M$^2$-Miner: Multi-Agent Enhanced MCTS for Mobile GUI Agent Data Mining
Authors: Rui Lv
Juncheng Mo
Tianyi Chu
Chen Rao
Hongyi Jing
Jiajie Teng
Jiafu Chen
Shiqi Zhang
Liangzi Ding
Shuo Fang
Huaizhong Lin
Ziqiang Dang
Chenguang Ma
Lei Zhao
Venue: ICLR 2026 Poster (2026)
Keywords: GUI Agent; Vision-Language Models; Data synthesis; Monte Carlo Tree Search
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We propose M$^2$-Miner, a Monte Carlo Tree Search-based collaborative multi-agent framework, which could efficiently mine GUI interaction trajectory data, thereby reducing the high cost of manual annotation.

Abstract: Graphical User Interface (GUI) agent is pivotal to advancing intelligent human-computer interaction paradigms. Constructing powerful GUI agents necessitates the large-scale annotation of high-quality user-behavior trajectory data (i.e., intent–trajectory pairs) for training. However, manual annotation methods and current GUI agent data mining approaches typically face three critical challenges: high construction cost, poor data quality, and low data richness. To address these issues, we propose M$^2$-Miner, the first low-cost and automated mobile GUI agent data-mining framework based on Monte Carlo Tree Search (MCTS). For better data mining efficiency and quality, we present a collaborative multi-agent framework, comprising InferAgent, OrchestraAgent, and JudgeAgent for guidance, acceleration, and evaluation. To further enhance the efficiency of mining and enrich intent diversity, we design an intent recycling strategy to extract extra valuable interaction trajectories. Additionally, a progressive model-in-the-loop training strategy is introduced to improve the success rate of data mining. Extensive experiments have demonstrated that the GUI agent fine-tuned using our mined data achieves state-of-the-art performance on several commonly used mobile GUI benchmarks. Our work will be released to facilitate the community research.

Published as a conference paper at ICLR 2026
M MULTI-AGENT ENHANCED MCTS FOR MOBILE GUI AGENT DATA MINING
Rui Ly Juncheng Mo Tianyi Chu², Chen Raol.2, H.ongii Jing', Jiajee Teng', Jiafu Chen 1.2 Shiqi Zhang', Liangzi Ding', Shuo Fang", Huaizhong Lin",
Ziqiang Dangl. Chenguang Mat.a, Lei Zhao
Ant Group, "Zhejiang University
Zi quang@aQ@@ma com; chenguang. wwggantgrou.......oooo. com; cazhl 1eij..ej..eue
ABSTRACT
Graphical User Interface (GUI) agent is pivotal to advancing intelligent human- computer interaction paradigms. Constructing powerful GUI agents necessi- tates the large-scale annotation of high-quality user-behavior trajectory data (i.e.. intent-trajectory pairs) for training. However, manual annotation methods and current GUI agent data mining approaches typically face three critical challenges: high construction cost, poor data quality, and low data richness. To address these issues, we propose Ma-Miner, the first low-cost and automated mobile GUI agent data-mining framework based on Monte Carlo Tree Search (MCTS). For better data mining efficiency and quality, we present collaborative multi-agent frame- work, comprising InferAgent, OrchestraAgent, and JudgeAgent for guidance, ac- celeration, and evaluation. To further enhance the efficiency of mining and en- rich intent diversity, we design an intent recycling strategy to extract extra valu- able interaction trajectories. Additionally, a progressive model-in-the-logg train- ing strategy is introduced to improve the success rate of data mining. Extensive experiments have demonstrated that the GUI agent fine-tuned using our mined data achieves state-of- tfte--of-the of-the-art art performance several commonly used mobile GUI benchmarks. Our work will be released to facilitate the community research.
INTRODUCTION
GUI agents, which operate software applications by understanding user intent and executing se- quences of actions within graphical user interfaces, have emerged as promising direction in both academia and industry. Powered by recent advances in MLLM, these agents are envisioned to au- tomate wide range of tasks, from navigating mobile apps to controlling complex desktop envi- ronments. Currently, GUI agents still face a critical limitation: their performance heavily relies on high-quality intent-to-trajectory data for training.
Existing GUI agent datasets typically rely on either manual intent-trajectory annotation Rawles al. (2023); Li al. (2024); Zhang al. (2025b); Deng al. (2023) or naive automated mining method Sun et al. (2025); Putta et al. (2024) to acquire prior knowledge about specific applications. Most of these datasets adopt an totent-to-lla-trajeeee
structure, as shown in Fig. I(a), which means they only record single successful path for each intent without capturing the full exploration pro- cess. Such an incomplete structure will lead to low richness of intent and be insufficient to support the training of a robust GUI agent model. We summarize the major limitations of current GUI agent data construction approaches as follows: 1) high construction cost: existing manually annotated GUI agent data typically requires several hours create each entry; 2) poor data quality: existing datasets (including both manually labeled and automatically mined data) often contain redundant steps, ambiguous intent descriptions, and biased operation paths; and 3) low data richness: existing datasets suffer from monotonous intents and only record the actions for each step, lacking other descriptive information.
Rui Lv and Juncheng Mo contributed equally to this work. Corresponding authors: Ziqiang Dang, Chenguang Ma and Lei Zhao.

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root
Pat
(a) Flat
(b) Vanilla tree
(c) Mul-Int. tree (Ours)
Figure 1: Left: Different GUI data structure. (a) Flat human-annotaaeed GUI data, which stores only the single trajectory corresponding to each intent. (b) Vanilla tree-structured GUI agent data, where each tree is associated with a single intent. (c) Our multi-intent tree structure, where each tree contains multiple intents and their corresponding trajectories. Right: t-SNE Visualization of intent distributions. Compared to existing methods, our intent recycling strategy produces more diverse set of intents from the same initial intent.
To address the above challenges, first introduce the Monte Carlo Tree Search (MCTS) Koc- sis & Szepesvári (2006) algorithm into mobile GUT agent data mining task and propose a tree- structured representation
record the complete mining process. It is noted that directly applying vanilla MCTS to GUI agent data mining will result in low efficiency due to random expansion and rollout-based reward calculation. Therefore, we present collaborative multi-agent framework composed of InferAgent, OrchestraAgentt and JudgeAgent, Specifically, InferAgent improves ex- pansion efficiency by increasing the hit rate of correct actions corresponding to a given intent. Or- chestraAgent merges equivalent actions and ranks them by confidence, further boosting data min- ing efficiency. JuegeAgent replaces result-based rewards with process-based rewards, significantly reducing the computational cost of simulation phase. As result, collaborative multi-agent framework achieves exponential improvements in mining efficiency. Besides, motivated by the ob- servation that non-primary paths in the tree hold the potential serve valuable intent-trajectory data, we propose a novel intent recycling strategy to extract these extra interaction trajectories, which improves mining efficiency and enriches intent diversity. Such strategy evolves the vanilla tree representation (Fig. 1(b)) of prior works Putta et al. (2024) into the augmented structure de- picted in Fig. (c)). Specifically, upon completion of the mining process, the recycling process begins by re-evaluating all paths in the tree with a dedicated intent recycling filter to identify suitable paths. Subsequently, new intents are generated for these identified trajectories, concluding the recycling phase. To intuitively show the enhanced diversity, we provide a comparative visualization of intent distributions the right part of Fig. The visualization achieved by reducing the dimensionality of the intent text embeddings (obtained via Sentence Transformer Reimers & Gurevych (2019)) to two dimensions using I-SNE Maaten & Hinton (2008). Furthermore, we introduce a progressive training strategy to improve mining success rate and generalization in new GUI environments, through which agents capabilities and data complexity grow in tandem, resulting in significant performance improvements, especially in unseen scenarios.
Extensive experiments demonstrate that GUI agents trained on data from M2-Miner achieve SOTA performance. Moreover, conduct comprehensive ablation studies validate the effectiveness of each key component: our collaborative multi-agent framework, the intent recycling strategy, and the progressive training strategy. Compared with vanilla MCTS, our method achieves higher mining efficiency (e.g., 64x boost at task length 9) and success rate, while also generating more diverse intents. Furthermore, our experimental results show that the mined data enriched with descriptions provides greater benefits for GUI agent training than traditional flat datasets. Our work offers a valuable data foundation and mining paradigm support the advancement of the GUI community.
The main contributions of this paper can be summarized as follows: (1) We propose M2-Miner, the first automated mobile GUT agent data-mining framework based on MCTS, which incorporates a collaborative multi-agent framework consisting of InferAgent, OrchestraAgent, and Judge/gent, to jointly enhance mining efficiency and data quality. (2) To enhance intent richness and further improve mining efficiency, novel intent recycling strategy is introduced for extracting additional valuable trajectories. (3) To improve the mining success rate and enhance generalization in unseen environments, we present progressive model-in-ther ments demonstrate the superiority of our approach in mining high-quality and rich intent trajectory data. Specifically, GUT agents trained on mined data achieve SOTA performance.
training strategy. (4) Extensive experi-
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2 RELATED WORKS
GUI Agents. With the rapid development of MLLMs, building intelligent GUI agents with auto- mated operation capabilities for mobile devices has emerged as a leading research trend. Repre- sented by OmniParser Lu et al. (2024), AppAgent Zhang et al. (2025a), Mobile-Agent Wang et al. (2024) and Web He et al. (2024), prompt-based GUI agents leverage large multimodal mod- els to perform actions without explicit training. Mobile-Agent employs GPT-4V Yang et al. (2023) with visual perception modules to interpret screenshots and generate operations via prompts. Simi- larly, Web Voyager uses a ReAct-style Yao et al. (2023) prompting mechanism to annotate webpage elements via bounding boxes and plan click navigation steps end-to-end. While these prompt- only approaches reduce the need for training, they are prone to hallucinations because the utilized large models lack concrete prior of GUI environments. Subsequent works generally conduct fine- tuning on aopliccation-peciffie trajectories or integrate trainable agent modules Cheng et al. (2024); You et al. (2024); Liu et al. (2024); Qin et al. (2025): Wu al. (2025b); Ye et al. (2025); Lu et al. (2025b); Xu et al. (2025a). SeeClick Cheng et al. (2024) relies on vision-based grounding through pre-training on ScreenSpot screenshots and shows improved task execution. UI-TARS Qin et al. (2025) takes this further with end-to-end training on screenshot-action pairs, leveraging iterative online trace collection to achieve SOTA results across multiple benchmarks. AutoGLM Liu et al. (2024) propose foundation GUI agent which combines planning and grounding strategies, ap- plying online curriculum reinforcement learning to progressively improve performance in browser and mobile domains. These methods suffer from limited handling, and weak generalization. The key factor behind these issues is the lack of sufficient high- quality GUT data.
reasoning, poor exception
GUI Agent Data Production. The training of GUI agents heavily relies on irtent-trajectory data. Existing GUI agent datasets are entirely manually constructed, such as AITW Rawles et al. (2023), AITZ Zhang et al. (2024), AndroidControl Li et al. (2024), GUI-Odyssey Lu et al. (2025a), and AMEX Chai al. (2025). This manual annotation process is very costly, demanding specialized an- notation tools and experienced annotators. Recently, several studies have begun to utilize automated data mining techniques to efficiently collect trajetttraecctory data. GUI agent data mining refers to the process of collecting and labeling interaction trajectories using an automated framework. Early data mining studies were primarily designed to support L.L.- GUT agents operating in web environments, where the agents perceive the interface through the HTML code. For example, AgentQ Putta et al. (2024) pioneers a data mining approach leveraging Large Language Models for sveet-trajectory mining in web scenarios. However, its applicability is limited parsable web envi- ronments, as it lacks support for mobile scenarios that demand rich multimodal perception. Further- more, the method suffers from low efficiency due the inherent limitations of native MCTS. With the rapid advancements in VLMs, a substantial amount of recent GUI agent research has shifted towards purely vision-based approaches. However, research on data mining methods tailored to such vision-based GUI agents is still largely under-explored. A representative work in this line of research is OS-Genesis Sun et al. (2025), which employs fully automated, interaction-driven GUI data processing pipeline without reliance on predefined tasks or manual annotations. Initially, the agent performs rase-b step-wise interactions to explore the GUI environment, collecting raw state-action pairs. Then, through a process of inverse task synthesis, these low-level interactions are transformed into action instructions and task intents, thereby generating intent-trajectory pairs in an unsupervised manner. Mobile-Agent-v3 Ye et al. (2025) presents a self-evolving pipeline for generating intent-trajecton data. It combines high-quality intent generation, automated interac- tion, trajectory validation, and challenging-tus guidance within a reinforcement fine-tuning loop, steadily improving the agent's success rate. To the best of our knowledge, M2-Miner is the first automated mobile GUI agent data-mining framework based on MCTS.
