# RVTBench: A Benchmark for Visual Reasoning Tasks

arXiv: https://arxiv.org/abs/2505.11838 · HTML: https://mlsanity.com/paper/2505.11838/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal reasoning, one of 17 directions in LLM reasoning.
- Builds on 14 of its 34 references that are in the map.
- First posted on arXiv 17 May 2025.

## Placed in

- LLM reasoning / [Multimodal reasoning](https://mlsanity.com/lr/multimodal-reasoning/)

## Builds on (most cited first, 14 of 14)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [The 2017 DAVIS Challenge on Video Object Segmentation](https://mlsanity.com/paper/1704.00675/) (2017)
- [Qwen2.5-Omni Technical Report](https://mlsanity.com/paper/2503.20215/) (2025)
- [VISA: Reasoning Video Object Segmentation via Large Language Models](https://mlsanity.com/paper/2407.11325/) (2024)
- [One Token to Seg Them All: Language Instructed Reasoning Segmentation in Videos](https://mlsanity.com/paper/2409.19603/) (2024)
- [ViLLa: Video Reasoning Segmentation with Large Language Model](https://mlsanity.com/paper/2407.14500/) (2024)
- [Motion-Grounded Video Reasoning: Understanding and Perceiving Motion at Pixel Level](https://mlsanity.com/paper/2411.09921/) (2024)
- [ViCaS: A Dataset for Combining Holistic and Pixel-level Video Understanding using Captions with Grounded Segmentation](https://mlsanity.com/paper/2412.09754/) (2024)
- [Online Reasoning Video Segmentation with Just-in-Time Digital Twins](https://mlsanity.com/paper/2503.21056/) (2025)
- [Position: Foundation Models Need Digital Twin Representations](https://mlsanity.com/paper/2505.03798/) (2025)
- [Operating Room Workflow Analysis via Reasoning Segmentation over Digital Twins](https://mlsanity.com/paper/2503.21054/) (2025)

## Built on by (most cited first, 9 of 9)

- [Multimodal Referring Segmentation: A Survey](https://mlsanity.com/paper/2508.00265/) (2025)
- [Reasoning Segmentation for Images and Videos: A Survey](https://mlsanity.com/paper/2505.18816/) (2025)
- [Look Less, Reason More: Rollout-Guided Adaptive Pixel-Space Reasoning](https://mlsanity.com/paper/2510.01681/) (2025)
- [Counterfactual World Models via Digital Twin-conditioned Video Diffusion](https://mlsanity.com/paper/2511.17481/) (2025)
- [Reasoning Text-to-Video Retrieval via Digital Twin Video Representations and Large Language Models](https://mlsanity.com/paper/2511.12371/) (2025)
- [Text-Driven Reasoning Video Editing via Reinforcement Learning on Digital Twin Representations](https://mlsanity.com/paper/2511.14100/) (2025)
- [Temporally-Constrained Video Reasoning Segmentation and Automated Benchmark Construction](https://mlsanity.com/paper/2507.16718/) (2025)
- [Constructing and Interpreting Digital Twin Representations for Visual Reasoning via Reinforcement Learning](https://mlsanity.com/paper/2511.12365/) (2025)
- [Fast Reasoning Segmentation for Images and Videos](https://mlsanity.com/paper/2511.12368/) (2025)

## Abstract (arXiv)

Visual reasoning, the capability to interpret visual input in response to implicit text query through multi-step reasoning, remains a challenge for deep learning models due to the lack of relevant benchmarks. Previous work in visual reasoning has primarily focused on reasoning segmentation, where models aim to segment objects based on implicit text queries. This paper introduces reasoning visual tasks (RVTs), a unified formulation that extends beyond traditional video reasoning segmentation to a diverse family of visual language reasoning problems, which can therefore accommodate multiple output formats including bounding boxes, natural language descriptions, and question-answer pairs. Correspondingly, we identify the limitations in current benchmark construction methods that rely solely on large language models (LLMs), which inadequately capture complex spatial-temporal relationships and multi-step reasoning chains in video due to their reliance on token representation, resulting in benchmarks with artificially limited reasoning complexity. To address this limitation, we propose a novel automated RVT benchmark construction pipeline that leverages digital twin (DT) representations as structured intermediaries between perception and the generation of implicit text queries. Based on this method, we construct RVTBench, a RVT benchmark containing 3,896 queries of over 1.2 million tokens across four types of RVT (segmentation, grounding, VQA and summary), three reasoning categories (semantic, spatial, and temporal), and four increasing difficulty levels, derived from 200 video sequences. Finally, we propose RVTagent, an agent framework for RVT that allows for zero-shot generalization across various types of RVT without task-specific fine-tuning.