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.

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.

Compressing long chain-of-thought (CoT) from large language models (LLMs) is an emerging strategy to improve reasoning efficiency. Despite its promising benefits, existing studies equally compress all thoughts within a long CoT, hindering more concise and effective reasoning. To this end, we first investigate the importance of different thoughts through automatic long CoT chunking and Monte Carlo rollouts. Building on this, we propose a theoretically bounded metric to jointly measure the effectiveness and efficiency of different thoughts. We then propose Long Short, an efficient reasoning framework that enables two LLMs to collaboratively solve problems: a long-thought LLM for effectively generating important thoughts, and a short-thought LLM for efficiently generating remaining thoughts. Specifically, we synthesize cold-start data to fine-tune LLMs for long-thought and short-thought reasoning styles, respectively. Furthermore, we propose synergizing-oriented multi-turn reinforcement learning, focusing on model self-evolution and collaboration between long-thought and short-thought LLMs. Experimental results show that our method outperforms seven efficient reasoning methods and enables Qwen2.5-7B to achieve comparable performance to DeepSeek-R1-Distill-Qwen-7B, while reducing token length by over 80% across five reasoning benchmarks. Our code and data are available at https://github.com/usail-hkust/LongShort.