METHOD
Given the urgent demand for high-quality GUI agent data and the laborious challenge of manually annotating interaction trajectories, we propose M2-Miner, the first automated mobile GUI agent data-mining framework based on Monte Carlo Tree Search (MCTS), as illustrated in Fig. 2. The preliminaries of MCTS are detailed in Appendix A. Based on vanilla MCTS, we specifically employ a collaborative multi-agent framework, which comprises InferAgent, OrchestraAgent,
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Figure 2: Overview. (a) The mining process of M2-Miner consists of four phases: Selection, Ex- pansion, Simulation, and Backpropagation. During these phases, InferAgent, OrchestraAgent, and JudgeAgent are employed for exploration guidance, acceleration, and state evaluation. (b) Intent cycling process. For a selected node, its corresponding trajectory is first passed through dedicated intent-recycling filter, then a novel intent is generated via MILM. If the JudgeAgent verifies that the generated intent aligns with the trajectory, the recycling is considered complete.
and JudgeAgent, enhance the expansion and simulation phases. Our approach reduces costs and improves both efficiency and mining success rate. Furthermore, we propose an intent recycling strategy fully extract valid trajectories from the intent trajectory trees. Finally, further boost the mining success rate, we introduce a model-in-the-loop training strategy to continuously evolve the capabilities of these agents. In the rest of this chapter, Sec. 3.1 first details the formulation of intent- trajectory tree; Sec. 3.2 thoroughly describes the collaborative multi-agent architecture; Sec. 3.3 presents our novel intent recycling strategy; and Sec. 3.4 introduces our model-in-the nooel-in training strategy.
3.1 INTENT-TRAJECCTRR TREE FORMULATION
Within the Monte Carlo Tree Search framework, our method constructs intent-trajectory trees to efficiently mine GUI intent trajectories within mobile applications.
Specifically, the mntent-trajectrry tree can be formalized as T (V.A.P,I) where V the set of nodes in the tree, each node E V corresponds to state s in the environment, AA represents the set of executable GUI actions (e.g., click, swipe, and type), with the available actions at state denoted as A(s), I denotes the set of user intents, typically specified as natural language task descriptions, and P represents the set of edges, capturing the parent-child relationships the search tree, (s, a, s') P if action transitions state to s'.
Each node v in the search tree is defined as a tuple (img.: meta,, Qu, Nu: staty), where img, denotes the screenshot of the environment at node v, meta, is the description of the action that led to node v, Qu represents an aggregated value estimate of node v (e.g., likelihood task success), No is visit count of node utilized for balancing exploration and exploitation during search, and stat, refers to the task completion status for node v, with possible values of success, failure, or intermediate.
Based on such a tree formulation, given an initial intent To and a starting GUI state Su, our objective is to efficiently construct tree that contains path corresponding valid interaction trajectory 7 (80, 4n, .I,...) satisfying lo-
3.2 COLLABORATIVE MULTI-AGEN FRAMEWORK
When vanilla MCTS is directly applied to GUI agent data mining, the mining efficiency and suc- cess rate are severely impacted by the expansion and simulation phases. Specifically, the expansion phase randomly selects child node to expand. However, due to the complexity of GUT agent tasks
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and the immense exploration space, this approach leads to large number of invalid and failed ex- plorations, which in turn significantly increases mining costs and dramatically lowers the mining success rate. Concurrently, scoring the current node during the simulation phase based on future exploration results leads to a slow mining speed and low efficiency. To address these issues, we propose a collaborative multi-agent framework, which comprises three key agents (InferAgent, Or- chestraAgent, and JudgeAgent). These three agents enhance the expansion and simulation phases of canonical MCTS. Fig. 2(a) illustrates the flowchart of the MCTS algorithm integrated with our multi-agent framework.
During the selection phase, the algorithm traverses the tree and selects a node to expand based on the UCT score. This phase does not involve any agents. Subsequently, in the expansion phase, In- ferAgent is responsible for inferring the actions most likely to achieve the target intent. Specifically, the InferAgent generates K candidate actions for the selected node based on the GUI screenshot of node u. To prevent redundant generation, previously produced actions will be incorporated into the prompt when generating new action. Besides, to ensure action space diversity, we leverage multiple distinct MLLMs to generate K actions. Fig. 2(a) shows an example where two tap actions, one wait, and one swipe generated. Note that the two tap actions essentially equivalent. Next, OrchestraAgent merges equivalent actions and ranks the remaining actions based on their likelihood of achieving the target intent, which avoids redundant expansions and efficiently guides the search toward promising trajectories. Specifically, the OrchestraAgent selects the most promising action at each iteration via a multi-choice question approach, ultimately yielding a sorted action queue through K - 1 queries. Subsequently, the sorted actions are utilized to generate new child nodes, which are then assigned decreasing initial UCT values based on the actions' order in the queue. As depicted in Fig. 2(a), the tap action is ultimately ranked first. This action is then executed, causing the virtual machine transition to a new page. Consequently, the corresponding node is expanded in the search tree. The mining process then proceeds to the simulation phase. In this stage, the JudgeAgent analyzes the GUI screenshot of the newly expanded node, determines the task com- pletion status of the node (i.e., stat) and calculates the rewards to update the Q-value of the node. When a node's 's status is terminal (i.e., success or failure), the reward is set to or O, respectively. For intermediate nodes, intents, outputting only "valid" or "invalid". Based on this paradigm, we utilize the logits from the MLLM head corresponding the token "valid" as the raw reward for intermediate nodes. To improve stability and comparability across different states, we introduce a normalization process using a softmax function to transform logits into a probability in [0, 1]. The normalized reward for intermediate nodes is thus computed as:
train the JudgeAgent to predict their rewards for achieving the target
exp (logit&valid)
Fintermediate =
exp (logitsvahd) + exp (logitsuvalia)
(1)
where lagit and logitsiuvalid denote the two output logits corresponding to the respective to- kens. This normalization ensures that rewards reflect the relative confidence of the model in bounded and interpretable manner and facilitates more stable learning. It essentially reflects the MLLM's confidence in its analysis of the potential. Such design for intermediate node rewards en- sures that the tree can integrate intermediate progress into its exploration, rather than solely relying on final outcomes, thereby facilitating faster and more stable policy refinement. After calculating the node's 's reward, its Q- valuu and visit count are updated as:
Q: Qi X N, R.
N
(2)
N111+
where i denotes the i-th visit to the node, consistent with the meaning of N, (i.e., N₁ = i), R the reward obtained during the i-th visit to the node, and Q: is the node's Q-value at the i-th visit. Finally, the process enters the backpropagation phase. Based on the reward computed in the simulation phase, the values of the nodes are updated upwards along the search path, starting from the newly expanded node. These four phases are repeated in a cycle until an interaction trajectory that matches the target intent is successfully mined. Overall, our MCTS framework leverages three specialized agents: the InferAgent for action generation (expansion phase), the OrchestraAgent for merging and prioritizing actions (expansion phase), and the JudgeAgent for reward estimation (simulation phase). For more details of our agents implementation and MCTS phases, please refer to Appendix A.2. Additionally, visualization of the mining process is available in the Appendix C.3.
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Basic Intents Generation
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Figure 3: Intent Generation. Our intent generation method consists of three stages: basic intents generation, complex intents generation, and intent recycling.
3.3 INTENT RECYCLING STRATEGY
For given intent, once mining is complete, corresponding trajectory tree is formed. We discover that within this tree, besides the single trajectory pertaining to the original intent, other trajectories possess the potential to generate new intents. For instance, in map application, while the original intent to be mined is querying routes between two locations, an incorrect tap on the "book ride" button might lead an additional ride-booking intent trajectory in the intent trajectory tree. These new intent- antent-trajectory pairs do not require re-mining, which can significantly boost mining efficiency, increase intent diversity, and alleviate the difficulty of intent construction. To fully leverage these valuable trajectories retained within the tree, we propose an intent recycling strategy, as illustrated in Fig. 2(b). Specifically, for each finished tree, we consider the paths from the root to every node as the potentially valuable trajectories. To filter out low-quality trajectories, we construct an in- tent recycling filter using the MLLM to assess and score the quality of these trajectories. For the trajectories that passed the filter, we use the MLLM to generate intents that align with the trajecto- ries, therehy obtaining potential irtent-trajectory pairs. Subsequently, we employ the JudgeAgent to assess the status of the trajectory's 's last node. If it is success, consider the trajectory valid which is then recycled and utilized in subsequent GUI agent training. This intent recycling strategy enables our Ma-Miner to fully leverage every search attempt during the MCTS process, acquiring unexpected interaction trajectory data. Details of the prompt templates are given in Appendix A.5, while examples illustrating the
trajectory tree can be found in Appendix C.2.
3.4 MODEL-IN-THE-LOOO TRAINING STRATEGY
During the mining process, we observe that insufficient capabilities of the InferAgent and JudgeAgent led to numerous invalid explorations and inaccurate termination judgments. which sulted in a low mining success rate. Consequently, we design a progressive model-in-the-logg train- ing strategy that iteratively improves the agents' performance. First, in the warm-up stage, we first train the InferAgent and JudgeAgent using public datasets to equip the multi-agent framework with basic capability for trajectory mining. Subsequently, leveraging the models obtained in the warm-up stage, we conduct continuous training of the InferAgent and JudgeAgent through three stages. At each stage, we first generate intents, then use the mining framework mine trajectory data, and sub- sequently retrain the model with all intent trajectory data mined prior to this stage. Fig. 3 illustrates the intent generation process at each stage. We will detail these three training stages below.
Training on basic intents. In this stage, we first collect the home screen screenshots of popular apps, and then, summarize the commonly used services of these applications and generate user intents based on MILM. Finally, we perform intent expansion through conditional rewriting (e.g.. modifying time, location). For example, "book a double room at a 3-star hotel in central London from December 10 to December 12, 2025." can be rewritten as "book double room at a 3-star hotel in central London from November 15 to November 16, 2025." Next, we utilize our proposed MCTS- based collaborative multi multi-agent framework to mine the trajectory data of these new intents. In this way. we obtain abundant intent trajectory data, which continuously used to train the two agents to improve their performance.
Stage 1:

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Figure 4: Infrastructure Framework. A layered framework for GUT agent data mining with model- in-the-loop training, comprising data, engine, algorithm, agent, execution, and environment layers.
Stage 2: Training on complex intents. We generate complex intents by adding conditions and functional combinations to the common user intents extracted in the previous stage. For example, the intent "book a double room at a 3-star hotel in central London from December 10 to December 12, 2025." can be extended to "book a double room at 3-star hotel central London from December 10 to December 12, 2025, and book me a flight to Paris at 4:00 PM December 12."; likewise, "play a popular song for me" can be extended to "play a popular song for me and share it with my friends". These teggseeuence intents, together with the previously failed intents, form set of complex intents that are mined using our multi-agent framework. The trajectory data obtained from this mining process are subsequently used for the training of both the Aneet and the Junge This process can be repeated in an alternating iteration between re-mining failed intents and continuously training the agents, thereby progressively improving the overall performance of the framework. Prompt templates for the two stages are detailed in Appendix A.6.
