TL;DR
Video-MME-v2 introduces a comprehensive benchmark with three-level hierarchy and group-based non-linear scoring to evaluate video MLLM robustness. Across 800 videos and 3,200 questions, human experts score 90.7 while best model Gemini-3-Pro reaches only 49.
Video-MME-v2 introduces a comprehensive benchmark with three-level hierarchy and group-based non-linear scoring to evaluate video MLLM robustness. Across 800 videos and 3,200 questions, human experts score 90.7 while best model Gemini-3-Pro reaches only 49.
34
已证实
7
证据不足
2
无法验证
N/A
可复现性
核心问题
How can we comprehensively evaluate the robustness and faithfulness of video multimodal large language models through benchmarks that assess capability consistency and reasoning coherence?
核心方法
{'approach': 'The authors created Video-MME-v2 with a three-level evaluation hierarchy (information aggregation, temporal dynamics, complex reasoning) and group-based evaluation using consistency-based and coherence-based question groups with non-linear scoring mechanisms. The benchmark contains 800 videos and 3,200 questions created through 3,300 human-hours of annotation, with over 80% of videos published in 2025 or later to minimize pretraining contamination.', 'key_components': ['The Non-Lin Score / Avg Acc ratio quantifies robustness in within-group consistency.', 'Stronger models achieve higher ratios (75% for Gemini-3-Pro, 72% for Doubao-Seed-2.0-Pro).', 'Smaller models show substantially lower ratios (around 40% for LLaVA-Video-7B).', 'Lower ratios indicate models answer only subsets of questions correctly within groups.', 'Three core capabilities identified: omni-modal aggregation, long-range temporal understanding, and complex reasoning.', 'Models with complete capability profiles (C1+C2+C3) generally achieve higher Non-Lin Scores.', 'Gemini-3-Pro with complete profile scores 49.4; MiMo-v2-Omni with same profile scores 38.6.', 'Synergy of all three capabilities is important for complex video understanding performance.', 'Larger parameter count can partly compensate for missing capabilities.', 'Qwen3.5-397B-A17B-Think (C2+C3) slightly outperforms MiMo-v2-Omni (C1+C2+C3) despite incomplete profile.'], 'section_ids': ['sec_18', 'sec_21', 'sec_22', 'sec_24']}
论点验证
已证实 (95%)
we introduce Video-MME-v2, a comprehensive benchmark designed to evaluate the robustness and faithfulness of video MLLMs for dynamic visual content comprehension. This is achieved through a novel multi-level evaluation hierarchy and a group-based evaluation strategy.
The benchmark is fully specified with detailed descriptions of the multi-level evaluation hierarchy (p_3, p_14-15) and group-based evaluation strategy (p_3, p_17-19). The paper demonstrates the benchmark through extensive experiments across multiple
The benchmark is fully specified with detailed descriptions of the multi-level evaluation hierarchy (p_3, p_14-15) and group-based evaluation strategy (p_3, p_17-19). The paper demonstrates the benchmark through extensive experiments across multiple
已证实 (95%)
Our evaluation hierarchy categorizes core video understanding skills into three progressive levels. Level 1 focuses on information aggregation, assessing the model's ability to perceive and aggregate cross-frame and cross-modal information. Level 2 examines temporal dynamics modeling, evaluating the capture of causality, state changes, and sequential order. Level 3 targets complex video reasoning, mimicking real-world scenarios to test advanced video comprehension skills such as physical understanding, social intelligence, and complex plot comprehension.
The three-level hierarchy is fully specified with concrete descriptions in p_3 and p_14-15. Each level has defined sub-categories and example tasks, making the contribution fully demonstrated.
The three-level hierarchy is fully specified with concrete descriptions in p_3 and p_14-15. Each level has defined sub-categories and example tasks, making the contribution fully demonstrated.
已证实 (95%)
Our group-based evaluation strategy assesses frontier models from two different perspectives: (1) Capability consistency, which examines the breadth of a specific fundamental perception skill through groups of tasks varying in aspect and granularity; and (2) Reasoning coherence, which measures the depth of a model's reasoning ability by presenting sequences of temporally and causally related questions that reveal whether the model can follow logical steps toward complex, high-level inference.
The group-based evaluation strategy is fully specified with detailed descriptions of capability consistency (p_17-18) and reasoning coherence (p_19) groups, including concrete examples of how each is constructed.
The group-based evaluation strategy is fully specified with detailed descriptions of capability consistency (p_17-18) and reasoning coherence (p_19) groups, including concrete examples of how each is constructed.
已证实 (95%)
we further introduce a non-linear scoring method that evaluates the joint correctness of correlated questions rather than treating them independently. It penalizes fragmented or guess-based success and enforces stepwise reasoning validity.
The non-linear scoring method is fully specified with explicit formulas in p_21-24. The quadratic suppression for consistency groups and first-error truncation for coherence groups are clearly defined.
