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2.31 TB
732,204 files
Updated about 14 hours ago
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| Name | Size | Uploaded | Xet hash |
|---|---|---|---|
| README.md | 1.56 kB xet | ba99e2d3 | |
| Visual-OPSD-compare.png | 573 kB xet | e77c3051 | |
| Visual-OPSD-pipeline.png | 574 kB xet | 5cea5a4c | |
| attention_analysis.png | 189 kB xet | a5809087 | |
| latency_comparison.png | 93.5 kB xet | 77591dff | |
| main_results1.png | 333 kB xet | 06e84bae | |
| pilot_intervention.png | 92 kB xet | 03c0beb2 |
Paper figures
This directory contains the figures referenced from the project's
README.md. They are the (PNG-rendered) originals used in the paper
Visual-OPSD: Cross-Modal On-Policy Self-Distillation for Efficient
Unified Multimodal Reasoning.
| File | Paper figure | Caption (short) |
|---|---|---|
main_results.png |
Figure 1 | Headline results: per-benchmark accuracy, Δ-vs-teacher, and accuracy–latency Pareto curve. Visual-OPSD (green) matches or beats the VT teacher on 6/9 benchmarks and dominates the Pareto front at 74.03 % / 10.0 s. |
pilot_intervention.png |
Figure 2 | Pilot intervention on the frozen ThinkMorph teacher: removing or corrupting the intermediate VT images barely changes accuracy across all 9 benchmarks, motivating the move from generative VT to distillation. |
latency_comparison.png |
Figure 3 | Per-sample inference latency on a single H800 — Visual-OPSD is 14.3× faster than the VT teacher because it never invokes the diffusion pathway. |
attention_analysis.png |
Figure 5 | V* attention-pattern analysis: once a VT is rendered, downstream reasoning attends almost exclusively to the generated image and largely ignores the original input — evidence that VTs concentrate task-relevant visual signal. |
The original LaTeX / PDF sources for these figures live in the paper's
supplementary material. They are bundled here as PNGs so the project
README.md can render inline on GitHub.
- Total size
- 2.31 TB
- Files
- 732,204
- Last updated
- Oct 21
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