Stage Training on recycled intents. We adopt the intent recycling strategy from Sec. 3.3 to review all previous intent trajectory trees, thereby enriching the trajectories. Similar to the earlier stages, both agents are continuously trained on all mined trajectory datasets. This iterative alter- nation between trajectory mining and model training forms a positive feedback, model-in in sadel-in-the-loop the- strategy that progressively improves mining success rates and data quality. The resulting multi-agent framework effectively mines trajectories.
4 EXPERIMENTS
4.1 INFRASTRUCTURE FRAMEWORK
We build customized infrastructure framework that supports mobile agent data mining and model- in-the-loop training. As illustrated in Fig. 4, the framework adopts a layered architecture, consisting of the data layer, engine layer, algorithm layer, agent layer, execution layer, and environment layer. Our layered framework unifies GUI agent execution, intent-trajectory data mining, and model train- ing into a whole, thereby forming a customized model-in-the-loog framework that can automatically perform end-to-end data mining and training. More details of the infrastructure and environment in- teraction are provided in Appendix B.7.
4.2 M²-MINER- AGENT DATASET STATISTICS
By employing our automated data mining framework together with minimal manual quality inspec- tion, we collect the Mf-Miner-Agent dataset (abbreviated MP), which consists of 20k images and 2,565 trajectories, with an average trajectory length of 7.8. Compared to open-source datasets such as Android Control Li al. (2024), AITZ Zhang et al. (2024), AMEX Chai et al. (2025), and GUI Odyssey Lu et al. (2025a), which rely on labor-intensive manual annotation and quality review processes, our dataset exhibits significantly lower collection cost. This is crucial when tar- geting new mobile applications. To fairly evaluate the cost of dataset construction, we estimate the costs for all datasets based on the hourly wage rate (rwage $7 /h), annotation efficiency (bannot 0.05h/ime (055/i and quality inspection efficiency (lingeet = 0.0014h (000ah/immge), provided by a professional annotation team. Table summarizes the key statistics M² AMiner-Agen and other datasets. In comparison AITZ, which is of similar scale, method reduces construction
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Table 1: Statistics of different datasets. Size: number of images; Su.RL: suitability for reinforcement learning algorithms; Auto: auto-annotated data; AvgStp: average step length; Trajs: number of trajectories; Cost: estimated data production cost (USD), Cost per image: estimated cost of producing one image (USD).
DC4
Dataset
Size
Su.RI.
Anto
AvgStp
Trajs
Cost
Cost per image
Android Control
15,283
0.36
Figure 5:
Evaluation of data quality. M our mined dataset,
AMEX
38k
12.8
2,946
13,680
0.36
GUI Odysse
119
15.4
7.735
42,816
0.36
ATTZ.
18k
7.5
2,504
6,476
0.36
M²-QR: our dataset after hu- man quality review.
M ---eerr-Ageet
20k
7,8
2,565
466
0.02
costs by $6,010. When comparing on per-image cost (calculated as total cost divided by the number of images), method is upproximately 18 times more cost-effective than all other datasets. The cost calculation formula and a detailed explanation are provided in Appendix B.6. To evaluate the approach. Specifically,
quality of our mined data, adopt sample-and-vens 100 sequence samples from each of the AC, AITZ, and our dataset, and conduct manual inspection verify their correctness. As shown in Fig. 5, the Data Quality Accuracy (DQA) of our mined and inspected data is higher than that of manually annotated datasets. Examples of incorrectly annotated samples from public data are provided in Appendix C.I.
randomly select
4.3 EXPERIMENT SETUP
Implementation Details. We Qwen2.5-VL-7B Bai al. (2025) as the base model for both the InferAgent and JudgeAgent. The OrchestraAgent, the intent recycling filter, and the MLLM for generating intents based recycled trajectories all implemented with Swen2.5-VL-72B. More training details and prompt templates are provided in Appendix B.I and Appendix A.2.
Evaluation Benchmarks. We evaluate our method four representative GUI interaction bench- marks: AC Li et al. (2024), ArTZ Zhang et al. (2024), GUI Odyssey Lu et al. (2025a) and CAGUT Zhang al. (2025b). Since the training of CAGUI has not been released, it can be used to evaluate the generalization ability of our mining framework in novel scenarios. For detailed information regarding the benchmarks and evaluation protocols, please refer the Appendix B.3.
Metrics. We assess the success rate and quality of mined data using two measures: Mining Suc- cess Ratio (MSR), which quantifies the proportion of successful mining attempts, and Data Quality Accuracy (DQA), which evaluates the correctness of the collected data, For the evaluation of GUI agents, we further utilize two mainstream metrics for GUI agent evaluation: the accuracy of action type prediction (TP) and the step success rate (SR). Appendix B.4 details the metrics.
Baselines.
We compare our method with a wide range of baselines, including various proprietary MLLMs, open-source GUI agent models, and representative models trained on automatically mined data, such as OS-Genesis-7B Sun et al. (2025) and GUT-Owl-7B Ye et al. (2025). Baseline re- sults and evaluation protocols follow the original paper to ensure faimess. More details about the baselines can be found in Appendix B.5.
4.4
MAIN RESULTS
To validate the effectiveness of our method, use public datasets and mined data to train Qwen2.5- VL-3B and Qwen2.5-V1L-7B, obtaining M2-Miner-3B and M²-Miner-7B, We evaluate against prior SOTA methods on the GUI agent task. Quantitative results Table show that our approach outperforms baselines across all benchmarks and achieves a new SOTA. For AC-Low, M2-Miner-7B achieves 97.5% on TP and 93.5% on SR, surpassing all previous methods by a large margin. While ranking second in TP (81.8%) on AC-High, we achieve the best SR (72.9%), indicating superior task completion. On the challenging AITZ benchmark, our model attains 81.3% on TP and 69.4% on SR, achieving new SOTA result. For CAGUI, Qwen2.5-VL-7B trained with our mined data boosts SR from 55.2% to 70.2%, highlighting strong generalization to unseen domains. Moreover, our model consistently outperforms OS-Genesis-7B Sun et al. (2025) and GUI-Owl-7B Ye et al. (2025), the primary baselines employing automatically mined data. While UI-TARS-77 7B Qin et al. (2025), which is trained large-scale private human-annotated dataset, exhibits strong performance,
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Usl
Mage
Soege
Figure 6: Ablation study of the col- laborative multi-agent framework for accelerating MCTS-based data mining. For better visualization, a logarithmic scale was used.
Figure 7: Ablation study on model-in-the-loop for JudgeAgent. The perfor- mance of positive and neg- ative samples is reported.
Figure 8: Ablation study on model-in-the-loop for data quality. The performances of SR, MSR and DQA at different stages are reported.
Table 2: Performance comparison on GUI agent benchmarks (AC-Low, AC-High, AITZ, GUI- Odyssey, and CAGUI). Bold and underline indicate the best and second results.
AC-Low
AC-High
AITZ
GUI-Odyssey
CAGUI
Models
TP
SR
TP
SR
TP
SR
TP
SR
TP
Gloved-souree Models
GPT-4o Hurst al. (2024)
74.3
19.4
66.3
20.8
70.0
35,3
3.67
3.67
Gemini 2.0 Docpmind (2024)
28.5
60.2
Claude Anthropic (2024)
74.3
19.4
63.7
12.5
60.9
Open-source Models W/ Private Humun-Annoitted Datasets
UI-TARS-7B" Qin (2025)
98.0
72.5
Open-sounce Models W/ Publiic Datasets
OdysseyAgent et (2025a)
65.1
39.2
58.8
32.7
59.2
31.6
78.2
67.6
25.4
SpiritSight-8B Huang (2025)
87.6
75.8
GUI-RI-7B Luo et al. (2025)
85.2
66.5
71.6
51.7
56.8
50,5
65.5
38.8
OS-Ailas
(2025a)
71.2
81.5
Aguvis-7B Xu et al. (2025h)
80.5
61.5
35.7
19.0
26.7
13.5
67.4
38.2
InfiGUI-RI Lio et (2025)
96.0
92.1
82.7
71.1
52.9
74.5
55.0
Qwea2,5-VL-3B Bai ot al. (2025)
92.2
80.4
76.5
60.2
75.1
52.7
81.2
57.8
71,9
53.1
Qwen2.5.VL.s VLL Bai et al. (2025)
94.1
85.0
75.1
62.9
78.4
54.6
83.7
60.3
74.2
55.2
Open-source source
Auto
Dunasets
OS OS-Genesis-7B Sun et al. (2025)
90.7
74.2
66.2
44.5
20.0
8.5
11.7
3.6
38.1
14.5
GUI-Owl-7B et al. (2025)
93.8
90.0
81.5
72.8
78.9
65.1
83.4
60.7
80.0
M--Miner-BB
97.2
71.2
Mr-Miner-7B
97.5
93.5
81.8
72.9
81.3
69.4
90.5
79.3
88.8
70.2
model not only achieves comparable results on TP but also consistently surpasses UI-TARS-7B in SR across almost all benchmarks. Appendix C.4 presents qualitative results of M²-Miner-7B.
4.5 ABLATION STUDIES
Ablation on Collaborative Multi-Agent Framework. We perform an ablation study on the multi-agent framework (Sec. 3.2) to assess the contribution each agent to mining efficiency. For vanilla MCTS with only the InferAgent, the InferAgent will generate several actions during the expansion phase, but these actions are often redundant (e.g.. clicking the same button at different coordinates) and are not ranked by priority. The simulation phase is the same vanilla MCTS, where node values are evaluated via costly rollouts. Rollouts are executed by continuously execut- ing the InferAgent until it outputs success, failure, reaches the preset maximum number of steps. When the OrchestraAgent is introduced in the expansion phase, it merges the redundant actions gen- erated by the InferAgent and prioritizes correct ones, thereby reducing exploration nodes. When the JudgeAgent is introduced in the simulation phase, rollouts are no longer needed. Instead, the node values are directly evaluated by the JudgeAgent, significantly reducing simulation time. shown in Fig. 6, compared with vanilla MCTS using only the InferAgent, the efficiency improvements brought by MC-Miner grows exponentially with task complexity, achieving a 64x speedup at task length 9, Furthermore, the results show that each agent is essential for boosting mining efficiency.
Ablation on todelpin-the-lop. To demonstrate the effectiveness our proposed progressive model-in-the-loop training strategy (Sec. 3.4), conduct ablation study on the CAGUI bench-
"UI-TARS-7B uses large amount of private fairness.
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Table 3: Ablation on model-in-the-loop. TBI:
Table 4: Ablation on data structure. ACT:
Training on basic intents; TCI: Training on com- plex intents; TRI: Training on recycled intents.
data containing only actions; DES: data con- taining descriptions; PREF: preference data.
Stage
TBI
TCI
TRI
TP
SR
Setting
ACT
DES
PREF
TP
SR
Warm-up
85.0
64.2
Stage
86.5
67.3
Act.
85.2
66.8
Stage
/
87.6
69.1
Act. Des.
88.2
69.9
Stage
88.2
69.9
Act. + Des. Pref.
88.8
70.2
Table 5: Ablation training datasets. The results show overall TP, SR, and per-action accuracy.
Model
Training Dataset
TP
SR
Click
Scroll
Type
Press
Stop
Owen2.5 VL-7B
55.6
61.1
Qwen2.5.VV V-
Public datasets
84.9
64.4
67.1
22.3
62.3
0.0
65.5
Qwen2.5 Qwen2.5-VI.-2 5-VL-BB
Auto-Mined Datasets
87.1
69.5
70.7
24.7
68.5
78.5
Qwen2.5-VL-7B
Public and Auto-Mined Datasets
88.8
70.2
71.2
26.6
69.5
0.0
72.8
mark, which has no training data and is therefore well-suited for simulating data mining in new scenarios. As shown in Table 3, benefiting from the newly mined trajectory data at each stage, the model's performance is naturally enhanced, which in turn facilitates the mining process. Fig. 7 in- dicates that JudgeAgent's performance in distinguishing positive and negative samples exhibits a steady upward trend. Furthermore, Fig. 8 shows that, although the data mining success rate the warm-up stage is relatively low, our approach yields a remarkable improvement in both MSR and DQA in all subsequent stages compared with the warm-up stage. Stage focuses on mining com- plex intents, which results in only modest improvement in MSR. Nevertheless, the data generated in this stage also significantly enhanced the TP and SR of downstream tasks. In addition, in the stage 3, when recycling diverse intents, both MSR and DQA improve significantly. These results indicate that our progressive model-in-tth-lowee strategy has significant advantages in improving TP, SR, MSR, and DQA. The data construction method for JudgeAgent is provided in Appendix A.4.
