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VideoReasonBench: Can MLLMs Perform Vision-Centric Complex Video Reasoning?

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 5.5Download Report PDF
video reasoningmultimodal large language models
Abstract:

Recent studies have shown that long chain-of-thought (CoT) reasoning can significantly enhance the performance of large language models (LLMs) on complex tasks. However, this benefit is yet to be demonstrated in the domain of video understanding, since most existing benchmarks lack the reasoning depth required to demonstrate the advantages of extended CoT chains. While recent efforts have proposed benchmarks aimed at video reasoning, the tasks are often knowledge-driven and do not rely heavily on visual content. To bridge this gap, we introduce VideoReasonBench, a benchmark designed to evaluate vision-centric, complex video reasoning. To ensure visual richness and high reasoning complexity, each video in VideoReasonBench depicts a sequence of fine-grained operations on a latent state that is only visible in part of the video. The questions evaluate three escalating levels of video reasoning skills: recalling observed visual information, inferring the content of latent states, and predicting information beyond the video. Under such task setting, models have to precisely recall multiple operations in the video, and perform step-by-step reasoning to get correct final answers for these questions. Using VideoReasonBench, we comprehensively evaluate 18 state-of-the-art multimodal LLMs (MLLMs), finding that most perform poorly on complex video reasoning—e.g., GPT-4o achieves only 6.9% accuracy—while the thinking-enhanced Gemini-2.5-Pro significantly outperforms others with 56.0% accuracy. Our investigations on "test-time scaling'' further reveal that extended thinking budget, while offering none or minimal benefits on existing video benchmarks, is essential for improving the performance on VideoReasonBench.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

VideoReasonBench introduces a benchmark for vision-centric complex video reasoning, emphasizing tasks that require precise recall of fine-grained visual operations and step-by-step inference over latent states. The paper resides in the 'Complex Reasoning and Interpretability Benchmarks' leaf, which contains seven papers including the original work. This leaf is moderately populated within the broader taxonomy of fifty papers, indicating an active but not overcrowded research direction focused on evaluating multi-hop inference and interpretable reasoning in video understanding.

The taxonomy reveals that VideoReasonBench sits within the 'Video Reasoning Benchmarks and Evaluation' branch, which also includes sibling categories for long-form video understanding, multimodal robustness evaluation, and specialized domain benchmarks. Neighboring leaves address complementary challenges: long-form benchmarks assess extended temporal contexts, while multimodal evaluation probes audio-visual integration. The scope note for the original paper's leaf explicitly excludes long-video and domain-specific benchmarks, positioning VideoReasonBench as a general-purpose diagnostic tool for complex reasoning rather than a specialized or extended-context evaluation.

Among thirty candidates examined across three contributions, none were identified as clearly refuting the proposed work. The benchmark contribution examined ten candidates with zero refutable overlaps, as did the systematic framework and evaluation contributions. This suggests that within the limited search scope, VideoReasonBench's emphasis on latent-state inference and escalating reasoning levels appears distinct from existing benchmarks like CLEVRER or IntentQA, which focus on causal or intent-based understanding. However, the analysis is constrained by the top-thirty semantic matches and does not constitute an exhaustive survey of all video reasoning benchmarks.

Based on the limited literature search, VideoReasonBench appears to occupy a recognizable niche within complex reasoning evaluation, differentiating itself through its focus on latent-state tracking and fine-grained visual operations. The absence of refutable candidates among thirty examined papers suggests novelty in task design, though a broader search might reveal closer precedents. The taxonomy context indicates this work contributes to an active but not saturated research direction.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: vision-centric complex video reasoning. The field has evolved into several interconnected branches that address different facets of understanding and generating video content. Video Understanding Architectures and Representations focuses on foundational models and encoding schemes that capture spatial and temporal dynamics, often leveraging transformers or state-space models like VideoMamba[16]. Video Reasoning Frameworks and Mechanisms explores structured inference methods, including chain-of-thought approaches and agent-based systems such as VideoAgent[6], which decompose complex queries into manageable steps. Video Reasoning Benchmarks and Evaluation provides diagnostic datasets and metrics to assess interpretability and multi-step reasoning capabilities, while Video Data and Caption Generation tackles the creation of high-quality annotations and synthetic training data, exemplified by ShareGPT4Video[5]. Conversational Video Understanding emphasizes interactive question-answering systems like VideoChat[2] and Chat-UniVi[11], and Text-to-Video Generation addresses synthesis from textual prompts, bridging language and visual generation. Within the benchmarking landscape, a particularly active line of work targets complex reasoning and interpretability. Datasets such as CLEVRER[24] and IntentQA[9] probe causal and intent-based understanding, while Video-Holmes[13] and the Video Thinking Test[42] push models to perform multi-hop inference and evidence grounding. VideoReasonBench[0] situates itself in this cluster by emphasizing systematic evaluation of reasoning depth and interpretability, aligning closely with Video-Holmes[13] in its focus on structured problem-solving but differing in the granularity of diagnostic tasks. Compared to MINERVA[3], which also stresses interpretable reasoning, VideoReasonBench[0] appears to prioritize a broader spectrum of reasoning types rather than a single modality or domain. These benchmarks collectively highlight ongoing challenges in balancing model scale, reasoning transparency, and generalization across diverse video scenarios.

Claimed Contributions

VideoReasonBench benchmark for vision-centric complex video reasoning

The authors propose a new benchmark that evaluates multimodal large language models on complex video reasoning tasks requiring fine-grained visual perception and multi-step reasoning. Each video depicts a sequence of operations on a latent state visible only partially, with questions assessing three escalating levels: recalling visual information, inferring latent states, and predicting beyond the video.

10 retrieved papers
Systematic framework defining vision-centric complex video reasoning

The authors establish a formal task definition conceptualizing videos as sequences of state transitions where operations are observable but states are only partially visible. They define three progressive reasoning levels with six corresponding skills, providing a principled approach to evaluating complex video reasoning.

10 retrieved papers
Comprehensive evaluation revealing MLLM deficiencies and thinking benefits

The authors conduct extensive experiments showing most state-of-the-art MLLMs achieve very low accuracy on their benchmark, while demonstrating that extended chain-of-thought reasoning provides minimal benefit on existing video benchmarks but proves essential for VideoReasonBench, highlighting its unique demand for reasoning depth.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

VideoReasonBench benchmark for vision-centric complex video reasoning

The authors propose a new benchmark that evaluates multimodal large language models on complex video reasoning tasks requiring fine-grained visual perception and multi-step reasoning. Each video depicts a sequence of operations on a latent state visible only partially, with questions assessing three escalating levels: recalling visual information, inferring latent states, and predicting beyond the video.

Contribution

Systematic framework defining vision-centric complex video reasoning

The authors establish a formal task definition conceptualizing videos as sequences of state transitions where operations are observable but states are only partially visible. They define three progressive reasoning levels with six corresponding skills, providing a principled approach to evaluating complex video reasoning.

Contribution

Comprehensive evaluation revealing MLLM deficiencies and thinking benefits

The authors conduct extensive experiments showing most state-of-the-art MLLMs achieve very low accuracy on their benchmark, while demonstrating that extended chain-of-thought reasoning provides minimal benefit on existing video benchmarks but proves essential for VideoReasonBench, highlighting its unique demand for reasoning depth.

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