Integrating explicit Chain-of-Thought (CoT) into end-to-end spoken dialogue models enhances intelligence but incurs prohibitive latency. While the "Thinking-while-Talking" paradigm alleviates this delay, it fundamentally compromises block atomicity, severing the logical connection between interleaved thought and speech. To address this, we present Dual-Reasoner, employing a Streaming Masking Mechanism underpinned by our Dual-Think-30k dataset to guarantee uninterrupted audio streaming. Crucially, to strictly align the fragmented thinking blocks to service speech generation, we introduce the Atomic-Consistency Restoration framework. To secure comprehensive capabilities in high-difficulty reasoning, this mechanism utilizes a quadruple-constraint system to reconstruct logical atomicity, ensuring that "think" chunks act as a rigorous anchor for "talk" outputs. Experimental results demonstrate that Dual-Reasoner achieves comprehensive reasoning enhancements within ultra-low latency constraints: it elevates the VoiceBench score from 67.24 to 73.41 over the baseline, while significantly reducing the Time-to-First-Audio (TTFA) from 20.35s to 3.65s and the Real-Time Factor (RTF) from 7.04 to 1.05.

Graphical User Interface (GUI) agents aim to automate a wide spectrum of human tasks by emulating user interaction. Despite rapid advancements, current approaches are hindered by several critical challenges: data bottleneck in end-to-end training, high cost of delayed error detection, and risk of contradictory guidance. Inspired by the human cognitive loop of Thinking, Alignment, and Reflection, we present D-Artemis—a novel deliberative framework in this paper. D-Artemis leverages a fine-grained, app-specific tip retrieval mechanism to inform its decision-making process. It also employs a proactive Pre-execution Alignment stage, where Thought-Action Consistency (TAC) Check module and Action Correction Agent (ACA) work in concert to mitigate the risk of execution failures. A post-execution Status Reflection Agent (SRA) completes the cognitive loop, enabling strategic learning from experience. Crucially, D-Artemis enhances the capabilities of general-purpose Multimodal large language models (MLLMs) for GUI tasks without the need for training on complex trajectory datasets, demonstrating strong generalization. D-Artemis achieves SOTA among open-source general models on AndroidWorld (75.8%) and ScreenSpot-V2 (96.8%). Extensive ablation studies further demonstrate the significant contribution of each proposed component.