Ablation on Data Structure. Manually annotated datasets typically contain only simple action labels and lack semantic information. In contrast, the trajectory trees preserved during our mining process contain both actions and descriptions with rich semantic content. Moreover, by leveraging the positive samples from the trajectory trees and the negative samples from other branches, pref- erence data can be constructed. The results in Table 4 demonstrate that the description data and preference data obtained during mining process can effectively improve the TP and SR.
Ablation on Training Datasets. We conduct an ablation study on the training data, and the re- sults are presented in Table 5. The experiments performed the CAGUI benchmark using the Qwen2.5-VL-77 VL-7B model. Without training data, the Qwen2.5- VL-7B model shows relatively low accuracy across all action types (CLICK, SCROLL, TYPE and STOP), indicating limited execution performance on new GUI applications. Training on public datasets produces moderate improve- ments, with SR increasing by 9.2% and performance gains observed across all action types. Us- ing only auto-mined data results in larger boosts (SR +14.3% CLICK +15.1%, %, SCROLL +13,4%, TYPE +26.6% and STOP +17.4%). highlighting the practical significance of our proposed data production framework. When combining public and auto-mined datasets, the model achieves the best results, outperforming the public-only setting by +3.9% TP, +5.8% SR, and showing consistent gains across all actions. In summary, the ablation results show that our auto-mined data serves as valuable complement to existing public datasets and highlight our core contribution: an automated data mining framework, which has practical significance for the GUI Agent domain.
CONCLUSION
In this work, we propose M2-Miner, a fully automated framework for mobile GUT agent data min- ing. By introducing MCTS and designing a collaborative multi-agent framework, our method signif- icantly improves data mining efficiency while enhancing data quality. The intent recycling strategy further enhances both mining efficiency and intent richness, while the progressive model-in-the-loog training paradigm boosts success rates in both familiar and novel environments. Extensive exper- iments show that GUI agents trained on our mined data achieve SOTA performance. M provides a promising approach for mining high-quality trajectory data for mobile GUI agent, estab- lishing solid foundation for the development of GUT research community.
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ACKNOWLEDGMENTS
This work was supported by Ant Group and in part by Zhejiang Province Program (2024C03263).
REFERENCES
Anthropic.
Introducing computer use, a new claude 3.5 sonnet, and claude 3.5 haiku. https: //www. anthropic. com www.///ww//ae
aow-sss--nndcotuteereeee usee 2024.
Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, et al. Qwen2. 5-vl technical report. arXiv preprint arXiv:2502. 2025.
Yuxiang Chai, Siyuan Huang, Yazhe Niu, Han Xiao, Liang Liu, Guozhi Wang, Dingyu Zhang, Shuai Ren, and Hongsheng Li. Amex: Android multi-annotation expo dataset for mobile gui agents. In 2025.
Findings of the Association for Computational Linguistics: ACL 2025, pp.
Kanzhi Cheng, Qiushi Sun, Yougang Chu, Fangzhi Xu, Li YanTao, Jianbing Zhang, and Zhiyong Wu. SeeClick: Harnessing GUI grounding for advanced visual GUI agents. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers),
pp. 9313-9332, Bangkok, Thailand, August 2024. Association for Computational Linguistics.
URL httph:tts//eet /tEtps://acaatthlooyy. org/ 2024 aacl long. 505.
Google tic
Deepmind. Introducing gemini 2.0: our new ai model for the agen- http://eehooooogooooeeeeeeeeeeee google-geme pi wwwpaaapeeeeeeeeeeee---00000-000 2024.
era.
Xiang Deng. Yu Gu, Boyuan Zheng, Shijie Chen, Sam Stevens, Boshi Wang. Huan Sun, and Yu Su. Mind2web: Towards a generalist agent for the web. Advances in Neural Information Processing Systems, 36:28091-2811 14, 2023.
Hongliang He, Wenlin Yao, Kaixin Ma, Wenhao Yu, Yong Dai, Hongming Zhang, Zhenzhong Lan, and Dong Yu. Webvoyager: Building an end-to-end web agent with large multimodal models. arXiv preprint urXiv:2401.13911 2024.
Zhiyuan Huang, Ziming Cheng, Junting Pan, Zhaohui Hou, and Mingjie Zhan. Spiritsight agent: Advanced gui agent with one look. In Proceedings of the Computer Vision and Pattern Recogni- tion Conference, pp. 29490-29500, 2025.
Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Os- trow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card, arXiv preprint arXiv:2410.2 2024.
Levente Kocsis and Csaba Szepesvári. Bandit based monte-carlo planning. In European conference machine learning. pp. 282-292 Springer, 2006.
Wei Li, William Bishop, Alice Li. Christopher Rawles, Folawiyo Campbell-Ajala, Divya Tyam- agundlu, and Oriana Riva. On the effects of data scale on ui control agents. Advances in Neural Information Processing Systems, 37:92130-9213 2024.
Xiao Liu, Bo Qin, Dongzhu Liang, Guang Dong, Hanyu Lai, Hanchen Zhang, Hanlin Zhao, lat Long long, Jiadai Sun, Jiaqi Wang, et al. Autoglm: Autonomous foundation agents for guis. arXiv preprint arXiv:2411.0083
2024.
Yuhang Liu, Pengxiang Li, Congkai Xie, Xavier Hu, Xiaotian Han, Shengyu Zhang, Hongxia Yang, and Fei Wu. Infigui-rl: Advancing multimodal gui agents from reactive actors to deliberative reasoners. arXiv preprint arXiv:2504. 14239, 2025.
Quanfeng Lu, Wenqi Shao, Zitao Liu, Lingxiao Du Fanqing Meng, Boxuan Li, Botong Chen, Siyuan Huang, Kaipeng Zhang, and Ping Luo. Guiodyssey: A comprehensive dataset for cross- app gui navigation on mobile devices. In Proceedings of the IEEE/CVF International Conference Computer Vision pp. 2241442241, 2025a.
11

---

Published as a conference paper at ICLR 2026
Yadong Lu, Jianwei Yang, Yelong Shen, and Ahmed Awadallah. Omniparser for pure vision based gui agent. arXiv preprint arXiv:2408.00220 00203, 2024.
Zhengxi Lu, Yuxiang Chai, Yaxuan Guo, Xi Yin, Liang Liu, Hao Wang, Han Xiao, Shuai Ren, Guanjing Xiong, and Hongsheng Li. Ui-rl: Enhancing efficient action prediction of gui agents by reinforcement learning. arXiv preprint arXiv:2503.21620 2025b.
Run Luo, Lu Wang, Wanwei He, and Xiaobo Xia. Gui-rl: A generalist rl-style vision-language action model for gui agents. arXiv preprint arXiv:2504. 10458, 2025.
Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of machine learning research, (5Nv))2557--2655, 2008
Pranav Putta, Edmund Mills, Naman Garg, Sumeet Motwani, Chelsea Finn, Divyansh Garg, and Rafael Rafailov. Agent q: Advanced reasoning and learning for autonomous ai agents. arXiv preprint ar wix::v2:0..... 079 2024.
Yujia Qin, Yining Ye, Junjie Fang. Haoming Wang, Shihao Liang, Shizuo Tian, Junda Zhang, Jiahao Li, Yunxin Li, Shijue Huang, et al. Ui-tars: Pioneering automated gui interaction with native agents. arXiv preprint 2rXiv:2501.12326, 2025.
Christopher Rawles, Alice Li, Daniel Rodriguez, Oriana Riva, and Timothy Lillicrap. An- droidinthewild: large-scale dataset for android device control. Advances in Neural Information Processing Systems, 36:59708-599 -97288 2023.
Nils Reimers and Iryna Gurevych. Sentence-bert: Sentence embeddings using siamese bert- networks. In Proceedings of the 2019 Conference Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2019. URL htttps://arat
org/abs/1900 10084.
Qiushi Sun, Kanzhi Cheng, Zichen Ding, Chuanyang Jin, Yian Wang, Fangzhi Xu, Zhenyu Wu, Chengyou Jia, Liheng Chen, Zhoumianze Liu, et al. OS-genesis: Automating GUT agent trajec- tory construction via reverse task synthesis. In Proceedings of the 63nd Annual Meeting of the Association for Computational Linguistics (Volume I: Long Papers). Association for Computa- tional Linguistics, 2025.
Vast.ai. Create cloud instance, 2025, URL WtRptttss////oodd. vast. wtt.://dd..aat....rreeeteeeeeeee Accessed: 2025-11-17.
Junyang Wang, Haiyang Xu, Jiabo Ye, Ming Yan, Weizhou Shen, Ji Zhang, Fei Huang, and Jitao Sang. Mobile-agent: Autonomous multi-modal mobile device agent with visual perception. arXiv preprint urXiv:2401.16155 2024.
Zhiyong Wu, Zhenyu Wu, Fangzhi Xu, Yian Wang, Qiushi Sun, Chengyou Jia, Kanzhi Cheng, Zichen Ding, Liheng Chen, Paul Pu Liang, and Yu Qiao. OS-ATLAS: Foundation action model for generalist GUT agents. In The Thirteenth International Conference on Learning Representa- tions, 2025a. URL https://////.....t http:::s//opeeevieeee ew net/ forum? dd OPDaFii Bt.
Zhiyong Wu, Zhenyu Wu, Fangzhi Xu, Yian Wang, Qiushi Sun, Chengyou Jia, Kanzhi Cheng, Zichen Ding, Liheng Chen, Paul Pu Liang, and Yu Qiao. OS-ATLAS: Foundation action model for generalist GUI agents. In The Thirteenth International Conference on Learning Representa- tions, 2025b. URL pettps://operreeeee
net/ wtr////oummmmmmmmoo SPDaFPNNi....
Ran Xu, Kaixin Ma, Wenhao Yu, Hongming Zhang, Joyce C Ho, Carl Yang, and Dong Yu. Retrieval- augmented gui agents with generative guidelines. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pp. 17877-17886, 2025a.
Yiheng Xu, Zekun Wang, Junli Wang, Dunjie Lu, Tianbao Xie, Amrita Saha, Doyen Sahoo, Tao Yu, and Caiming Xiong. Aguvis: Unified pure vision agents for autonomous GUI interac- tion. In Forty-second International Conference on Machine Learning, 2025b. URL https: // /poperevview. net. net/forum? d=P1 wwwpdhhwwhh...... fx4.
12

---

Published as a conference paper at ICLR 2026
Zhengyuan Yang, Linjie Li, Kevin Lin, Jianfeng Wang, Mhung-Ching Lin, Zicheng Liu, and Li- juan Wang. The dawn of Imms: Preliminary explorations with gpt-4v (ision). arXiv preprint arXiv:2309.17421, 2023.
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. React: Synergizing reasoning and acting in language models. In International Conference on Learning Representations (ICLR), 2023.
Jiabo Ye, Xi Zhang, Haiyang Xu, Haowei Liu, Junyang Wang, Zhaoqing Zhu, Ziwei Zheng, Feiyu Gao, Junjie Cao, Zhengxi Lu, et al. Mobile-agent-v3: Fundamental agents for gui automation. arXiv preprint arXiv:25066 15144, 2025.
Keen You, Haotian Zhang, Eldon Schoop, Floris Weers, Amanda Sweangin, Jeffrey Nichols, Yinfei Yang, and Zhe Gan. Ferret-ui: Grounded mobile ui understanding with multimodal llms. In European Conference on Computer Vision, pp. 240-255. Springer, 2024.
Chi Zhang, Zhao Yang, Jiaxuan Liu, Yanda Li, Yucheng Han, Xin Chen, Zebiao Huang, Bin Fu, and Gang Yu. Appagent: Multimodal agents as smartphone users. In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, pp. 1-20, 2025a.
