## Bar Chart: Performance Comparison of Natural-SFT and FLV-SFT Across Domains
### Overview
The chart compares the number of correct answers achieved by two methods, **Natural-SFT** (blue) and **FLV-SFT** (red), across three domains: **Logical**, **Mathematical**, and **General**. Percentage improvements for FLV-SFT over Natural-SFT are annotated in green text above each red bar.
### Components/Axes
- **X-axis (Domain)**: Categorized into "Logical," "Mathematical," and "General."
- **Y-axis (Number of Correct Answers)**: Ranges from 0 to 350 in increments of 50.
- **Legend**: Located in the top-right corner, with:
- **Blue**: Natural-SFT
- **Red**: FLV-SFT
- **Annotations**: Green text above each red bar indicates percentage improvement (e.g., "+32.8%").
### Detailed Analysis
1. **Logical Domain**:
- Natural-SFT: 219 correct answers.
- FLV-SFT: 291 correct answers (+32.8% improvement).
2. **Mathematical Domain**:
- Natural-SFT: 163 correct answers.
- FLV-SFT: 243 correct answers (+49.3% improvement).
3. **General Domain**:
- Natural-SFT: 166 correct answers.
- FLV-SFT: 213 correct answers (+28.5% improvement).
### Key Observations
- FLV-SFT consistently outperforms Natural-SFT in all domains.
- The largest improvement is in the **Mathematical domain** (+49.3%), followed by **Logical** (+32.8%) and **General** (+28.5%).
- Natural-SFT shows relatively stable performance across domains, with minor variation (219 → 163 → 166).
### Interpretation
The data demonstrates that **FLV-SFT significantly enhances performance** across all tested domains, with the most pronounced gains in **Mathematical tasks** (+49.3%). This suggests FLV-SFT may be particularly effective in structured, rule-based reasoning. The smaller improvement in the **General domain** (+28.5%) implies potential limitations in handling less structured or ambiguous problems. The consistent outperformance of FLV-SFT highlights its methodological advantages, possibly due to architectural or training differences not specified in the chart. The y-axis scale (0–350) contextualizes the absolute performance, showing FLV-SFT achieves 60–80 more correct answers per domain than Natural-SFT.
