Update README.md

README.md

@@ -11,7 +11,8 @@
   <a href="https://tinker.thinkingmachines.ai/playground">Launch Blog</a>
 </p>
 
-Ricursive Intelligence introduces **Ripple-65B Preview**, a 65B parameter dense Transformer model pretrained on 12 trillion tokens that scores **80.52 on TerminalBench 2.1**,
+Ricursive Intelligence introduces **Ripple-65B Preview**, a 65B parameter dense Transformer model designed for agentic tasks. 
+Ripple-65B Preview delivers strong results on agentic coding, scoring **80.52 on TerminalBench 2.1** and
 surpassing significantly-larger US open-weight models such as Reflection Beam 501B (**80.1**), Meta Muse Spark Open 816B (**76.4**), 
 Thinking Machines Inkling 975B (**63.8**), and NVIDIA Nemotron 3 Ultra 550B (**56.4**). 
 
@@ -88,33 +89,33 @@ Therefore, our results represent a best-effort attempt at a fair comparison.
   | Pre-training tokens | 12T | 20T | **45T** | N/A | 23.8T |
   | Training FLOPs (6ND) | 4.7e24 | 6.6e24 | **1.1e25** | N/A | 3.3e24 |
   | **Knowledge** | | | | | |
-  | MMLU | 89.60 | *92.0* | *91.3* | **92.6** | *92.4* |
-  | MMLU-Pro | 84.15 | 86.8 | *87.3* | **87.95** | *76.5* |
-  | GPQA Diamond | 85.19 | 87.0 | 87.2 | 89.0 | **90.6** |
+  | MMLU | 89.6 | *92.0* | *91.3* | ***92.6*** | *92.4* |
+  | MMLU-Pro | 84.2 | 86.8 | *87.3* | ***88.0*** | *76.5* |
+  | GPQA Diamond | 85.2 | 87.0 | 87.2 | 89.0 | **90.6** |
   | **Math** | | | | | |
-  | AIME 2024 | 97.08 | *94.17* | *97.5* | **97.92** | *96.67* | 
-  | AIME 2025 | 98.33 | *94.17* | *91.67* | **100.0** | *99.58* |
-  | AIME 2026 | 95.00 | *95.42* | 97.1 | **98.33** | *97.7* |
-  | HMMT Feb 2026 | 90.53 | *80.3* | *78.79* | **93.94** | **93.94** | 
+  | AIME 2024 | 97.1 | *94.2* | *97.5* | ***97.9*** | *96.7* | 
+  | AIME 2025 | 98.3 | *94.2* | *91.7* | ***100.0*** | *99.6* |
+  | AIME 2026 | 95.0 | *95.4* | 97.1 | ***98.3*** | 97.7 |
+  | HMMT Feb 2026 | 90.5 | *80.3* | *78.8* | ***93.9*** | **93.9** | 
   | **Coding & Agentic** | | | | | |
-  | Terminal-Bench 2.1 (reported) | **80.52** | 56.4 | 63.8 | 76.4 | *80.1* |
-  | Terminal-Bench 2.1 (Terminus 2) | **80.52** | *51.31* | *55.43* | *79.78* | *67.79* |
-  | SWE-bench Verified | — | 71.9 | 77.6 | *78.67* | **81.6**[^n] |
-  | SWE-bench Pro v1.0 | — | *46.92* | 54.3 | — | **66.1** |
-  | SWE-bench Pro v2.0 Hard | — | *30.72* | *1.96* | *52.29* | 76.5 |
-  | DeepSWE 1.1 | — | *0.22* | *1.96* | **60.0** | 43.3 |
-  | SciCode | 39.40 | 44.6 | *39.18* | **58.5** | 49.2 | 
-  | τ³-bench banking | —| 22.6 | 23.7 | *14.43* | **36.1** |
-  | AutomationBench (Public) | — | *8.39* | *14.56* | *42.44* | 39.3 | 
+  | 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 | 
+  | τ³-bench banking | —| 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 |
   | **Safety** | | | | | |
-  | FORTRESS Adversarial | 83.83 | *65.80* | 78.0 | *89.07* | *81.33* |
-  | FORTRESS Benign | 95.47 | *89.13* | 95.9 | *88.13* | *86.40* |
-  | StrongREJECT | 97.47 | *100.0* | 98.6 | *96.59* | *100.0* |
+  | FORTRESS Adversarial | 83.8 | *65.8* | 78.0 | *89.1* | *81.3* |
+  | FORTRESS Benign | 95.5 | *89.1* | 95.9 | *88.1* | *86.4* |
+  | StrongREJECT | 97.5 | *100.0* | 98.6 | *96.6* | *100.0* |
   | **Instruction Following**  | | | | | |
-  | IFBench | 79.22 | **81.7** | 79.8 | *78.56* | 79.6 |
+  | IFBench | 79.2 | **81.7** | 79.8 | *78.6* | 79.6 |
   | **Long Context**  | | | | | |
-  | AA-LCR | 75.00 | 65.4 | *78.33* | *80.67* | **78.2** |
+  | AA-LCR | 75.0 | 65.4 | *78.3* | *80.7* | **78.2** |
 
 </div>
 
@@ -128,10 +129,10 @@ With the rise of the importance of posttraining, many modern LLM recipes now ble
 For our first model, we kept our pretraining data clean with minimal synthetic data, focusing on high quality natural data rather than aggressively targeting evals.
 We instead added significantly more synthetic data during midtraining, which allowed us to quickly iterate on our synthetic data recipe without blocking pretraining.
 