Jiwen Zhang, Jihao Wu, Teng Yihua, Minghui Liao, Nuo Xu, Xiao Xiao, Zhongyu Wei, and Duyu Tang. Android in the zoo: Chain-of-action -hooughtt for gui agents. In Findings of the Association for Computational Linguistics: EMNLP 2024, pp. 2016-120311 2024.
Zhong Zhang, Yaxi Lu, Yikun Fu, Yupeng Huo, Shenzhi Yang, Yesai Wu, Han Si, Xin Cong, Haotian Chen, Yankai Lin, et al. Agentcpm-gut gui: Building mobile-use agents with reinforcement fine- tuning, arXiv preprint arXiv:2506.01391, 2025b.
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STATEMENT OF USING LLM
We use LLM to optimize our English expressions for improved clarity.
APPENDIX OUTLINE
Here we provide implementation details (Appendix A), experimental details (Appendix B) and vi- sualization results (Appendix C) omitted from the main paper for brevity.
Specifically, Appendix A. introduces the preliminaries for the canonical Monte Carlo Tree Search algorithm. Appendix A.2, A.3, and A.4 describe the details of InferAgent, OrchestraAgent, and JudgeAgent, respectively. Appendix A.5 details our intent recycling strategy. Appendix A.6 intro- duces model-in-the-looe details.
Appendix B.1 and B.2 present the training details and the action space, respectively. Ap- pendix B.3, B.4 and B.5 introduce benchmark datasets, evaluation metrics and baselines. Ap- pendix B.6 describes calculation of dataset construction cost. Appendix B.7 details infrastructure framework and environment interaction process.
Appendix C.I shows erroneous samples in public datasets. Appendix C.2 presents examples of traent-tra trees. Appendix C.3 shows examples of data mining process. Appendix C.4 presents GUI agent inference examples on benchmark datasets. Besides, we have provided a sup- plementary video that demonstrates the data mining process.
A IMPLEMENTATION DETAILS
A.1 PRELIMINARY: MONTE CARLO TREE SEARCH
Monte Carlo Tree Search is a heuristic tree search algorithm widely used in sequential decision- making tasks. Unlike traditional tree search methods that rely on handcrafted evaluation functions, MCTS dynamically constructs a search tree through iterative simulations, balancing exploration and exploitation using statistical estimates. Each node in the tree represents a state in the environment, and each edge corresponds to an action that transitions the environment to a new state.
At the core of MCTS lies four-step procedure repeated at each iteration: Selection, Expansion, Simulation, and Backpropagation. Specifically, during the selection phase, the algorithm traverses the search tree and selects node using a principled selection policy. commonly used policy is the Upper Confidence Bounds applied to Trees (UCT) Koesis & Szepesvári (2006), which balances exploitation and exploration by choosing actions that maximize:
In N [(ss.)) + +o/ ass,a)
a' arg max aEA(s)
(3)
where Q(s, a) is the cumulative reward of the child node that is obtained by taking action in state 8, (s) represents the visit count for state s, N(s a) denotes the number of times action a has been taken in state s, and c 0 is a constant controlling the exploration-exploittion trade-off. After a node is selected, an action is randomly chosen in the expansion phase to obtain a child node. Subsequently, in the simulation phase, the algorithm conducts complete process (roll-out) starting from this newly expanded node until terminal state is reached. This process typically follows simple and fast policy, such as making decisions completely at random. The purpose of the roll-out is to quickly obtain a result or evaluation, such as reward. The backpropagation phase begins after the rollout concludes and reward is obtained. During this phase, the algorithm backtracks from the newly expanded node up to the root, utilizing the simulation results to update the statistical information of all nodes along the path, such as their visit counts and cumulative rewards (e.g., number of successes).
Despite its effectiveness, vanilla MCTS suffers from computational inefficiency in large or complex environments (e.g., GUI agent task). Specifically, because vanilla MCTS randomly selects actions for expansion and relies on repeated rollouts to obtain rewards, its applicability is restricted high- dimensional real-time applications.
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A.2 INFERAGENT
In the expansion phase of Monte Carlo Tree Search (MCTS), the InferAgent plays pivotal role by intelligently inferring and generating the most plausible action set achieve the target intent for each tree node. In the vanilla MCTS, random action expansion often results in inefficiency and redundant coverage of the action space, To address this, we design multi-model collabora- tive mechanism for InferAgent, improving the effectiveness and diversity of expansion outcomes. Specifically, the InferAgent is composed of two models: InferAgent-Qwee Qwen2.5-VI-7B Bai al. (2025) using the prompt shown in Fig. 9, and InferAgent-Owenn-221 derived by instructing Qwen2.5-VI. VVLL22B Bai et al. (2025) with the prompt in Fig. 9. During the action, ensuring
which is trained on
expansion of each node, first employ InferAgent-Queet -weent to generate reasonable exploration space. To mitigate the bias issue of the trained model and enhance the di- versity of the action space, we then utilize weffereget-ween---eee actions. Notably, the prompt for InferAgent- caeei--eennn- 72B explicitly includes the list of previously generated actions to prevent the model from producing repeated actions. By introducing historical actions as constraints, this approach effectively guides the MLLM to explore novel, non-repetitive candidate actions. Furthermore, we leverage cetrAgeet-Qwert for reasoning in GUI agent tasks, using the same prompt template as in Fig. 9.
to subsequently generate K 1
A.3 ORCHESTRAAGENT
OrchestraAgent is equipped with an action merging function (derived from the prompt in Fig. 10) and an action ranking function (derived from the prompt in Fig. 11), both implemented based on Qwen2.5-V VV--2BB.. With these two functions, OrchestraAgent can efficiently merge equivalent ac- tions and sort candidate actions by intent relevance during the MCTS expansion phase, thereby improving exploration efficiency.
A.4 JUDGEAGENT
JudgeAgent consists of two main modules: the Outcome Reward Model (derived from Qwen2.5- VL-72B with the prompt of Fig. 12) and the Process Reward Model (trained on Qwen2.5-VL-77 Bai et al. (2025) using the prompt of Fig. 13), which are responsible for determining the task completion status of nodes and calculating the rewards, respectively. During the simulation phase, the Outcome Reward Model is first applied to determine whether the node corresponds to terminal state. If the status is success or failure (i.e. impossible to succeed), rewards of or will be assigned respec- tively. If the status intermediate (i.e., not yet succeeded), the Process Reward Model is further utilized to assess the potential validity (i.e., valid or invalid) of this node. The logit corresponding the token "valid" then used the reward signal, described in Sec. 4.2 of the main paper. The JudgeAgent constructs positive samples based on the mined data, and identifies incorrect steps from the intent trajectory tree to serve as negative samples. The Action Type distribution of negative samples is synchronized to closely match that of the positive ones. The dataset is then divided into training and test sets in a 9:1 ratio for model training and evaluation. The distribution of the data is illustrated in Fig. 14.
A.5 INTENT RECYCLING STRATEGY
As detailed in Sec. 4.3, for each completed trajectory tree, we enumerate all root-to-node paths as candidate trajectories. Subsequently, we employ the recyeling filter (derived from Qwen2.5-Vi 72B with the prompt of Fig. 15) to evaluate the quality and relevance of each candidate trajectory, considering the UI context and action sequences, and filter out low-quality trajectories. For the trajectories that pass this filter, we further instruct Qwen2.5-VL-72B with the prompt of Fig. 16 to generate corresponding intent descriptions. Finally, JudgeAgent evaluates whether the reviewed trajectory fulfills its associated intent, retaining only those samples deemed successful. By apply- ing such strategy, M2-Miner fully leverages every search process, significantly improving mining efficiency and enriching the diversity of intents.
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Prompt for InferAgent-Qwen
System Message
You ore. helpful assistont
# Tools
You may call one or more functions to ass ist with the user query.
You provided with function signatures within <tea to<//o//tools> XML tags
<tools
('type": "function", "function": ("hame": "mobile_use usee", "description": "Use a touchscreen to interact with mobile device, and take screenshots, Ann This interface mobile device with teuchscreen, You perform actions like clicking, typing, swiping, etc. "n" Some appl icationsnon may take time to start or process actions, so you may need to wait and take successive screenshats to see the results of your actions, Vn" The screen's resolution is width height vi" Make sure click any buttons, links, icons, etc with the cursor tip in the center of element. Don't click
perform. The available actions are:'v"" "eey Porform a key event on the mobile device, Vn This supports adb' ss' "keyevent syntax, An EExoppee: \'volume_upp" V"valme__cow cownn", \"power\", Vccamera", V'clar\'' Ass" "llick' Click
edges asked." "parometers": ""properties". ("action": ("description": "The
the screen with coordinate (x, y), Ann
'ong_prees" Press the point on the screen with coordinate (x. y) for specifled seconds. ln "swipe" Swipe from the starting point with coordinate (x. y) to the end point with coordinates2 (x2, y2). yn" 'type' Imput the specified text into the activated input box. "system_bufti butten" Press system button lnr open': Open an opp the device. "wait: Wait specified seconds for change happen \nn" "terminate:: : Terminate the current task and neport its completion status"" "eram": ("key", "click", "long_ppeess" "swipe", 'type", "system_button button", "open", "wait", "terminate"]. "type": "string"). "coordinate": ("description"" "(x. y): The (pixels from the left edge) and (pixels from the top edge) coordinates move mouse Required only by actionndlick". aetiorFlong_.rress press" and "cctionsuipe" !. "type": "arroy"), "coordinate?": ("descniption" "(x. The x (pixels from the left edge) and (pixels from the top edge) coordinates to move the Requined only by actionnswipp"" "type": "array"). ). "text": ("description"" "Required only "actionskyy" "acttiostype" and actionsopen". :,, "type": "stning"): "time": "time":""desorri"" """desorippiin" "The deconds to wait. Required only by aationslong__prest and actioo=wait" :, "number"), "button': "buuton":""'eseneeee ("dessniption":" "Bock "Back means neturning to the previous interface, Home meons returning the desktop. Meru means opening application background menu, and Enter
pressing the Required only by oytiorssystemm_eee ('Back' "Home", "Merd', "Enter"), "type": "string)) "status": ("deesription": "The status of the tosk. Required only by "actiona terminate". "type": "string". "erum": ["success", "failure"D), DD) "required": l'action"], "type": "object"))
"enum":
</tools>
For each function call, return json object with function name and arguments within stool_ coll:ccoooo//ooodllllel XML tags:
<tool_coll
("nome": -function-name, "orguments": kargs: corgs-joon-ebjjee joon-objjcct))
</teolccall.
anawering, summorize your action bacriin_eecrppptooov//rettettttp tags,
+tool_ call>>/tool_coll XML togs.
User
The user query: [user_neott [nqqusstt]
Current step query: low_lew__r instruction (Induded only low__lew_nssteeeeeeeeee Jew__enstruution defined) Task progress have have dane the fallowing operation on the current device) [Nistory_ ceerrent_screendii
Assistant
dection_andd_escrrppiinn
Figure 9: Prompt for InferAgent-Qwen.
A.6 MODEL-IN-THE-LLOOP
As illustrated Fig. 17 and Fig. 18, we constructed two types of prompts: one for basic intent generation and another for complex intent generation. Leveraging the Qwen2.5-VL-72B model, we produced intent datasets two successive stages corresponding to these prompt types.
B EXPERIMENTAL DETAILS
B. TRAINING DETAILS
The InferAgent and JudgeAgent are trained iteratively with our model-in-the-loop training strategy. Specifically, in the warm-up stage, training data drawn from public datasets, including Android-
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Prompt for Merge Actions
System Message
Please evaluate the semontic similority the following operation options, and output "VES" "NO" based the evalustion result.
Operation Option Description: %s
Operation Option Description: Ns
Please compare descriptions and structured expressiors these two openation options. If they semontically similar, that they describe same operction merged into operation, then output "YES". If they not sementically similar, that they describe different openations, then output "NO".
