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全球雙榜SOTA！明略科技專有大模型 Mano開啟GUI智能操作新時代

2025-09-28

明略科技推出的GUI大模型Mano在Mind2Web和OSWorld兩大基準測試中取得了創(chuàng)紀錄的SOTA成績，成功率達到40.1%。通過在線強化學習和自動數(shù)據(jù)采集，Mano為GUI智能體領域提供了可擴展的新范式，顯著提升了復雜任務的執(zhí)行能力。該技術的突破不僅推動了自動化邊界的擴展，也為企業(yè)智能化轉型提供了強有力的支持

官宣！明略科技推出專有大模型產(chǎn)品線DeepMiner，可信商業(yè)數(shù)據(jù)分析智能體終于能用了！

2025-09-22

明略科技推出專有大模型產(chǎn)品線DeepMiner，以“可信智能體+可信數(shù)據(jù)”雙輪驅動，解決企業(yè)智能體落地中幻覺率高、過程不透明等痛點。該產(chǎn)品通過多智能體協(xié)同架構、企業(yè)級數(shù)據(jù)整合及全流程透明化設計，降低幻覺率并支持知識沉淀，其自研Mano和Cito模型分別實現(xiàn)精準執(zhí)行與深度推理，助力企業(yè)構建可信生產(chǎn)力。

明略科技 Mano Technical Report

2025-09-18

Graphical user interfaces (GUIs) are the primary medium for human-computer interaction, yet automating GUI interactions remains challenging due to the complexity of visual elements, dynamic environments, and the need for multi-step reasoning. Existing methods based on vision-language models (VLMs) often suffer from limited resolution, domain mismatch, and insufficient sequential decisionmaking capability. To address these issues, we propose Mano, a robust GUI agent built upon a multi-modal foundation model pre-trained on extensive web and computer system data. Our approach integrates a novel simulated environment for high-fidelity data generation, a three-stage training pipeline (supervised fine-tuning, offline reinforcement learning, and online reinforcement learning), and a verification module for error recovery. Mano demonstrates state-of-the-art performance on multiple GUI benchmarks, including Mind2Web and OSWorld, achieving significant improvements in success rate and operational accuracy. Our work provides new insights into the effective integration of reinforcement learning with VLMs for practical GUI agent deployment, highlighting the importance of domain-specific data, iterative training, and holistic reward design.