoned queries to candidate videos without any task-specific training. They also introduce CoVR-Reason, a new benchmark with structured annotations that pairs reference-edit-target triplets with reasoning information to enable evaluation of reasoning capabilities.
Core Technical Contribution
The core novelty is shifting from direct text-to-video matching to a reasoning-first pipeline that explicitly models after-effects and implicit consequences of edits. Instead of assuming the modification text fully specifies what changed, the authors use large multimodal models to infer causal chains and temporal dependencies—what happens to motion, state, viewpoint, or duration as a consequence of the stated edit. This is technically novel because it decouples the retrieval problem into two stages: (1) reasoning about what the edit actually implies, and (2) matching the reasoned query to videos, allowing zero-shot application without task-specific finetuning. The benchmark contribution is equally important: CoVR-Reason provides structured annotations that capture these reasoning steps, enabling the first systematic evaluation of reasoning quality in composed video retrieval.
How It Works
The system takes three inputs: a reference video, a textual modification (e.g., 'make the person jump higher'), and a pool of candidate videos. The reasoning stage runs inference on a large multimodal model (likely a vision-language model like GPT-4V or similar) that takes the reference video frames and edit text as input and outputs structured reasoning about causal consequences—this might include predicted changes to motion trajectories, object state transitions, temporal duration effects, and viewpoint shifts implied by the edit. The second stage encodes both the reference video and the reasoned predictions into a multimodal embedding space, then ranks candidate videos by their alignment to this reasoned query representation. The key architectural choice is that reasoning happens before retrieval matching, allowing the model to surface implicit consequences that would otherwise be missed by direct text-based similarity. The zero-shot design means no task-specific parameters are learned; the entire pipeline relies on the pretrained capabilities of the foundation multimodal model.
Production Impact
For engineers building video search or content recommendation systems, this approach provides a concrete way to handle complex editing requests that go beyond simple keyword matching. In practice, you'd replace naive text-based video retrieval with a two-stage pipeline: first, pass user edits through a reasoning module to expand queries with inferred consequences, then match against video embeddings—this could significantly improve retrieval quality for complex edits at the cost of additional inference latency (one extra forward pass through a large model). The zero-shot nature is a major production advantage: you don't need to collect and annotate task-specific training data for each new video domain, reducing time-to-market for new products. However, the approach requires access to a capable multimodal model (either via API or self-hosted), which introduces dependency on external services, potential rate-limiting, and costs that scale with query volume. Latency impact is notable: reasoning inference plus video embedding lookup will be slower than direct matching, so you'd need caching strategies or batch processing for large-scale applications.
Limitations and When Not to Use This
The paper assumes that large multimodal models can reliably infer causal consequences, but in practice these models hallucinate and make reasoning errors—the paper doesn't quantify failure rates or provide bounds on reasoning quality, making it unclear when this approach is trustworthy in production. The zero-shot approach relies entirely on the foundation model's pretraining, meaning it will struggle with domain-specific edits (e.g., specialized video effects, niche sports terminology, technical production jargon) that weren't well-represented in training data. The benchmark evaluation is limited to structured reasoning triplets, but doesn't address robustness to adversarial edits, ambiguous text, or multi-step reasoning chains that might appear in real user queries. Additionally, the paper doesn't discuss computational costs or latency—reasoning on every query via a large model may be prohibitively expensive for high-volume systems, and there's no discussion of distillation, quantization, or other efficiency techniques to make this practical at scale.
Research Context
This work extends prior composed image/video retrieval research (which treated text modifications as direct specifications) by incorporating reasoning about implicit consequences—building on recent trends in chain-of-thought prompting and reasoning-enhanced retrieval in NLP. It contributes to the growing area of multimodal retrieval systems that leverage foundation models, sitting alongside work on vision-language alignment and zero-shot video understanding. The CoVR-Reason benchmark advances the evaluation of video understanding by introducing structured reasoning annotations, similar to how VQA datasets like GQA added compositional reasoning evaluation beyond simple classification. This opens research directions in grounded reasoning for video (understanding how edits propagate through spatiotemporal space), reasoning robustness in video (handling ambiguity and underspecification), and efficient reasoning for retrieval (making reasoning-first approaches practical at scale).
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