VRBench: A Benchmark for Multi-Step Reasoning in Long Narrative Videos
VRBench evaluates multi-step reasoning in long videos, with 960 videos and 8,243 QA pairs.
Key Findings
Methodology
VRBench employs a human-AI collaborative framework to generate multi-step reasoning chains, covering seven types such as event attribution and implicit inference. It uses a multi-phase evaluation pipeline assessing both outcome and process.
Key Results
- Gemini-2.0-Pro model achieved the best performance on VRBench with an accuracy of 74.61%.
- GPT-4o model scored 68.68% in outcome-level accuracy but only 56.11% in process rating.
- InternVL2.5-78B emerged as the best non-proprietary VLM with 62.31% overall accuracy.
Significance
VRBench addresses the neglect of temporal reasoning and procedural validity in existing evaluations, setting a new standard for multi-step reasoning research and advancing academia and industry.
Technical Contribution
VRBench is the first to support multi-step annotation and evaluation in video reasoning, offering new metrics and scoring mechanisms, revealing reasoning fragility in existing models.
Novelty
VRBench is the first benchmark specifically for multi-step reasoning in long narrative videos, emphasizing temporal relations and complete reasoning chains, unlike previous single-step perception evaluations.
Limitations
- VRBench's evaluation process is complex, requiring substantial human involvement, potentially affecting efficiency.
- Models perform poorly on counting problems, needing finer visual perception.
- Some models support only limited frame inputs, affecting reasoning accuracy.
Future Work
Future work can expand VRBench's language and video types, enhancing multi-language support and long video understanding capabilities.
AI Executive Summary
VRBench is the first benchmark specifically designed for multi-step reasoning in long videos, addressing the neglect of temporal reasoning and procedural validity in existing evaluations. It includes 960 long videos and 8,243 human-labeled QA pairs, curated through a multi-stage filtering process to ensure plot coherence. We developed a human-AI collaborative framework that generates coherent reasoning chains requiring multiple temporally grounded steps, spanning seven types. VRBench designs a multi-phase evaluation pipeline assessing models at both the outcome and process levels. Besides MCQs for final results, we propose a progress-level LLM-guided scoring metric to comprehensively evaluate the quality of the reasoning chain from multiple dimensions. Through extensive evaluations of 12 LLMs and 19 VLMs, we undertake a thorough analysis and provide valuable insights advancing the field of multi-step reasoning.
VRBench's construction involves collecting long narrative videos from YouTube, ensuring plot coherence through a multi-stage filtering process. We developed a human-AI collaborative framework generating reasoning chains requiring multiple temporally grounded steps, spanning seven types. VRBench designs a multi-phase evaluation pipeline assessing models at both the outcome and process levels. Besides MCQs for final results, we propose a progress-level LLM-guided scoring metric to comprehensively evaluate the quality of the reasoning chain from multiple dimensions. Through extensive evaluations of 12 LLMs and 19 VLMs, we undertake a thorough analysis and provide valuable insights advancing the field of multi-step reasoning.
VRBench's construction involves collecting long narrative videos from YouTube, ensuring plot coherence through a multi-stage filtering process. We developed a human-AI collaborative framework generating reasoning chains requiring multiple temporally grounded steps, spanning seven types. VRBench designs a multi-phase evaluation pipeline assessing models at both the outcome and process levels. Besides MCQs for final results, we propose a progress-level LLM-guided scoring metric to comprehensively evaluate the quality of the reasoning chain from multiple dimensions. Through extensive evaluations of 12 LLMs and 19 VLMs, we undertake a thorough analysis and provide valuable insights advancing the field of multi-step reasoning.
Deep Analysis
Background
The field of video reasoning has rapidly evolved, with existing standards like GSM8K focusing on domain-specific knowledge in mathematics and science but neglecting contextual analysis in visual narrative content. As vision language models develop, benchmarks rigorously evaluating complex reasoning capabilities become crucial.
Core Problem
Existing evaluations overemphasize domain expertise rather than plot-driven reasoning, lack temporally-grounded reasoning chains, and ignore procedural validity. VRBench aims to address these challenges.
