## Radar Charts: Object and Spatial Cognition Performance
### Overview
The image presents two radar charts, labeled (a) Object Cognition and (b) Spatial Cognition. Each chart displays the performance of six different models – Gemini-2.5-Pro, Qwen2.5-VL-72B, VideoRefer-VL3-7B, RoboBrain-2.0-32B, RGA3-7B, and RynnEC-7B (labeled as "Ours") – across various cognitive attributes. The charts use a radial scale to represent performance, with higher values indicating better performance.
### Components/Axes
Each radar chart has the following components:
* **Center:** Represents the minimum performance value (presumably 0).
* **Radial Axes:** Represent different cognitive attributes.
* **Legend:** Located at the top of the image, associating colors with each model.
* **Labels:** Each radial axis is labeled with a specific cognitive attribute.
**Object Cognition (a):**
* Attributes: Situational Seg., Direct Seg., Counting, Size, Surface, Function, Position, State, Shape, Material, Color, Category.
* Scale: The radial axes extend to a maximum value of approximately 70.
**Spatial Cognition (b):**
* Attributes: Trajectory Review, Relative Position, Absolute Position, Object Distance, Object Size, Object Height, Spatial Imagery, Movement Imagery, Egocentric Distance, Egocentric Direction.
* Scale: The radial axes extend to a maximum value of approximately 80.
**Legend:**
* Gemini-2.5-Pro: Orange
* Qwen2.5-VL-72B: Light Blue
* VideoRefer-VL3-7B: Green
* RoboBrain-2.0-32B: Red
* RGA3-7B: Purple
* RynnEC-7B (Ours): Dark Blue
### Detailed Analysis or Content Details
**Object Cognition (a):**
* **Gemini-2.5-Pro (Orange):** Shows relatively high performance in Category (approx. 60), Color (approx. 60), and Material (approx. 68). Performance dips significantly in Counting (approx. 11) and Direct Seg. (approx. 45). The line generally fluctuates, indicating varying performance across attributes.
* **Qwen2.5-VL-72B (Light Blue):** Exhibits moderate performance across most attributes, with a peak in Color (approx. 56) and a low point in Counting (approx. 14). The line is relatively smooth.
* **VideoRefer-VL3-7B (Green):** Shows a peak in Material (approx. 66) and a low point in Counting (approx. 26). The line is somewhat erratic.
* **RoboBrain-2.0-32B (Red):** Demonstrates relatively low performance across all attributes, with a peak in Color (approx. 38) and a low point in Counting (approx. 7).
* **RGA3-7B (Purple):** Shows moderate performance, peaking in Category (approx. 58) and dipping in Counting (approx. 16).
* **RynnEC-7B (Dark Blue):** Displays a relatively consistent performance across attributes, peaking in Category (approx. 62) and dipping in Counting (approx. 22).
**Spatial Cognition (b):**
* **Gemini-2.5-Pro (Orange):** Shows high performance in Trajectory Review (approx. 77) and Relative Position (approx. 41). Performance is lower in Object Height (approx. 21) and Movement Imagery (approx. 15).
* **Qwen2.5-VL-72B (Light Blue):** Exhibits moderate performance, peaking in Trajectory Review (approx. 40) and dipping in Movement Imagery (approx. 13).
* **VideoRefer-VL3-7B (Green):** Shows a peak in Relative Position (approx. 40) and a low point in Object Height (approx. 22).
* **RoboBrain-2.0-32B (Red):** Demonstrates relatively low performance across all attributes, peaking in Object Distance (approx. 30) and dipping in Movement Imagery (approx. 6).
* **RGA3-7B (Purple):** Shows moderate performance, peaking in Trajectory Review (approx. 41) and dipping in Object Height (approx. 21).
* **RynnEC-7B (Dark Blue):** Displays a relatively consistent performance across attributes, peaking in Trajectory Review (approx. 46) and dipping in Movement Imagery (approx. 15).
### Key Observations
* **Counting is a consistent weakness:** Across both charts, all models exhibit the lowest performance in the "Counting" attribute (Object Cognition) and "Movement Imagery" (Spatial Cognition).
* **Gemini-2.5-Pro and RynnEC-7B generally perform well:** These models consistently show higher values across most attributes in both charts.
* **RoboBrain-2.0-32B consistently underperforms:** This model exhibits the lowest values across most attributes in both charts.
* **Trajectory Review is a strength:** In the Spatial Cognition chart, several models (Gemini-2.5-Pro, RGA3-7B, and RynnEC-7B) demonstrate relatively high performance in "Trajectory Review."
### Interpretation
The radar charts provide a comparative analysis of six models' cognitive abilities in object and spatial reasoning. The data suggests that while all models possess some level of competence in these areas, significant performance variations exist. The consistent weakness in "Counting" and "Movement Imagery" across all models indicates a potential area for improvement in current AI architectures. Gemini-2.5-Pro and RynnEC-7B appear to be the most well-rounded performers, while RoboBrain-2.0-32B lags behind.
The separation of object and spatial cognition into distinct charts allows for a focused evaluation of each domain. The higher performance in "Trajectory Review" for several models in the Spatial Cognition chart suggests a stronger ability to understand and predict motion. The charts highlight the complex interplay of different cognitive attributes and provide valuable insights into the strengths and weaknesses of each model. The "Ours" model (RynnEC-7B) appears to be competitive, particularly in Object Cognition, but further investigation is needed to understand the specific architectural features contributing to its performance. The charts are useful for identifying areas where further research and development are needed to improve AI's cognitive capabilities.
