AI RESEARCH BREAKTHROUGH
Unlocking AI's Cognitive Frontier: RISE-Video Benchmark Reveals Deep Reasoning Gaps in Video Generation
Our pioneering RISE-Video benchmark uncovers critical limitations in how current Text-Image-to-Video models internalize and reason over implicit world rules, extending beyond mere visual fidelity to deep cognitive challenges.
Executive Impact: Key Performance Metrics
RISE-Video's comprehensive evaluation highlights critical areas for improvement in AI video generation, indicating a significant gap between visual fidelity and true cognitive reasoning.
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Best Reasoning Alignment
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Best Temporal Consistency
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Best Physical Rationality
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Highest Visual Quality Achieved
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Overall Model Accuracy
Deep Analysis & Enterprise Applications
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22.5%
Average Accuracy for Reasoning Tasks
Despite advancements in visual fidelity, the best-performing models achieve an accuracy of only 22.5% on reasoning-oriented tasks, highlighting a significant gap in their ability to decode implicit world rules.
96.2%
Highest Visual Quality Achieved
While models excel at visual quality (up to 96.2% for Seedance 1.5-pro), this does not translate to robust reasoning capabilities. This dichotomy underscores the need for new evaluation paradigms.
Reasoning Alignment Evaluation Flow
Input Video Frames
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LMM-as-Judge Analysis
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Answer Knowledge-aware Questions
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Binary (Yes/No) Scoring
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Compute Reasoning Alignment
| Metric | Focus | Key Feature |
|---|---|---|
| Reasoning Alignment | Correctness of inferred relationships, changes, outcomes. | Targeted LMM questioning. |
| Temporal Consistency | Stability of non-instructed elements over time. | 1-5 scale, uniform frame sampling. |
| Physical Rationality | Adherence to physical laws and real-world logic. | Excludes abstract puzzles, LMM verifies accuracy. |
| Visual Quality | Perceptual fidelity and technical integrity of video. | 1-3 scale, super-resolution for fair assessment. |
Closed-Source vs. Open-Source Divide
Our evaluation reveals a consistent performance gap: closed-source models significantly outperform open-source counterparts in both reasoning capability and visual quality. This suggests advanced proprietary architectures and larger-scale data are critical for current state-of-the-art TI2V systems.
For instance, Hailuo 2.3 leads with 76.6% RA, while top open-source models like Wan2.2-I2V-A14B only reach 39.5%. This indicates a need for more research into efficient training and architectures for publicly available models to catch up.
| Category | Best Performer | Key Takeaway |
|---|---|---|
| Perceptual Knowledge | Hailuo 2.3 (86.7%) | Models are strong at low-level visual attributes. |
| Logical Capability | Hailuo 2.3 (55.6%) | Consistently low scores across all models; a major bottleneck. |
| Experiential Knowledge | Hailuo 2.3 (85.4%) | Some models infer implicit actions (e.g., unscrewing bottle cap) well. |
| Temporal Consistency | Sora 2 (92.2%) | Excels at preserving non-instructed elements and stable generation. |
Calculate Your Potential ROI
Estimate the potential efficiency gains and cost savings by integrating advanced AI video generation into your enterprise workflows.
Estimated Annual Savings
Hours Reclaimed Annually
Your Enterprise AI Roadmap
A phased approach to integrate and maximize the impact of advanced AI video generation within your organization.
Phase 1: Initial Assessment & Pilot
Identify core use cases, integrate a pilot TI2V system, and conduct initial performance benchmarks with RISE-Video.
Phase 2: Custom Model Training & Refinement
Fine-tune models on proprietary data, leverage RISE-Video for targeted reasoning improvements, and iterate on generation quality.
Phase 3: Scaled Deployment & Continuous Monitoring
Roll out AI-generated video solutions across relevant departments, establish monitoring for rule adherence and quality, and measure ROI.
Phase 4: Advanced Reasoning Integration
Explore integrating deeper cognitive reasoning capabilities into custom models, expanding beyond current TI2V limitations for complex, implicit world rule simulations.
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