User
Descriptionl: ]
Description2: destron_dsscrpptionn2
]eirent__crreenhhot]
Assistant
Answer: Answer:[VES/NO)
Figure 10: Prompt for Action Merging Function of OrchestraAgent.
Table 6: Action Space for Mobile GUT Agent Tasks. Coord.:
coordinate
Action Type
Description
Required Parameters
click
Tap specific Coord. (x, y) Long press point for specified duration
Coord.: [2.
longprress
Coord.: Iz, yl. time:
onds
swipe
Swipe from one point to another
Coord.: [21, yı). Coord.
[ V2]
Type text into the current input field Simulate key events (e.g., volume_up, power) Wait for specified number of seconds system.button Press system button (Back, Home, etc.)
type
texz: string
key
text: key name
wait
time: seconds
butzon: Back, Home, Menu, Enter
termi narminate nate
Terminate the current task and report completion status
status: success or failure
Control Li al. (2024), AITZ Zhang et al. (2024), GUI-Odyssey Lu et al. (2025a), and AMEX Chai et al. (2025). All training is conducted 8 NVIDIA A A100-80G GPUs; at each stage retrain for 2 epochs on the full mined dataset, using a global batch size and a learning rate of 3 x 10-5 We apply LoRA fine-tuning all linear modules with rank of 32 and of 64.
B.2 ACTION SPACE
Table lists all the action spaces used by M2-Miner. Specifically, these action types comprehen- sively encompass common interactions mobile devices, such as tapping or long pressing specific coordinates, swiping between points, typing text, simulating key events, pressing system buttons, waiting, and terminating tasks. Each action requires clearly defined parameters, ensuring precise and reproducible interaction modeling.
B.3 BENCHMARKS
Table presents detailed statistics on the composition of the datasets used. The division between the training and test sets strictly follows the official split to ensure consistency with the original data. We provide more information about the utilized datasets below. AndroidControl (AC) Li et al. (2024) is a comprehensive and widely adopted benchmark for assessing real-world mobile GUI navigation agents. The dataset is constructed from human-collected interaction tasks within the Android environment, encompassing 15.283 trajectories from 833 different apps. Each episode contains, on average, 5.5 fine- grained annotated action steps, enabling detailed evaluation at both the action type and execution levels for single-app scenarios. For evaluation, AndroidControl sup-
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Prompt for Rank Actions
System Message
You are expert in mobile phone outomation who is functioning critic You will be shown few possible tasks that can be done towards obejetive you review which You will be given the main objective, the screenshat of the mobile phone. on which these tasks are supposed to be executed and the past history of execution.
mobile phone
suited
objective.
#*# Execution Flow Guidelines
You will have a look of the objective that needss be achieved.
Then, you will look the tosks that been done now, their successes/ failures. If tasks have been completed till now, that the system has started from scratch.
Post this, have careful look the current screenshot provided to you. Use that see the actions the proposed tosks have the cornect
they are supposed
Once you have carefully observed the screenshot, previous tasks and the objective, think step by step and choose the best possible next step of the given possible tasks that executed the screenshot order towards the overall objective, Think of like three different paths that eventoully should lead to the overall objective, You should oct like. critic and carefully follow the below Instructions.
these given tasks like branches from the some root node he webpage)- -
Your input output will strictly well-formatted JSON trtiuuues ### Input Format
below.
objective: Mandatory representing the main objective be achieved via mobile phone automation
beaskeb Optional summory (string format) list of completed tasks contributing objective fulfillment. tasks_for _ev__ _eval Mandoory List of possible le next tasks of which anyone can be donz on the screanshot to achieve/ meve towards the objective. Think step step. Choose one of based the overall objective, tasks completed new mobile phone. You will be provided with screenshot of the mobile phone to think better.
######## Format of Task Object in tasks_f joske_for_evddl Mondatory Integer regresenting the id descriptions Mandatory string repnesenting the description of the task
JSON object indicating the that
order complete
appropriate fields.
#1771 Output Format
thought A Mandstory string specifying thoughts how didd come with top tasik reitenste the objective hene so that can system eventual
a critic, reason deeply about possible in option step by step to come up with one top tosk. Illustrate your thoughts here.
always remember
tep_taskkidd the task you think the best suited be performed the screenshot lead towards/ complete the objective
User
Objective: [objective_info)
beeooee [tosks [tasks_beforee_ _est
haske_for_eeai foock__oor_evo_istt
Ccurrent_screessher
Assistant
Thought: [thought]
Top_task_id: [fask_id]
Figure 11: Prompt for Action Ranking Function of OrchestraAgent.
Table 7: Overview of GUI agent datasets. AvgStp denotes the average step length. Total Traj. represents the total number of trajectories. Train Split and Test Split specify how many trajectories are allocated for model training and evaluation respectively.
Dataset
Platform
AvgStp
Total Traj.
Train Split
Test Split
AndroidControl et al. (2024)
Mobile
5.5
15,283
13,602
1,681
AITZ Zhang el al. (2024)
Mobile
6.0
2,504
1.998
506
GUI Odyssey Lu et al. (2025a)
Mobile
15.3
7,735
5,801
1,934
AMEX Chai et al. (2025)
Mobile
12.8
2,946
2,946
0
CAGUI Zhang et al. (2025b)
Mobile
7.5
600
0
600
ports two settings: AndroidConttol-Hige (AC-High), where each prompt presents only high-level instruction, and AndroidControl--oon (AC-Low), which provides both high-level and corresponding low-level instructions. The benchmark adopts success rate (SR) and action type accuracy (Type)
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Prompt for Outcome Reward Model
System Message
You are expert mobile automation judge. Your tosk to evaluste whether on AI agent has achieved the user's objective, using the user's objective description, current screenshot, agent's completed
WM Evolastion States
1. "Success" Thoroughly verify that all key elements explicitly stated in the user's is objective (such as time, location, origin, destination, subject, etc.)are present. Only judge as Success if every requirement is fully sutisfied.- The screenshot must clearly cisplay the exact page requested, including specified details. The must accurately novigated the intended target state, Exomple: If the objective "o to payment page for flight from Beijing to Shanghai on June 12," the screenshat must show the payment page with June 12 the date, Beijing the origin, and Shanghai the destination. Output:" ("thought": "your reasoning", "errmnna"""" true, "status": "success")"
2. "NNt Yet Succeeded"* The agent has not yet reached the final target page or state, but it is still possible to continue and achieve the objective.- If any condition specifled the objective yet satisfied, select this Output: ("thought": "your reasoning", "is__ "i_ttrmnnaa folde, 'status": "not__ye___ ____ee__ccceeeed"
3. **Inposiible Succeed"* The agent has reached on error dead end where is impossible to achieve the objective. This includes continuing would require user credentials, payments, private information AI provide. Output: ("thought"" "oour reasoning", "is_tew terminal: false, "status": "ipossibll__o_oooooooo Te_sscceed""
Evollcttoo Process
1. Identify the key required elements in the objective (such as time, location, origin, destination, subject,etc)
2. Compare the screenshot completed
been fulfilled. cases, exploin your recsoning in the "thought" field before giving your
check if required
3. If all conditions are met, outipt Success, If requirements are not yet met but achieving the objective is still possible, output Not Yet Succeeded.I If the objective impossible to achieve, output Impossible Succeed.
## Output Format
Return a JSON object with exactly three keys ""thought" A string explaining the reasoning behind your judgment "s__termiaa""
Boolecn indicating if the precess has reached a final statt (success impossible to continue)) - "*status" String, one of: "suucess""
'not__ytt succeeded" oor "impossible le_ succeedd"
User
The user query [user_ _requsst]]]
[oll ail_histrry__ctions]
curreet__erreershat]
Assistont
Thought: [thought]
Is_ fs_terminal: [true/false]
Status: [success/not http://ootteeettccceeeeeeeeeeeee wtwp://wwccceeee///ooooss.ooooo.
Figure 12: Prompt for Outcome Reward Model of Judge.gent.
as its main metrics, leveraging ground truth action labels for quantitative assessment. The official train/test split allocates 13,602 trajectories for training and 1,681 I for testing.
AITZ Zhang et al. (2024) task-oriented mobile interaction dataset comprising 2,504 unique instructions and 18,643 screen-action pairs, along with four types of semantic annotations, covering more than 70 Android applications. Each operation trajectory contains an average of 7.5 steps, reflecting real-world user behaviors and dynamic UI scenarios, and supporting robust contextual reasoning agent evaluation. In our experiments, we follow the official split with 1,998 trajectories in the training set and 506 in the test set.
GUI Odyssey Lu et al. (2025a) is large-scale benchmark targeting complex cross-app mobile tasks. It contains 8,334 episodes (7,735 trajectories after semantic processing Xu et al. (2025b)), with an average of 15.3 steps per episode, covering 6 mobile devices, 212 distinct apps, and 1,357 app combinations. Each step enriched with detailed semantic reasoning annotations, which help agents form cognitive processes and strengthen reasoning for multi-step and cross cross-application navigation. We follow the official in-domain split protocol, resulting in 5,801 trajectories for training and 1, 1,934
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Prompt Process Reward Model
System Message
You action validater for GUI outomation systems. Your role evaluate whether proposed action logical and appropriate step toward completing given
## Core Responsibiiitie:
Validate that the oction makes progress toward the stated gool: Consider the context provided actions: Evaluste feasibility the oction follows logical VI patterns.
appropriateness
Validation Criterio:
*AVAD octions must:
confextually appropriate given previous Follow standard interac patterns: Target the correct UI elements for the intended task, Represent reasonable next steps the workflow.
Directly
completing the
**INWALID actions include:**
Actions that contradict the human instruction: Redundant actions that repeat previous unnecessorily Actions targeting incornect away the objective.
Steps would
#*# Context Usoge:
Past actions provide workflow context but don't need to repeated, Focus whether the current action logically follows from the
inst
current
applications
#+ Action Space:
The following octions evailable the mobile GUI outomatioo system:
**vvilable Actions:**
"eyy Perform a key event (supports odb keyevent syntax "volume_upp up' "valume__oww "power", "camerd": "clear"): "click": Click at
coordinati (x. y) on screen: pres_ Press at coordinate (x. y) for specified seconds: swipe' Swipe from coordinate (x,
coordinate2 (x2, y2): "ype' Input text activated input bexc" systemm button : iPrsss system button 'Back". "Home" "Menu" "Enter"). ("success' "failure")
open' Open app
specified
changes, "termiaate End
#41 Output Requirements:
Respond with exactly word: "valid" 'invalid"
explanations, reasoning,
Base decision on logical progression toward the human instruction
User
The query: [user_request]
History octions: (history_octionn
Current action to validate: [current_action]
[current_ ]crrret_ssreeshht]]]
Content: (valid/invalid]
Figure 13: Prompt for Process Reward Model of JudgeAgent.
Positive Samples Distribution system juttin
Negative Samples Distribution buttan
swipe
weit
6.3%
6.8%
5.9%
6.1%
type
13:4%
terminate
58.396
cick
15.4%
terminate
clik
Figure 14: Distribution diagram of positive and negative sample data for the JudgeAgent.
for testing. AMEX Chai et al. (2025) is mobile GUI dataset focused on evaluating agent execution under constrained or domain-specific contexts. contains 2,991 trajectories averaging 11.9 steps each, all allocated training, with official test set provided.
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Prompt Intent Recycling Filter
System Message
# # GUI Agent Fath Quality Assessment Expert
You prafessional GUT autamation quality assessment expert. Your task analyze SUI operation execution patha provided
users and evaluate whether there are redundant or erroneous steps in the patns.
## Assessment Target Description
Path
Linear operation sequences
Action Description: GUI operations from previous node curnent node (click, swipe, input, back, etc.)
