REVIEW 24 cited by
GPT-4V in Wonderland: Large Multimodal Models for Zero-Shot Smartphone GUI Navigation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
GPT-4V in Wonderland: Large Multimodal Models for Zero-Shot Smartphone GUI Navigation
read the original abstract
We present MM-Navigator, a GPT-4V-based agent for the smartphone graphical user interface (GUI) navigation task. MM-Navigator can interact with a smartphone screen as human users, and determine subsequent actions to fulfill given instructions. Our findings demonstrate that large multimodal models (LMMs), specifically GPT-4V, excel in zero-shot GUI navigation through its advanced screen interpretation, action reasoning, and precise action localization capabilities. We first benchmark MM-Navigator on our collected iOS screen dataset. According to human assessments, the system exhibited a 91\% accuracy rate in generating reasonable action descriptions and a 75\% accuracy rate in executing the correct actions for single-step instructions on iOS. Additionally, we evaluate the model on a subset of an Android screen navigation dataset, where the model outperforms previous GUI navigators in a zero-shot fashion. Our benchmark and detailed analyses aim to lay a robust groundwork for future research into the GUI navigation task. The project page is at https://github.com/zzxslp/MM-Navigator.
Forward citations
Cited by 24 Pith papers
-
OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments
OSWorld provides the first unified real-computer benchmark for open-ended multimodal agent tasks, exposing large performance gaps between humans and state-of-the-art LLM/VLM agents.
-
A Comprehensive Study of Implementation Bugs in Multi-modal Agents
First systematic taxonomy of 158 multi-modal agent bugs plus a runtime analyzer that recovers most open issues and surfaces 31 new ones.
-
SEE: Structure-aware Exploring \& Exploiting for Long-horizon GUI Agent Trajectory Synthesis
A graph-based pipeline automatically synthesizes long, multi-step mobile-app interaction trajectories, and fine-tuning GUI agents on them improves task success and cross-benchmark transfer.
-
GUICrafter: Weakly-Supervised GUI Agent Leveraging Massive Unannotated Screenshots
GUICrafter uses curriculum learning on unannotated GUI screenshots for visual grounding followed by RL calibration on limited labels to match or exceed prior GUI agents with far less annotation.
-
GoClick: Lightweight Element Grounding Model for Autonomous GUI Interaction
GoClick is a compact 230M-parameter encoder-decoder VLM for GUI element grounding that matches larger models' accuracy via a Progressive Data Refinement pipeline yielding a 3.8M-sample core set.
-
AgentProg: Empowering Long-Horizon GUI Agents with Program-Guided Context Management
AgentProg reframes interaction history as a program with variables and control flow, plus a belief state for partial observability, achieving SOTA success rates on long-horizon GUI benchmarks while baselines degrade.
-
UltraCUA: A Foundation Model for Computer Use Agents with Hybrid Action
Training computer-use agents on a hybrid GUI-plus-programmatic-tool action space, with SFT and online RL, improves OSWorld success by ~23% relative over GUI-only baselines and transfers to Windows without Windows-spec...
-
CODA: Coordinating the Cerebrum and Cerebellum for a Dual-Brain Computer Use Agent with Decoupled Reinforcement Learning
A decoupled planner-executor GUI agent, trained by per-app reinforcement learning followed by specialist-to-generalist distillation, lifts ScienceBoard success from about 7.6% to 21.0% average and 40% pass@8.
-
SEAgent: Self-Evolving Computer Use Agent with Autonomous Learning from Experience
A self-evolving computer-use agent trained with full-trajectory state judging and curriculum task generation goes from 11.3% to 34.5% average success on five OSWorld apps, and a specialist-to-generalist variant beats ...
-
DroidRetriever: A Transparent and Steerable Automation System for Collaborative Mobile Information Seeking
DroidRetriever is a transparent steerable mobile automation system that decomposes information-seeking tasks with multi-LLM agents, navigates apps, synthesizes reports with screenshots, and provides a dashboard for re...
-
TraceVLA: Visual Trace Prompting Enhances Spatial-Temporal Awareness for Generalist Robotic Policies
Visual trace prompting improves spatial-temporal awareness in VLA models, delivering 10% gains on SimplerEnv and 3.5x on real-robot tasks.
-
Crepe: A Mobile Screen Data Collector Using Graph Query
Crepe introduces a graph-query technique in a no-code Android app for flexible collection of targeted mobile screen data with privacy and consent controls.
-
SeeClick: Harnessing GUI Grounding for Advanced Visual GUI Agents
SeeClick improves visual GUI agents via GUI grounding pre-training on automatically curated data and introduces the ScreenSpot benchmark, with results indicating that stronger grounding boosts downstream task performance.
-
DRS-GUI: Dynamic Region Search for Training-Free GUI Grounding
DRS-GUI introduces a dynamic region search method with Focus/Shift/Scatter actions and MCTS-based planning that improves GUI grounding accuracy by 14% on ScreenSpot-Pro for both general and GUI-specific MLLMs without ...
-
Harnessing Uncertainty: Entropy-Modulated Policy Gradients for Long-Horizon LLM Agents
EMPG re-weights policy-gradient updates by step-level token entropy, amplifying confident correct actions and muting uncertain ones, and adds a future-clarity bonus.
-
A Comprehensive Survey of Agents for Computer Use: Foundations, Challenges, and Future Directions
A survey of 87 agents for computer use and 33 datasets that introduces a three-dimensional taxonomy across domain, interaction, and agent perspectives and identifies six research gaps.
-
AppAgent: Multimodal Agents as Smartphone Users
AppAgent lets large language models operate diverse smartphone apps via visual interactions and learns app usage from exploration or demonstrations.
-
Large Language Model-Brained GUI Agents: A Survey
A survey consolidating frameworks, data practices, large action models, benchmarks, applications, and research gaps in LLM-brained GUI agents.
-
Rethinking Agentic Reinforcement Learning In Large Language Models
The paper reviews conceptual foundations, methodological innovations, effective designs, critical challenges, and future directions for LLM-based Agentic Reinforcement Learning.
-
Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security
This survey discusses key components and challenges for Personal LLM Agents and reviews solutions for their capability, efficiency, and security.
-
Rethinking Agentic Reinforcement Learning In Large Language Models
The paper surveys the conceptual foundations, methodological innovations, challenges, and future directions of agentic reinforcement learning frameworks that embed cognitive capabilities like meta-reasoning and self-r...
-
Rethinking Agentic Reinforcement Learning In Large Language Models
This review synthesizes conceptual foundations, methods, challenges, and future directions for agentic reinforcement learning in large language models.
-
Empowering Multimodal LLMs with External Tools: A Comprehensive Survey
A survey paper maps how external tools are used to augment multimodal large language models across data, tasks, evaluation, and future directions.
-
LLM-Powered AI Agent Systems and Their Applications in Industry
A survey categorizing LLM-powered agent systems into software-based, physical, and hybrid types, covering industrial applications and challenges such as latency and security.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.