docs: update Results with full experiment tables

README.md

@@ -122,26 +122,15 @@ On non-NVIDIA chips, the default dataset is automatically set to `KernelGenBench
 
 ### Multi-Source (NVIDIA A100, 210 operators)
 
-All results use Claude Opus-4.6. Acc = accuracy (%), Spd = geometric mean speedup relative to PyTorch/cuBLAS baseline.
+Evaluation across 210 operators from three sources (ATen, vLLM, cuBLAS), showing accuracy and speedup by operator source across all generation paradigms.
 
-| Method | Overall Acc | Overall Spd | ATen Acc | ATen Spd | vLLM Acc | vLLM Spd | cuBLAS Acc | cuBLAS Spd |
-|--------|:-----------:|:-----------:|:--------:|:--------:|:--------:|:--------:|:----------:|:----------:|
-| Pass@1 | 41 | 0.70 | 39 | 0.90 | 20 | 0.76 | 68 | 0.49 |
-| Pass@5 | 57 | 0.68 | 62 | 0.79 | 28 | 0.71 | 74 | 0.49 |
-| Claude Code | 87 | 0.78 | 92 | 0.86 | 68 | 1.02 | 94 | 0.51 |
-| OpenCode | 81 | 0.73 | 92 | 0.82 | 46 | 0.97 | 92 | 0.50 |
-| AKO4all | 83 | 0.97 | 91 | 1.00 | 64 | 1.62 | 84 | 0.61 |
+![Multi-Source Results](assets/table_multi_source.png)
 
 ### Multi-Chip (110 ATen operators, 6 platforms)
 
-All results use Claude Opus-4.6. Platforms A–E are anonymized vendor hardware.
+Cross-platform evaluation on 110 ATen operators across six hardware platforms, showing whether correctness and speedup transfer across heterogeneous hardware backends. Platforms A–E are anonymized vendor hardware.
 
-| Method | NVIDIA Acc/Spd | Platform A Acc/Spd | Platform B Acc/Spd | Platform C Acc/Spd | Platform D Acc/Spd | Platform E Acc/Spd |
-|--------|:--------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|
-| Pass@1 | 39 / 0.90 | 46 / 0.19 | 44 / 0.69 | 37 / 0.98 | 38 / 0.89 | 38 / 0.88 |
-| Pass@5 | 62 / 0.79 | 63 / 0.15 | 60 / 0.74 | 54 / 0.92 | 65 / 0.68 | 57 / 0.83 |
-| Claude Code | 92 / 0.86 | 89 / 0.18 | 93 / 0.80 | 88 / 0.87 | 96 / 0.89 | 83 / 0.83 |
-| AKO4all | 89 / 1.00 | 84 / 0.30 | 88 / 1.09 | 88 / 1.08 | 86 / 1.12 | 80 / 1.07 |
+![Multi-Chip Results](assets/table_multi_chip.png)
 
 ![Cross-platform accuracy, speedup, and ecosystem overhead](assets/figure_crossplatform.png)