-GUI Final State: Screershot state efter executing all operations
## Core Assessment Dimensions
#WW 1. Operation Redundoncy Anclysis
Duuplicate Operations"*: Whether
intermittent identical operations
"Invalid Loops**: Whether there are meaningless operations like entering on interface and immediately exiting
Unnecessory Steps**: Whether there operation sequences that could achieve the same effect through shorter paths are unnacessory operations returning to previous
State Rollbocks"*;
re-entering
### Operation Correctness Verification
** Context Adaptation"*: Whether operations match the current SUZ
Element Validity**; Whether there are operations clicking non-existent or non-interactive elements
Input Rationality"*: Whether Text input and selection operations comply with interface requirments
Consistency**: Whether
charges
performed operations
### Logical Flow Reasonab laness
** Operation Sequence**; Whether
order follows normal behaviar logic
State Transitions** Whether interface state changes ofter each operation are reasonable
Dependency Relations**: Whether prerequisite relationships between operations are followed
"EEcception Hondling Whether unnecessory recovery operations included
## Scoring Criteria
**09--00*; Path operations technically sound, redundant steps, clear logical flow
**..70.,
basically reasonable, with minor redundoncies maintains good quality
**0.5-0.6**; Path is technically feasible but has obvieus redundancies or ficifit operation sequences
**0.3-0.4 Path has mony technical with legical invalid operations
.^00000.2** Path leverely redundont or containd erroneous operations that may coude execurion failures
## Output Format
Quality [numenical
## Important Considerations
Focus technical cornectness and operction efficiency of the path itself
Consider user operation habits interface resporst characteristics
Distinguish between necessary wairing/confirmatinnn confirmationnn operations and actual redundant steps
path quality based solely operation sequence
User
Actions: [oll_ wall_histryy_ctiinn
cuurrent_screenhhon
Assistant
Quality Score: (numenical value from 0.0-10)
Figure 15: Prompt for intent recycling filter.
CAGUI Zhang et al. (2025b) is a Chinese benchmark dataset covering both grounding and agent tasks in realistic mobile applications. It consists of 600 tasks across a variety of app scenarios, with each task comprising a natural-language query, screenshot, and the corresponding answer operation, totaling 4,516 single-step images. CAGUI enables robust evaluation in multilingual set- tings and provides comprehensive assessment of model reliability and adaptability to unseen tasks and interfaces. Furthermore, since its training set has not been released and all 600 tasks are used for testing, CAGUI is particularly suitable for evaluating the generalization ability of GUI agents in novel scenarios. In our experiments, we use CAGUI for scenario generalization testing to thoroughly assess the robustness and adaptability of the method.
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Prompt for Intent Generation
System Message
# Intent Generation Expert
sequence of operations corresponding UT screenshots provided by user. your You generation expert. Given Given
to generate concise intent description that represents the task or request that could be accamplished through this operation path.
## Task Overview
"*Input"*: sequence GUI operations along with the post operation screenshot
""Output"": A clear, actionable intent description written as command or request
## Intent Seneration Guidelines
#### Analysis Facus
** Operation Pattern Recognition*:: Identify the overall behavior pattern from the sequence. of actions
U Progression"*. Anolyze
change throughout path
**Completion Indicators"*: Determine what the user has actually achieved by the end of the sequence.
### Intent Characteristics
Actionable" The Intent should describe concrete task or goal as command
"oompeete*" Reflect the full scope of what can be accomplished through this path
"NNoturl!*:: Use longuage resembles
request instruction
""Specffi**; Include relevont details (opp nomes, search terms, specific actions) when they add clarity
### Description Standards
"Perspective" imperstive form commands requests ('Oper.." "Search for..." ". "Add...")
**ense**; Use present tense imperative to indicate actionable requests
"Concisenesss"
captures "Specificity" Include key details like app nomes, search queries, or specific items when relevent
of the tosk
## Output Requirements
Format"": only,
explanationd
Struccure"
ison
('description":" single sentence describing the task command or request")
User
Actions: aol_history_actions [curvent_screenhhe
Assistont
Description single sentence describing the task command request]
Figure 16: Prompt for intent generation of recycling strategy.
B.4 METRICS
We evaluate model performance using several action-level and type-specific metries. The main definitions are as follows:
Step Success Rate (SR) The proportion of steps where the predicted action type and all corre- sponding parameters exactly match the ground truth:
ΣN ((excct_matchh)
SR
where N is the total number of steps and T(.) is the indicator function.
Action Type Accuracy (TP) The proportion of steps where the predicted action type matches the ground truth, regardless of parameters:
MN Die I(yyee match,)
TP
(5)
N
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Prompt for Basic Intent Generation
System Message
You multimodal assistant capable recognizing functions
screenshat and generating diverse intents.
Input
The system will provide a screenshot of the home page of popular app.
Task Workflow
I.Identify
service functions
Observe text,
loyout screenshot
functional
commonly
Output concise list of function nomes and descniptions (to facllitate later intent generation).
2.Generote original functional intents
1.
Bosed the necognized functions, construct specific, executable natural longuage intents,
clearly described contain least one modifiable condition (time location person).
3. Exomple: "Set on alorm for 6 a.m.." "Check today's 's weather in Beljing.
3, C.Codiition rewriting
1. For each original ment.generate newnitten
Rewrite conditions may
Time: change to different date or time of day
Location: change to another city londmark
Person: the involved object person Keep functional type intent unchanged, only odjust the conditions. Ensure the rewrites are reasonable and diverse, avoiding high repetition.
Output Format
[Function Recognition]
Function Nome Function Description
[Original Functional Intent & Rewrite]
Original XXXXX
Rewrite XXXXX
Rewrite XXXXX
Rewnite XXXXXX
Rewrite XXXXX
Original Intent 2: XXXXX
Rewrite XXXXX
Original Intent XXXXX
Rownite XXXXX
Rewnite XXXXX
Rewnite
Rewnite XXXX
Rewnite XXXXX
User
Irput: [app name] ]
Assistant
Function Recogrition: [Description of functions]
Intents: (Descniption intents)
Figure 17: Prompt for basic intent generation.
Mining Success Ratio (MSR) The proportion of successful mining attempts, where mining task considered successful if the model judges it as successful and the number mining steps does not exceed the predefined upper limit; otherwise, it is counted as failure:
MSR Nuining.sucoess
(6)
Nmining total
where N "mining_succes denotes the number of successful mining attempts, and N Wuining.ttal is the total number of mining attempts.
Data Quality Accuracy (DQA) The proportion of trajectories that entirely correct and all tra- jectories. A trajectory considered correct if it aligns with the expected intent, executes all actions without errors, and successfully completes the instruction; otherwise, it is counted incorrect:
Noorrect
DQA
Nuntal
where Neorrect the number of correct trajectories, and Niotal is the total number trajectories.
All metrics computed using strict matching rules described in implementation. For action- specific accuracy, only steps corresponding to the respective action type are included in the denom- inator.
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Prompt for Complex Intent Generation
System Message
Role Task:
intelligent essistant specialized understanding user inside mobile applicatiors
apps, players,
social media apps, note taking apps, calendar apps, nevigation apps, weather apps, and shopping apps). Your job is to take simple APP openction intent and expand into more complex, long sequence intent by adcing extra conditions and comb multiple features inside the opp
Expansion Rules:
Preserve the goel original intent. Only include
features
within mobile apps.
Add two or more additional conditions, actions, or features. The output must be natural user intent statenent without explonation. Exomples:
Input: "Set on alorm for a.m."
a.mm.anathes another of 2 p.m. and one more p.m." Input: "Play populor song'
Output: 'Set on alarm at a.m.
Output: "Play a popular song. share
my sockal
my favorites playlist
Input: "Record meeting notes the Notes app"
Output: 'Record meating notes in the Notes opp, set reminder to review them ot and them to cloud storage"
Input: "Check
weather Output: 'Check today's weather Weather opp"
Weather app a daily weather
a.m."
Input: "Search for nearby coff shops Maps'
Output: 'Search for nearby coffee shops in Mops, navigate the highest-roted one, and share the location with friend"
User Expond:
User
Imput: intent]
[current_ecreenshot]
Assistant
Output [Description ton of complex intent]
Figure 18: Prompt for complex intent generation.
B.5 BASELINES
We compare our method with the following baselines demonstrate its superiority. The closed- source baselines include major MILMS such as GPT-4o Hurst et al. (2024), Gemini 2.0 Deepmind (2024) and Claude Anthropic (2024). For open-source models, we include Odyssey Agent Lu et al. (2025a). SpiritSight-8B Huang al. (2025), GUI-R1-7B Luo al. (2025), OS-Atlas-7B Wu et al. (2025a), Aguvis-77B 7B Xu et al. (2025b), InfiUU-RI-3B Liu et al. (2025), Qwen2.5- VLL-3B Bai et al. (2025) and Qwen2.5-VL-7B Bai al. (2025). We further consider representative models trained on automatically mined data, such as OS- OS-Genesis-7B Sun et al. (2025) and GUI-OwI-7B Ye et al. (2025), well the model based on private numan-annotated data, i.e., UI-TARS-7B Qin et al. (2025). Baseline results and evaluation protocols follow the original paper to ensure fairness.
B.6 CALCULATION DATASET CONSTRUCTION COST
The costs for the other datasets are calculated using the following formula:
Cother = Ning X (fannot + (inspec) X Twager
(8)
where Ning refers to the images number. The total cost to construct our dataset is $466, which is composed of $196 for manual quality inspection and $270 for computational overhead, derived from the following formula:
Cintal Cinspect + Ceomp:
Cinspect
Nimg lingpect X Twages
Ceomp Tirain Guain Purin + Tmine X Gmine X Pmine:
(10)
where Ning 20,000 is the number of images. The training stage runs for Tumin = 24 h on Guain
A100-80G GPUs rental price of Pusin $0.994//PP//. The data mining stage runs for Tmine 26.7 on Gmine = L40-45G GPUs, priced at Pmiie 43.3//// based on Vast.ai Vast.ai (2025).
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B.7 INFRASTRUCTURE FRAMEWORK AND ENVIRONMENT INTERACTION
As illustrated in Fig. 4 of the main paper, our infrastructure framework adopts a layered architec- ture. The data layer organizes intents, interaction trajectories, screenshots, and metadata via an intent-trajectory tree structure. The engine layer implements a progressive training framework that starts with pre-training and then goes through three subsequent stages (Stage to Stage 3), enabling support for a model-in-the-loon Tree Search (MCTS) controller to handle selection, expansion, simulation, and backpropagation. The agent layer introduces multi-agent collaboration mechanisms for inference, orchestration, and reward evaluation during data mining. The execution layer integrates action parsing with toolchains, translating text/action parsing into ADB interface calls such as click, type, and swipe. The envi- ronment layer provides sandbox and virtual machine cluster support including environment control, isolation and security, and resource monitoring.
training strategy. The algorithm layer centers on a Monte Carlo
We use the Android Studio virtual machine (Android API 36) as our sandbox environment. We use adb commands to capture GUI screenshots from the virtual machine. To control the virtual machine, we parse the structured output (i.e., JSON code) of the GUI Agent into executable adb commands. In each iteration of the mining algorithm, machine via adb commands. The Python process then retrieves the screenshot and combines it with text prompt to construct the prompt. This prompt is fed the inferAgent, which then produces a structured representation (i.e., JSON code) of an action. We parse the structured representation into the corresponding adb command, send the command to the virtual machine, and the virtual machine executes the operation accordingly. This completes one cycle of state acquisition -> action reasoning + operation execution.
first obtain screenshot of the virtual
C VISUALIZATION RESULTS
C.I EXAMPLES OF ERRONEOUS SAMPLES IN PUBLIC DATASETS
In this section, we present erroneous samples of AC and AITZ. As shown in Fig. 19,
C.2 EXAMPLES OF INTENT-TRANEE TRAJECTORY TREE
Examples of intent-trajectory trees for the AITZ and AndroidControl datasets are presented in Fig. 20 and Fig. 21, respectively. Each intent trajectory tree contains multiple intent-trajectory pairs. Taking Fig. 20 as an example, there are three catent-rrjecctory pairs in total. The original intent is "search for usb-a to usb-c on amazon.com then add to cart", while the recycled intents are "Search for smart watches in the Amazon app" and "Search for USB-A to USB-C cables on Ama- zon"". Based on our approach, we are able to generate diverse potential intents, thereby achieving significant diversity beyond the constructed intents.
