Update README.md

README.md

@@ -14,7 +14,7 @@ We're introducing Ling-3.0-flash, our next-generation native hybrid reasoning mo
 
 + **Native Hybrid-Linear Architecture:** Ling-3.0 adopts a native hybrid linear attention architecture from the very start of pretraining (5:1 alternating stacking of Kimi Delta Attention (KDA) and MLA), upgraded with KDA fine-grained diagonal gating and 1/64 sparse MoE. With 124B total parameters and 5.1B activated parameters, it achieves a synergistic leap in long-context efficiency and computational cost.
 + **Remarkable Efficiency & Performance:** Engineered for speed, compute efficiency, and production deployment, Ling-3.0-flash delivers class-defying performance against both larger SOTA competitors and previous-generation flagships. Activating only 5.1B parameters per token, it provides impressive reasoning, instruction following, and long-context capabilities to empower complex agentic workflows in production environments.
-+ **Comprehensive Agentic Evolution:** Tailored for real-world productivity workflows, the model incorporates 10,000+ interactive training environments to achieve end-to-end closed-loop execution across Coding, General, and Deep Research Agent tasks. It natively integrates the SGLang HiCache + Mooncake hierarchical caching architecture (featuring physical dual-pools and a cluster-shared L3 cache), eliminating redundant recomputation during long-horizon interactions and reducing Time to First Token (TTFT) by 60% to over 80% in long-input scenarios.s the SGLang HiCache + Mooncake hierarchical caching architecture (featuring physical dual-pools and a cluster-shared L3 cache), eliminating redundant recomputation during long-horizon interactions and reducing Time to First Token (TTFT) by 60% to over 80% in long-input scenarios.
++ **Comprehensive Agentic Evolution:** Tailored for real-world productivity workflows, the model incorporates 10,000+ interactive training environments to achieve end-to-end closed-loop execution across Coding, General, and Deep Research Agent tasks. It natively integrates the SGLang HiCache + Mooncake hierarchical caching architecture (featuring physical dual-pools and a cluster-shared L3 cache), eliminating redundant recomputation during long-horizon interactions and reducing Time to First Token (TTFT) by 60% to over 80% in long-input scenarios.
 
 <!-- 这是一张图片，ocr 内容为：SWE-BENCH MULTILINGUAL TERMINAL-BENCH 2.1 SWE-BENCHPRO TAU3-BANKING-AA LL088000LL 65 6 71.2 76.5 75.9 AP 28.0 60 0 72.4  73.3 56.6 56.2  56.3 71.0 53.9 70 22.9 55 57.0 50 47.9 48.3 60 56.7 14.6 45 11.3 71.3 39.3 39.0 70.1 50 8.9 40 42.7 40 34.1 35 30 30 WIDESEARCH MCP-ATLAS SKILLSBENCH BROWSECOMP 品质88品质品等学导新品 品民品品导品只只口 豆豆复品品导导导 品品&品品定品名品 6 4 79.5 米 0 75.2 73.6 74.4 70.2 69.0 82.0 66.7 6 65.5 62.2 53.5 75.8 61.2 73.2 71.7 74.0 55.2 53.6 52.6 49.4 G 20.3 19.5 71.9 31.3 MULTI-AGENT IFBENCH SYSBENCH MRCR-256K MULTI-IF 100 雪饼 110 110 87.7  89.3 79.2 06 82.9 84.6 86.2 84.6 84.8 75.7 82.3 93.6 91.4  93.9 90793.3949 72.6 84.3 90 品8888元 81.1 86.5 86.2 69.0 70 67.3 70 60 56.6 05 30 0 30 10 ULIO RING-2.6-1T(XHIGH) STEP-3.7-FLASH(HIGH) LING-3.0-FLASH MINIMAX-M2.7 WALBAN DEEPSEEK-V4-FLASH-PREVIEW(MAX) CLAUDE-SONNET-4.6(MAX) GPT-5.4-MINI(HIGH) NEMOTRON-3-SUPER-120B-A12B NOTE:THINKING MODE IS ENABLED BY DEFAULT. -->
 ![](https://intranetproxy.alipay.com/skylark/lark/0/2026/png/23157180/1785831264180-d6ca4404-acef-4424-84db-fbc5a4c6db5f.png)