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README.md

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-**Highlights:**
+## Model Self-Evolution
 
-- **Software Engineering:** SWE-Pro 56.22%, SWE Multilingual 76.5, Multi SWE Bench 52.7, VIBE-Pro 55.6%, Terminal Bench 2 57.0%
-- **Professional Work:** GDPval-AA ELO 1495 (highest among open-source), Toolathon 46.3%, MM Claw 62.7%, 97% skill adherence across 40+ complex skills
-- **Self-Evolution:** Autonomous harness optimization achieving 30% improvement over 100+ iterations; MLE Bench Lite 66.6% medal rate
-- **Entertainment:** Strong character consistency and emotional intelligence; open-sourced [OpenRoom](https://github.com/MiniMax-AI/OpenRoom) interactive demo
+M2.7 initiates a cycle of model self-evolution: during development, we let the model update its own memory, build dozens of complex skills for RL experiments, and improve its own learning process based on experiment results. An internal version of M2.7 autonomously optimized a programming scaffold over 100+ rounds — analyzing failure trajectories, modifying code, running evaluations, and deciding to keep or revert — achieving a **30% performance improvement**. On MLE Bench Lite (22 ML competitions), M2.7 achieved a **66.6% medal rate**, second only to Opus-4.6 and GPT-5.4.
+
+## Professional Software Engineering
+
+M2.7 delivers outstanding real-world programming capabilities spanning log analysis, bug troubleshooting, refactoring, code security, and machine learning. Beyond code generation, M2.7 demonstrates strong system-level reasoning — correlating monitoring metrics, conducting trace analysis, verifying root causes in databases, and making SRE-level decisions. Using M2.7, we have reduced live production incident recovery time to **under three minutes** on multiple occasions.
+
+On SWE-Pro, M2.7 achieved **56.22%**, matching GPT-5.3-Codex, with even stronger performance on real-world engineering benchmarks: **SWE Multilingual (76.5)** and **Multi SWE Bench (52.7)**. On **VIBE-Pro (55.6%)**, M2.7 is nearly on par with Opus 4.6. On **Terminal Bench 2 (57.0%)** and **NL2Repo (39.8%)**, M2.7 demonstrates deep understanding of complex engineering systems. M2.7 also supports native **Agent Teams** for multi-agent collaboration with stable role identity and autonomous decision-making.
+
+## Professional Work
+
+M2.7 achieved an **ELO score of 1495** on GDPval-AA (highest among open-source models), surpassing GPT5.3. It handles Word, Excel, and PPT with high-fidelity multi-round editing, producing editable deliverables. On Toolathon, M2.7 reached **46.3%** accuracy (global top tier), and maintains **97% skill compliance** across 40+ complex skills on MM Claw. On the MM Claw end-to-end benchmark, M2.7 achieved **62.7%**, close to Sonnet 4.6.
+
+## Entertainment
+
+M2.7 features strengthened character consistency and emotional intelligence. We open-sourced [OpenRoom](https://github.com/MiniMax-AI/OpenRoom), an interactive demo that places AI interaction within a Web GUI space with real-time visual feedback and scene interactions. Try it at [openroom.ai](https://www.openroom.ai/).
 
 ## How to Use