The non-linear scoring method is fully specified with explicit formulas in p_21-24. The quadratic suppression for consistency groups and first-error truncation for coherence groups are clearly defined.
已证实 (95%)
we develop a meticulous human annotation pipeline with substantial human involvement to ensure high data quality. In total, 12 annotators and 50 reviewers contribute more than 3,300 human-hours.
Specific numbers are provided: 12 annotators, 50 reviewers, 3,300+ human-hours. These are concrete, verifiable claims about the annotation process.
Specific numbers are provided: 12 annotators, 50 reviewers, 3,300+ human-hours. These are concrete, verifiable claims about the annotation process.
已证实 (95%)
we curate a dataset of 800 videos and 3,200 questions.
Specific numbers are provided: 800 videos and 3,200 questions. These are concrete, verifiable claims about the dataset size.
Specific numbers are provided: 800 videos and 3,200 questions. These are concrete, verifiable claims about the dataset size.
已证实 (95%)
human experts achieve a score of 90.7, the bestperforming model, Gemini-3-Pro, reaches only 49.4. A significant performance gap also persists in the open-source community, where the top-performing model, Qwen3.5-397B-A17B-Think, achieves 39.1.
Specific numbers are provided: human experts 90.7, Gemini-3-Pro 49.4, Qwen3.5-397B-A17B-Think 39.1. These are concrete performance metrics.
Specific numbers are provided: human experts 90.7, Gemini-3-Pro 49.4, Qwen3.5-397B-A17B-Think 39.1. These are concrete performance metrics.
证据不足 (55%)
Failures in high-level reasoning are not solely due to insufficient reasoning ability, but are also caused by errors accumulated in earlier stages, including visual information aggregation and temporal modeling.
This is an interpretive claim about causality (error propagation). The paper shows hierarchical performance degradation but does not provide direct evidence of error propagation - no analysis of specific error cases, no demonstration that Level 1 err
This is an interpretive claim about causality (error propagation). The paper shows hierarchical performance degradation but does not provide direct evidence of error propagation - no analysis of specific error cases, no demonstration that Level 1 err
已证实 (90%)
Conventional per-question accuracy substantially overestimates model capability, whereas our group-based nonlinear scoring reveals that even state-of-the-art models lack consistency across correlated queries.
Specific numbers demonstrate the discrepancy: Gemini-3-Pro Avg Acc 66.1% vs Non-Lin Score 49.4, Doubao-Seed-2.0-Pro Avg Acc 60.5% vs Non-Lin Score 43.3. The ratio analysis (p_49) further supports the claim.
Specific numbers demonstrate the discrepancy: Gemini-3-Pro Avg Acc 66.1% vs Non-Lin Score 49.4, Doubao-Seed-2.0-Pro Avg Acc 60.5% vs Non-Lin Score 43.3. The ratio analysis (p_49) further supports the claim.
已证实 (90%)
enabling thinking modes improves performance with subtitles but can cause significant regression without textual cues, indicating current models still overweight language-based reasoning, resulting in an over-reliance on language priors.
Specific numbers are provided: Qwen3.5-122B-A10B-Think shows +3.8/+5.8 improvements with thinking mode, while KimiVL-16B shows -3.3/-3.3 regression. The pattern supports the claim about thinking modes and language priors.
Specific numbers are provided: Qwen3.5-122B-A10B-Think shows +3.8/+5.8 improvements with thinking mode, while KimiVL-16B shows -3.3/-3.3 regression. The pattern supports the claim about thinking modes and language priors.
已证实 (85%)
Omni-modal aggregation, long-context temporal modeling, and complex reasoning demonstrate synergistic improvement, yet large parameter scales can partially compensate for missing capabilities.
Specific numbers support both parts: Gemini-3-Pro (C1+C2+C3) scores 49.4, MiMo-v2-Omni (C1+C2+C3) scores 38.6; Qwen3.5-397B-A17B-Think (C2+C3) scores 39.1, slightly surpassing MiMo-v2-Omni. The synergy and scale compensation claims are supported by t
Specific numbers support both parts: Gemini-3-Pro (C1+C2+C3) scores 49.4, MiMo-v2-Omni (C1+C2+C3) scores 38.6; Qwen3.5-397B-A17B-Think (C2+C3) scores 39.1, slightly surpassing MiMo-v2-Omni. The synergy and scale compensation claims are supported by t
已证实 (95%)
we organize our dataset into three hierarchical levels comprising 12 sub-categories and over 30 task types, spanning from basic multiple-point information aggregation through dynamic temporal modeling to complex reasoning.
Specific numbers are provided: three hierarchical levels, 12 sub-categories, over 30 task types. The taxonomy is referenced in Table 4.
Specific numbers are provided: three hierarchical levels, 12 sub-categories, over 30 task types. The taxonomy is referenced in Table 4.