C.3 EXAMPLES OF DATA MINING PROCESS
We present demo video of the data mining process using MP-Miner on both AndroidControl and CAGUI benchmark suites. As shown in the video, our method is able efficiently perform intent trajectory mining across both Chinese and English app scenarios. This highlights the effectiveness and versatility of -2-Miner in handling diverse language settings and application domains.
C.4 EXAMPLES OF GUI AGENT INFER
In this section, we present testing cases of M three representative benchmark datasets: AndroidControl, AITZ and CAGUI. As shown in Fig. 22, 23, we visualize all sequential images contained within an intent for AndroidControl and AITZ. For each image, provide the intent, thought, exact match, and action results, where the predicted action is marked with a red triangle and the ground truth action is marked with green circle. In addition, present more intu- itive example of CAGUI in the demo video. These visualizations demonstrate that the GUI agent trained with our method can generalize well across different environments and task lengths, achiev- ing promising results in weal-world scenarios.
25

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Published as a conference paper at ICLR 2026
Intent: How do get to the nearest AT&T Store? (AITZ)
Intent: My phone is not functioning properly, therefore I'm going to turn it service. Upload the DIY project file to Google Drive to save it. (AC)
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Google Mesd Documer
Googe Peic
Tokle
Pr. J00,00000 1000000-220
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JOONO HEH000000
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S00000 FRLLDO
Figure 19: Erroneous samples of AC and AITZ.
26

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Published as a conference paper at ICLR 2026
Intert Recycleg Search for svort wotches in the Arzzznn upp
Intert Recychig: Search for USB USB-C cabiss on Amazznn
Original Intert: search for udb to ub thet odd to cart
Figure 20: Example of Intent- TrajectTry Tree on AITZ. The number of candidate actions K is set to 3. The original intent is marked in green, while recycled intents are marked in red. Two intent- trajectory data are additionally recycled through our method.
27

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Published as a conference paper at ICLR 2026
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Figure 21: Example of Intent- TrajectoTa Tree AndroidControl. The number of candidate actions K is set to 2. The original intent marked in green, while recycled intents are marked in red. Two intent-trajectory data are additionally recycled through method.
28

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Published as a conference paper at ICLR 2026
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Figure 22: Test Case on AITZ. The user intent is "Empty the shopping cart on bestbuy.com Search for razer kraken on bestbuy.com, select the first entry, add it to the cart, then select checkout."
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Figure 23: Test Case on AndroidControl. The user intent is "I'd want to share my created art to my
friend Rick at rick. weinstein wew.wwsteii@ddarrp..commm daayrep.com so that I can obtain feedback on it."
29


Reviews:
[{"review_id": "y4xg55P6Qo", "reviewer": "Reviewer_WPrJ", "summary": "This paper proposes M²-Miner, an automated framework for mining mobile GUI agent training data using Monte Carlo Tree Search (MCTS) enhanced with a collaborative multi-agent system. The framework introduces InferAgent, OrchestraAgent, and JudgeAgent to enhance expansion and simulation efficiency, an intent recycling strategy that extracts multiple intent-trajectory pairs from a single search tree, and a progressive model-in-the-loop training approach. Experiments show that GUI agents trained on M²-Miner data achieve state-of-the-art performance on several mobile GUI benchmarks while significantly reducing annotation costs.", "questions": "Did you build a custom framework to run the model-in-the-loop training strategy? If so, please provide more details. If not, please specify the base framework used.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The intent recycling strategy re-evaluates sibling paths to extract multiple intent-trajectory pairs from a single search tree, significantly improving data diversity and mining efficiency without additional exploration costs.\n\nThe progressive model-in-the-loop training implements a three-stage training strategy, allowing agent capabilities to improve progressively in tandem with data complexity, which enhances the mining success rate in unseen scenarios.", "weaknesses": "- The ablation study should be expanded: include a baseline using the stronger 72B model for InferAgent and JudgeAgent, but without the model-in-the-loop (MITL) strategy. This is necessary to validate the true effectiveness of MITL.\n\n- The paper mentions using 8 A100-80G GPUs for training and \"retraining for 2 epochs on the full mined dataset at each stage\". These significant computational costs, as well as the API costs for Qwen2.5-VL-72B, seem to be omitted from the 196 total cost claimed in Table 1. It is better to clarify whether the 196 figure covers this computational overhead.\n\n- The dataset partitioning is unclear. Your training set (during warm-up ) and test set both use AC and AITZ. You need to provide more detailed information, such as statistics on the exact splits, to clarify how data overlap is prevented, especially since ICLR appendices have no page limits.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "S0mDBQBoHs", "reviewer": "Reviewer_eLqw", "summary": "This paper proposes a multi-agent framework for mobile GUI agents. By introducing a tree-based structure, it organizes and stores learned operations in a structured and reusable manner. The framework incorporates three specialized agents to optimize the MCTS search process and reward estimation, effectively avoiding inefficient random exploration. Furthermore, it introduces a model-in-the-loop training paradigm that enables continual learning and self-improvement during deployment, expanding the system’s learning capability from a relatively small initial dataset. Experimental results show significant performance improvements, and comprehensive ablation studies validate the effectiveness of each component.", "questions": "Q1. Consider comparing with more recent agent-based methods, such as AppAgent [1], or provide a clear justification for why these methods were excluded.\n\nQ2. Ablation Study. The ablation study lacks clarity on how different components are decoupled. Specifically, how does the InferAgent function when the JudgeAgent is removed? Please elaborate on how the agents’ dependencies are handled during ablation.\n\nQ3. Experimental Clarity. It is unclear how large language models (LLMs) are wrapped or packaged as agents for GUI interaction in your experiments. Please explain how the LLMs receive GUI state inputs and produce executable actions, and whether environment feedback is included in this loop.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "S1. Strong Experiments. It compares with 13 methods and analyzes the effect of agent numbers and online learning strategies, showing solid and comprehensive evaluation.\n\nS2. Practical Significance. The framework is scalable and adaptable, demonstrating potential for real-world GUI automation and broader mobile applications.\n\nS3. Trajectory Recycling is an interesting and computation-efficient design.", "weaknesses": "W1. Writing and Presentation Issues. The paper contains several typos and minor writing problems that affect readability:\n\n1. Line 52: “we presents” ->“we present”\n2. Line 274: “where i denotes the i-th visit to the node” appears twice.\n3. Line 353” “This is crucial when targeting new application scenarios.” is unclear — please specify what scenarios are referred to.\n4. Line 480 “significantly improve” -> “improves”.\n5. Line 484 “an solid foundation” -> “a solid foundation”\n6. Line 485 “Statement of Using LLM” lacks a period at the end.\n\nW2. Outdated Baselines. The comparison does not include the latest agent-based methods, such as AppAgent[1]. The authors should either explain the exclusion or add these newer methods to the experiments.\n\nW3. Clarity of Agent Interaction\n\nThe description of how the three agents (Infer, Orchestra, and Judge) coordinate during the MCTS process remains vague. A clearer explanation of their information flow and role boundaries would improve reproducibility and reader understanding.\n\n[1]. Zhang, Chi, et al. \"Appagent: Multimodal agents as smartphone users.\" *Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems*. 2025.\n\nW4. The novelty of applying MCTS and multi-agent in the GUI data mining is unclear.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "1QDEicSZKI", "reviewer": "Reviewer_xyHC", "summary": "This paper propose M2-Miner, the first low-cost and automated mobile GUI agent data-mining framework based on Monte Carlo Tree Search(MCTS). For better data mining efficiency and quality, the paper present a collaborative multi-agent frame work, comprising InferAgent, OrchestraAgent, and JudgeAgent for guidance, acceleration, and evaluation. To further enhance the efficiency of mining and enrich intent diversity, this paper design an intent recycling strategy to extract extra valuable interaction trajectories. Additionally, a progressive model-in-the-loop training strategy is introduced to improve the success rate of data mining.", "questions": "see the weaknesses", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1.This paper propose a fully automated framework for mobile GUI agent data mining. By introducing MCTS and designing a collaborative multi-agent framework, the method improve data mining efficiency while enhancing data quality. \n2.The intent recycling strategy further enhances both mining efficiency and intent richness, while the progressive model-in-the-loop training paradigm boosts success rates in both familiar and novel environments.\n3.Extensive experiments show that GUI agents trained on the mined data achieve SOTA performance.", "weaknesses": "1. The paper propose an automated mobile GUI agent data-mining framework based on Monte Carlo Tree Search(MCTS). Monte Carlo tree search is a classic algorithm, is its innovation insufficient?\n2.The background knowledge of MOBILE GUI AGENT DATA MING was not sufficiently introduced in the paper writing, making it difficult to understand.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "TkEza4mtMl", "reviewer": "Reviewer_gnp5", "summary": "The paper introduces M-Miner, a low-cost and automated framework for mining high-quality intent–trajectory pairs to train mobile GUI agents. It builds on Monte Carlo Tree Search and uses a collaborative multi-agent setup with InferAgent for exploration guidance, OrchestraAgent for coordination and speedup, and JudgeAgent for trajectory evaluation. An intent recycling strategy increases intent diversity, and a progressive model-in-the-loop scheme improves mining success over time. Experiments indicate that agents fine-tuned on the mined data achieve state-of-the-art on several mobile GUI benchmarks.", "questions": "1. I think the comparison in Table 1 is unfair and lacks practical significance. Were the time costs considered? Table 2 reveals limitations of the method. How exactly is the human quality review conducted? And its cost?\n2. In Figure 3, the content measured by the acc metric is not defined.\n3. Line 373, the explanation of CAGUI may be problematic. Since the training data are not released, how do you ensure there is no test-data leakage during training? Generalization experiments are generally conducted entirely on unseen apps. \n4. Baselines are missing, e.g., [1] Falcon-UI: Understanding GUI Before Following User Instructions and [2] MobileIPL: Enhancing Mobile Agents’ Thinking Process via Iterative Preference Learning. To my knowledge, these are not concurrent works.\n5. Table 1 mentions AMEX and GUI-Odyssey as standard GUI evaluation datasets. I recommend adding experiments on these datasets (not strictly required during the rebuttal phase, given time constraints).\n6. In Section 4.4, the intent evolution procedure appears not very different from the instruction filtering in [3] DiGIRL: Training in-the-wild Device-control Agents with Autonomous Reinforcement Learning. Is this understanding correct? \n7. M2-Miner-Agent training dataset + other datasets like AITZ do demonstrate the validity of the data, but how can we ensure that there is no training data leakage, because the newly generated instructions are very similar to the original ones, but we all know that the original train and test instructions are also very similar and share the same data distribution.\n8. If the original data is removed from the ablation experiment and only the mined data is used, what will the model results be?\n9. What is the difference between mining data from scratch using brand new instruction templates in your framework, compared to replacing existing instructions with slots? I think a comparison might be necessary, as the paper doesn't explain why it's necessary to mine data on existing data.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The M-Miner multi-agent framework automatically collects data based on intents and appears to be the first work to do so; it reduces the time and cost of manual annotation.\n2. Models trained on this dataset achieve strong performance, indicating the dataset’s potential value.\n3. The framework demonstrates sufficient novelty, and ablation experiments show that each component plays a sufficient role.\n4. The paper is clearly structured and easy to understand.", "weaknesses": "1. The chapter organization needs adjustment. The theoretical preliminaries of MCTS are not difficult for researchers in RL-based agent domains. The authors devote about a page to this, which seems unnecessary. It could be moved to the appendix, freeing space to detail the three-stage intent component and the training procedure. A table-style exposition would suffice rather than a full appendix-level example.\n2. Although the results are SOTA, the gains are quite limited relative to some Qwen2-VL–based methods; see the Questions for specifics.\n3. The approach assumes expansion from an existing dataset rather than starting entirely from scratch. It is unclear why data were not collected from scratch on the same apps. As presented, the method resembles data augmentation.\n4. The quality validation reveals shortcomings in the method, and the paper does not discuss the cost of human quality review.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)