-Ripple-65B Base Preview is a pretrain-only checkpoint. The model overall compares favorably with Laguna M.1, and is comparable with DeepSeek V3.1 and V4 Flash on 
-reasoning, math, and code.
-Nemotron 3 Ultra dominates on traditional pretraining evals, likely due to their heavy use of [task-seeded synthetic Q&A data](https://huggingface.co/blog/nvidia/task-seeded-sdg).
-However, its pretraining eval performance did not translate to modern agentic tasks, likely due to Q&A being out of distribution relative to these tasks.
+Ripple-65B Base Preview is a pretrain-only checkpoint. The model overall compares favorably with Poolside Laguna M.1,
+and is comparable with DeepSeek V3.1 and V4 Flash on reasoning, math, and code.
+Nemotron 3 Ultra dominates on traditional pretraining evals, likely due to their heavy use of [task-seeded synthetic data](https://huggingface.co/blog/nvidia/task-seeded-sdg).
+However, its pretraining eval performance did not translate to modern agentic tasks, likely due to their synthetic data being out of distribution relative to these tasks.
 
 We report official model results where available.
 For our own model and for missing evals, we use our internal fork of [Olmes](https://github.com/allenai/olmes).
@@ -161,16 +162,16 @@ For our own model and for missing evals, we use our internal fork of [Olmes](htt
   | DROP (F1) | 1-shot | *77.8* | *66.8* | *81.2* | **88.6** | *87.6* |
   | HellaSwag (acc_norm) | 0-shot | *84.6* | *86.2* | *86.8* | 85.7 | ***89.1*** |
   | HellaSwag (acc_norm) | 10-shot | *87.2* | *88.1* | *89.4* | *88.8* | **90.5** |
-  | ARC-Challenge (MC acc) | 25-shot | *95.7* | *90.8* | *95.1* | *95.8* | **97.4**[^d] |
+  | ARC-Challenge (MC acc) | 25-shot | *95.7* | *90.8* | *95.1* | *95.8* | **97.4** |
   | OpenBookQA (acc_norm) | 0-shot | *47.4* | *48.6* | ***50.4*** | *48.0* | 48.6 |
   | PIQA (acc_norm) | 0-shot | *83.6* | *84.8* | ***85.2*** | *84.7* | 83.8 |
-  | WinoGrande (acc) | 5-shot | ***83.4*** | *83.2* | *83.2* | *81.4*[^a] | 79.3 |
+  | WinoGrande (acc) | 5-shot | ***83.4*** | *83.2* | *83.2* | *81.4* | 79.3 |
   | **Code & Math** | | | | | | |
-  | HumanEval (pass@1, n=20, T=0.2) | 0-shot | *70.9* | *83.4* | *69.8* | 69.5 | **83.8**[^b] |
-  | MBPP Sanitized (pass@1, n=32, T=0.2) | 3-shot | *81.5* | *81.1* | *75.2* | *74.6* | **86.0**[^c] |
+  | HumanEval (pass@1, n=20, T=0.2) | 0-shot | *70.9* | *83.4* | *69.8* | 69.5 | **83.8** |
+  | MBPP Sanitized (pass@1, n=32, T=0.2) | 3-shot | *81.5* | *81.1* | *75.2* | *74.6* | **86.0** |
   | BigCodeBench (pass@1, n=5, T=0.8) | 3-shot | *49.2* | *50.5* | ***59.9*** | 56.8 | *55.6* |
-  | GSM8K (CoT EM) | 8-shot | *88.5* | *80.4* | *79.6*[^e] | **90.8** | 88.1 |
-  | GSM1K (CoT EM) | 8-shot | *88.4* | *79.7* | *81.8*[^e] | *83.2* | ***90.2*** |
+  | GSM8K (CoT EM) | 8-shot | *88.5* | *80.4* | *79.6* | **90.8** | 88.1 |
+  | GSM1K (CoT EM) | 8-shot | *88.4* | *79.7* | *81.8* | *83.2* | ***90.2*** |
   | MATH (flex EM) | 4-shot | *59.1* | *52.5* | *59.7* | 57.4 | **82.0** |
   | MGSM (CoT EM, 11-lang avg) | 8-shot | *78.2* | *65.4* | *81.7* | 85.7 | **87.7** |