已证实 (90%)
Our design extends this perspective by incorporating question groups that explicitly model the relationships among related queries for both perception and reasoning, enabling a more comprehensive assessment of model understanding.
The design is fully specified with detailed descriptions of consistency-based groups (p_17-18) and coherence-based groups (p_19), including how relationships among queries are modeled.
The design is fully specified with detailed descriptions of consistency-based groups (p_17-18) and coherence-based groups (p_19), including how relationships among queries are modeled.
已证实 (95%)
we introduce a group-level non-linear metric designed to evaluate a model's robustness against related questions within a specific group.
The group-level non-linear metric is fully specified with explicit formulas in p_21-24.
The group-level non-linear metric is fully specified with explicit formulas in p_21-24.
已证实 (95%)
For consistency groups, we use a non-linear scoring function: given N correct answers out of 4 related questions, the group score is (N/4)^2. This quadratic suppression penalizes isolated correct guesses and rewards consistent performance across different facets of the same capability.
The formula (N/4)^2 is explicitly stated in p_24. This is a precise mathematical specification.
The formula (N/4)^2 is explicitly stated in p_24. This is a precise mathematical specification.
已证实 (95%)
For coherence groups, we apply a first-error truncation mechanism: starting from the first reasoning step, only the longest consecutive sequence of correct answers counts toward the score. Once an error occurs, any later correct answers are ignored.
The first-error truncation mechanism is explicitly described in p_24. This is a precise specification of the scoring rule.
The first-error truncation mechanism is explicitly described in p_24. This is a precise specification of the scoring rule.
已证实 (95%)
Over 80% of the videos in our dataset are published in 2025 or later, with nearly 40% published after October 2025. This temporal boundary ensures they are highly unlikely to have been included in the pre-training corpora of current MLLMs.
Specific numbers are provided: over 80% published in 2025 or later, nearly 40% after October 2025. These are concrete statistics about the dataset.
Specific numbers are provided: over 80% published in 2025 or later, nearly 40% after October 2025. These are concrete statistics about the dataset.
已证实 (95%)
the videos span an average duration of 10.4 minutes, with 99% under 20 minutes and 53% under 10 minutes.
Specific numbers are provided: average duration 10.4 minutes, 99% under 20 minutes, 53% under 10 minutes. Referenced in Figure 3.
Specific numbers are provided: average duration 10.4 minutes, 99% under 20 minutes, 53% under 10 minutes. Referenced in Figure 3.
已证实 (95%)
the mean and median view counts are 4.83 million and 355 thousand respectively. 84.3% of the selected videos have exceeded 10,000 views, and 94.4% exceed 1,000 views.
Specific numbers are provided: mean 4.83 million, median 355 thousand views; 84.3% exceed 10,000 views, 94.4% exceed 1,000 views. Referenced in Figure 5.
Specific numbers are provided: mean 4.83 million, median 355 thousand views; 84.3% exceed 10,000 views, 94.4% exceed 1,000 views. Referenced in Figure 5.
已证实 (90%)
we conduct real-time validation and adversarial stress-testing using frontier models (e.g., Gemini-3-Pro). This cross-check ensures the precision of question phrasing and the rigor of ground-truth settings.
The validation process using frontier models is described in p_32. The process is specified though specific validation results are not shown.
The validation process using frontier models is described in p_32. The process is specified though specific validation results are not shown.
... 共 43 个论点
可复现性评估
较低可复现性 (0%)
缺失的复现细节
- The Video-MME-v2 benchmark dataset itself is not available - no information about video sources, number of videos, video duration distribution, or how to access the dataset
- Annotation process details are missing - how questions and answers were created, who annotated them, annotation guidelines, quality control procedures
- Evaluation code is not available - exact implementation of Non-Lin Score calculation, within-group consistency measurement, and accuracy metrics
- Specific prompts and templates used for each model are not provided - critical for reproducing model responses
- Model version details and API parameters are incomplete - exact model versions, temperature settings, top-p values, max tokens, and other generation parameters
- Random seeds for reproducibility are not mentioned
- Data splits and organization details are missing - how questions are grouped, train/test splits if any
- Video preprocessing specifications are absent - resolution, format, frame extraction methodology
- Hardware and runtime environment specifications are not provided
- Detailed evaluation protocol for each model type (commercial vs open-source) is incomplete
局限性（作者自述）
- current models may perform adequately on shallow, perception-level tasks, they fundamentally lack the capability consistency and reasoning coherence required to navigate dynamic, real-world scenarios.
- current reasoning mechanisms remain imperfect and may introduce additional noise, particularly in settings where textual modality is absent.
本分析由 PDF 阅读助手 自动生成，仅供参考，不构成学术评审意见。验证结论和可复现性评估基于论文文本自动分析，可能存在偏差。原始论文请参阅 arXiv。
分析时间：2026-04-08T13:10:20+00:00 · 数据来源：Paper Collector