Existing Chinese preference datasets suffer from limited scale, restricted domain coverage, and insufficiently rigorous data validation. Human annotation significantly limits the scalability of human preference datasets. As a result, Chinese Alignment and Chinese Reward Models (CRM) have not yet been thoroughly explored. To address these challenges, we design an LLM-based data annotation pipeline with no human intervention. Based on this pipeline, we curate COIG-P (Chinese Open Instruction Generalist - Preference), a high-quality, large-scale Chinese preference dataset consisting of 1M Chinese preference pairs and 92k carefully curated Chinese queries across diverse domains, including Chat, Coding, Maths, and others. We conduct experiments to verify the quality of COIG-P from two perspectives. (1) COIG-P brings significant performance improvements for the Qwen2/2.5 and Infinity-Instruct model series on AlignBench through DPO, with gains ranging from 2% to 12%. Furthermore, it significantly outperforms other existing Chinese preference datasets. (2) We train an 8B-sized CRM and manually annotate a Chinese Reward Benchmark (CRBench). Our CRM demonstrates robust scoring ability on CRBench. In addition, in practical data construction experiments, the quality of the data constructed by our CRM is comparable to that produced by GPT-4o.

MLLM-based GUI grounding methods commonly formulate target localization as autoregressive coordinate generation, enabling models to leverage strong instruction following and semantic understanding. However, by casting localization as coordinate-token prediction, these methods rely on implicit decoding to convert region-level target evidence into precise click coordinates, making point-level spatial alignment unreliable. Through diagnostic analysis, we find that intermediate decoder layers already exhibit target-region awareness, yet such region-level evidence fails to guide final point-level decisions. To address this, we propose InnerZoom, a single-forward cross-layer evidence bridging framework that refines target evidence inside the model during decoding, avoiding extra inferences required by Zoom-In. InnerZoom reframes coordinate decoding as an evidence-preserving process by identifying target-related cues within the original forward pass, refining them across decoder layers, and reinjecting the refined evidence into coordinate prediction. Extensive experiments show that InnerZoom-4B achieves state-of-the-art performance on all six mainstream GUI grounding benchmarks, outperforming the second-best result by 4.1 points on OSWorld-G.Under the 4B setting, InnerZoom improves the same SFT+RL baseline by 5.3 points on average and outperforms two-pass Zoom-In by 1.3 points on average, while reducing measured GPU inference latency by 44% and estimated TFLOPs by 29%. Code and models will be at https://github.com/YenanLiu/InnerZoom.

While AndroidWorld has become the dominant mobile-use benchmark due to its reproducible environment and deterministic evaluation, recent agents achieving over 90% success rates indicate saturation and motivate the need for greater challenge. In addition, its environment lacks key application categories, such as e-commerce and enterprise communication, and does not reflect realistic mobile-use scenarios characterized by vague user instructions and hybrid tool usage. We introduce MobileWorld, a substantially more challenging benchmark with 201 tasks across 20 applications that reflects real-world usage through long-horizon, cross-application workflows requiring nearly twice as many steps (27.8 vs. 14.3) and featuring significantly more multi-app tasks (62.2% vs. 9.5%) than AndroidWorld. MobileWorld balances production-grade utility and reproducible evaluation using open-source alternatives to industry standards (e.g., Mattermost for Slack), enabling full observability through source code modification and direct database access. Beyond standard GUI manipulation, MobileWorld introduces novel task categories including agent-user interaction and Model Context Protocol (MCP)-augmented tasks for evaluating agents in user-aware, hybrid-tool scenarios. We develop a planner-executor framework with extended action spaces supporting user interactions and MCP calls. Results show a sharp performance drop from AndroidWorld, with the best agentic framework and end-to-end model achieving 51.7% and 20.9% success rates, respectively, highlighting substantial room for future research.