Update README.md

README.md

@@ -47,7 +47,7 @@ The end result is a model that is especially capable at reasoning and agentic ta
 US open-weight models with more than 10x the number of total parameters.
 At only 65B total parameters, the model can fit on consumer hardware such as the NVIDIA DGX Spark. 
 To encourage local model development, we provide an NVFP4 checkpoint and colab to help you get started. 
-The model is also fast at token generation when running on datacenter GPUs, easily reaching 500+ tok/s on 4xB200 prior to quantization or custom kernels. 
+The model is also fast at token generation when running on datacenter GPUs, reaching up to 500 tok/s on 4xB200 prior to quantization or custom kernels. 
 
 **Pre-training.** Ripple-65B Preview is pretrained from scratch on our 12T text-only predominantly-English and code corpus at 8K sequence length. 
 Ripple-65B Base Preview is a **pretrain-only checkpoint** without any midtraining or synthetic QA data.
@@ -77,7 +77,8 @@ All models are tested at full BF16 precision with maximum effort levels.
 
 Each lab typically only provides a limited subset of available benchmarks.
 Our table below shows official model card scores where available, and we use our own in-house eval flow for missing evals (italized).
-In cases where the harness is not specified, we use OpenCode.
+For SWE-bench Pro v2.0 Hard and DeepSWE 1.1, we use Terminus 2 for Ripple-65B Preview.
+In other cases where the harness is not specified, we use OpenCode.
 A higher number is better for all benchmarks.
 
 Replicating benchmark scores has been challenging, particularly for modern agentic evals, due to the number of factors which can cause 
@@ -107,19 +108,19 @@ Therefore, our results represent a best-effort attempt at a fair comparison.
   | HMMT Feb 2026 | 90.5 | *80.3* | *78.8* | ***93.9*** | **93.9** | 
   | **Coding & Agentic** | | | | | |
   | Terminal-Bench 2.1 (self-reported) | **80.5** | 56.4 | 63.8 | 76.4 | 80.1 |
-  | Terminal-Bench 2.1 (Terminus 2) | **80.5** | *51.3* | *55.4* | *79.8* | *67.8* |
-  | SWE-bench Verified | — | 71.9 | 77.6 | *78.7* | **81.6** |
-  | SWE-bench Pro v1.0 | — | *46.9* | 54.3 | — | **66.1** |
-  | SWE-bench Pro v2.0 Hard | — | *30.7* | *2.0* | *52.3* | **76.5** |
-  | DeepSWE 1.1 | — | *0.22* | *2.0* | **60.0** | 43.3 |
-  | SciCode | 39.4 | 44.6 | *39.2* | **58.5** | 49.2 | 
+  | Terminal-Bench 2.1 (Terminus 2) | **80.5** | *55.1* | *56.2* | *78.7* | *70.8* |
+  | SWE-bench Verified | 76.5 | 71.9 | 77.6 | *78.7* | **81.6** |
+  | SWE-bench Pro v1.0 | 61.42 | *46.9* | 54.3 | — | **66.1** |
+  | SWE-bench Pro v2.0 Hard | **79.1** | *30.7* | *2.0* | *52.3* | 76.5 |
+  | DeepSWE 1.1 | 44.2 | *0.2* | *1.8* | **60.0** | 43.3 |
+  | SciCode | 39.4 | 44.6 | *47.0* | **58.5** | 49.2 | 
   | τ³-bench banking | 31.27 | 22.6 | 23.7 | *14.4* | **36.1** |
   | AutomationBench (Public) | 36.4 | *8.4* | *14.6* | *42.4* | 39.3 | 
-  | MCP Atlas | — | *62.2* | 76.0 | **89.2** | 80.8 |
+  | MCP Atlas | 71.67 | *62.2* | 76.0 | **89.2** | 80.8 |
   | **Safety** | | | | | |
-  | FORTRESS Adversarial | 83.8 | *65.8* | 78.0 | ***89.1*** | *81.3* |
+  | FORTRESS Adversarial | 83.8 | *79.3* | 78.0 | **94.2** | *92.1* |
   | FORTRESS Benign | 95.5 | *89.1* | **95.9** | *88.1* | *86.4* |
-  | StrongREJECT | 97.5 | ***100.0*** | 98.6 | *96.6* | ***100.0*** |
+  | StrongREJECT | 97.5 | *98.2* | **98.6** | *94.7* | *95.8* |
   | **Instruction Following**  | | | | | |
   | IFBench | 79.2 | **81.7** | 79.8 | *78.6* | 79.6 |
   | **Long Context**  | | | | | |