Innovation
VRBench is the first benchmark specifically designed for multi-step reasoning in long narrative videos, using a human-AI collaborative framework to generate reasoning chains and a multi-phase evaluation pipeline assessing both outcome and process.
Methodology
- �� Collect 960 long videos ensuring plot coherence
- �� Generate reasoning chains using human-AI collaborative framework
- �� Design multi-phase evaluation pipeline assessing both outcome and process
- �� Propose progress-level LLM-guided scoring metric
Experiments
Evaluated 12 LLMs and 19 VLMs on VRBench using a multi-phase evaluation pipeline assessing both outcome and process. Extensive experimental analysis reveals reasoning fragility in existing models.
Results
Gemini-2.0-Pro model achieved the best performance on VRBench with an accuracy of 74.61%. GPT-4o model scored 68.68% in outcome-level accuracy but only 56.11% in process rating.
Applications
VRBench can be used to evaluate models' multi-step reasoning capabilities, advancing the field of video understanding, particularly in analyzing temporal relations in long videos.
Limitations & Outlook
VRBench's evaluation process is complex, requiring substantial human involvement, potentially affecting efficiency. Models perform poorly on counting problems, needing finer visual perception.
Plain Language Accessible to non-experts
Imagine you're watching a movie with many characters and plots. VRBench is like a smart assistant helping you understand each character's motivations and the relationships between events. It analyzes each segment of the movie to find out why characters do certain things and the cause-and-effect relationships between events. Just like when you're watching a movie, the assistant tells you the mood changes of characters and the direction of the story. VRBench uses multi-step reasoning to help you better understand the complex plot of the movie.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a super complex game with lots of characters and quests. VRBench is like a super smart game assistant helping you understand why each character completes a certain quest and the relationships between quests. It analyzes each scene of the game to tell you the motivations of characters and the cause-and-effect relationships of quests. Just like when you're playing a game, the assistant tells you the mood changes of characters and the direction of the story. VRBench uses multi-step reasoning to help you better understand the complex plot of the game!
Glossary
Multi-step reasoning
A reasoning process involving multiple steps, often requiring temporal coherence.
Used in VRBench to evaluate models' reasoning capabilities.
Vision Language Model
A model that analyzes both visual and language information.
Used in VRBench for understanding and reasoning about video content.
Human-AI collaborative framework
A workflow involving both humans and AI to generate reasoning chains.
Used to generate high-quality reasoning chains in VRBench.
Temporal reasoning
Analyzing the relationships and causal chains of events over time.
VRBench emphasizes the importance of temporal reasoning.
Progress-level scoring
A scoring mechanism to evaluate the quality of the reasoning process.
Used to comprehensively evaluate the quality of reasoning chains.
Open Questions Unanswered questions from this research
- 1 How to improve models' performance on counting problems? Current models still lack in visual perception.
- 2 How to reduce human involvement in VRBench's evaluation process? Automation needs to be improved.
Applications
Immediate Applications
Video content analysis
VRBench can be used to analyze complex plots in long videos, helping understand character motivations and event relationships.
Long-term Vision
Intelligent video assistant
In the future, VRBench could develop into an intelligent video assistant, analyzing movie and video content in real-time, providing viewing suggestions.
Abstract
We present VRBench, the first long narrative video benchmark crafted for evaluating large models' multi-step reasoning capabilities, addressing limitations in existing evaluations that overlook temporal reasoning and procedural validity. It comprises 960 long videos (with an average duration of 1.6 hours), along with 8,243 human-labeled multi-step question-answering pairs and 25,106 reasoning steps with timestamps. These videos are curated via a multi-stage filtering process including expert inter-rater reviewing to prioritize plot coherence. We develop a human-AI collaborative framework that generates coherent reasoning chains, each requiring multiple temporally grounded steps, spanning seven types (e.g., event attribution, implicit inference). VRBench designs a multi-phase evaluation pipeline that assesses models at both the outcome and process levels. Apart from the MCQs for the final results, we propose a progress-level LLM-guided scoring metric to evaluate the quality of the reasoning chain from multiple dimensions comprehensively. Through extensive evaluations of 12 LLMs and 19 VLMs on VRBench, we undertake a thorough analysis and provide valuable insights that advance the field of multi-step reasoning.