MLLM Explorer Agent Framework

• MLLM Explorer Agent is a multimodal system that integrates large language models with decision-making modules for autonomous exploration of digital and real-world environments.
• It employs perception modules, agentic planners, and action generators to systematically traverse interfaces and synthesize structured knowledge.
• The framework enables efficient knowledge extraction and scalable automation, reducing costs while enhancing test coverage and exploratory depth.

A Multimodal LLM (MLLM) Explorer Agent is a framework or autonomous system built upon advanced LLMs, often extended to handle multimodal data inputs (e.g., text, vision, audio), designed to actively, adaptively, or intelligently explore complex digital, physical, or knowledge environments. These agents synthesize capabilities in perception, reasoning, action planning, interaction, and knowledge extraction to automate or augment exploratory tasks ranging from software interface traversal and tool selection to scientific hypothesis generation and data synthesis.

1. Foundational Concepts and Terminology

An MLLM Explorer Agent integrates LLMs with agentic decision-making and, where relevant, multimodal embedding or perception modules. “Explorer” in this context denotes an agent not simply driven by reactive, single-step responses, but one that operates over long-horizon tasks involving systematic discovery: traversing unknown environments (such as mobile app GUIs (Zhao et al., 15 May 2025), web interfaces (Ding, 4 Jan 2024), or scientific simulators (Werbrouck et al., 30 Sep 2025)), accumulating structured knowledge, and sometimes autonomously decomposing complex objectives.

Key terms include:

• Abstract Interaction Graph (AIG): High-level GUI state-action abstraction used to guide exploration with minimal LLM invocation (Zhao et al., 15 May 2025).
• Function-aware Task Goal Generator / Function-aware Trajectories: Generators that analyze GUI structure to autonomously construct semantic exploration goals (Xie et al., 22 May 2025).
• Transition-aware Knowledge Extractor: Unsupervised mechanism that mines state transition rules from observation-action-outcome triples, producing operationally relevant knowledge without human annotation (Xie et al., 22 May 2025).

MLLM Explorer Agents can be purely LLM-driven or employ edge-cloud collaborative designs, modular subagent decompositions, or knowledge-guided workflows, but share the unifying focus on exploration and discovery.

2. Architectures and Exploration Methodologies

MLLM Explorer Agent architectures are highly dependent on target domains but consistently combine several modules:

• Perception Modules: These may include visual backbones (OCR, object detection, vision encoders) to process GUI screenshots (Wang et al., 29 Jan 2024, Li et al., 5 Aug 2024), or domain-tailored submodules (e.g., spectral judges for remote sensing (Yu et al., 23 Dec 2024)).
• Agentic Planners: LLM-based reasoning layers that plan, decompose, or sequence sub-goals based on global instructions, historical interaction, or dynamically maintained context (e.g., in-context learning with DOM/history (Ding, 4 Jan 2024), memory-based reflection (Yi et al., 8 May 2025), directed acyclic graphs for multimodal queries (Nooralahzadeh et al., 24 Dec 2024)).
• Action Generators: LLMs may generate structured actions or select among an abstracted action space (e.g., TapButton, Swipe, Text input (Li et al., 5 Aug 2024)). In knowledge-driven systems, actions are guided by the evolving Abstract Interaction Graph, reducing costs and redundancy (Zhao et al., 15 May 2025).
• Exploration Policy Modules: The agent may autonomously construct goals and trajectories (function-aware exploration), or adaptively update its policy via reinforcement learning or LLM-driven stochastic processes (Hao et al., 21 May 2025, Liu et al., 29 May 2025).(Werbrouck et al., 30 Sep 2025) presents agents that freely hypothesize and experiment in black-box scientific environments.

A central methodology is the decoupling of knowledge acquisition (via periodic LLM prompt calls or unsupervised mining of transition relations) and routine low-level interaction (handled abstractly or by non-LLM code), boosting efficiency and generalization.

3. Knowledge Extraction, Abstraction, and Maintenance

MLLM Explorer Agents emphasize systematic knowledge acquisition:

• Transition-aware Knowledge Mining is a core mechanism in GUI-explorer, where the agent autonomously collects state-action-outcome triples during exploration, prunes out inoperative transitions, and distills operation logic as key-value pairs mapping visual UI patches to executable operations (Xie et al., 22 May 2025). This process enables continual knowledge base refinement without human labeling or retraining.
• Abstraction and Grouping: LLM-assistance is used to generate “abstract” UI states and actions by clustering functionally similar screens and interactions. The resulting Abstract Interaction Graph serves as the agent’s knowledge backbone, efficiently guiding further exploration or test coverage (Zhao et al., 15 May 2025).
• Multimodal Context Integration: For scientific or data-intensive environments, explorer agents may employ modular subagents (e.g., geological image judges, spectral data judges (Yu et al., 23 Dec 2024)) whose outputs are hierarchically fused by a decision module, often using mathematically weighted integration.
• Memory and Reflection: Edge-cloud frameworks such as EcoAgent maintain concise textual state histories and invoke reflection modules for failure recovery, supporting robust, adaptive action in open-world environments (Yi et al., 8 May 2025).

4. Efficiency, Scalability, and Evaluation

MLLM Explorer Agents are specifically engineered for high efficiency and broad applicability:

Agent/Framework	Key Efficiency Strategy	Reported Gains
LLM-Explorer (Zhao et al., 15 May 2025)	LLM for abstraction, not per-step action	148× lower LLM-token cost; up to 35% higher coverage
EcoAgent (Yi et al., 8 May 2025)	Edge-only execution, compact texting	>27× reduction in cloud-token cost; similar SR to cloud agents
GUI-explorer (Xie et al., 22 May 2025)	Training-free, autonomous mining	2.6%–11.7% absolute SR gain

Benchmarks such as SPA-Bench, AndroidWorld, AitW, and in-house app collections are used, with task success rates, activity coverage, process scores, and resource consumption as primary metrics. Notably, task completion rates of ~53.7% (SPA-Bench) and procedure-specific success rates of 66.92% (AitW) demonstrate competitive or state-of-the-art performance.

5. Applications and Domain-Specific Extensions

MLLM Explorer Agents have demonstrated widespread applicability:

• Mobile and GUI Automation: Advanced agents automate complex, multi-step mobile device or cross-app workflows using vision-centric perception and SOP-driven in-context planning (Ding, 4 Jan 2024, Wang et al., 29 Jan 2024, Li et al., 5 Aug 2024).
• Efficient Fuzz/Testing: The abstraction-based approach allows for rapid, coverage-oriented UI exploration, facilitating effective bug/malware detection and systematic test generation (Zhao et al., 15 May 2025).
• Autonomous Knowledge Discovery: Agents can engage in iterative, unsupervised scientific inference, generating and verifying hypotheses about black box systems in materials science, guided by minimal probe feedback (Werbrouck et al., 30 Sep 2025).
• Data Synthesis: Multistage MLLM agents generate high-quality 2D/3D/4D synthetic data by orchestrating asset collection, generative modeling, semantic refinement, and temporally coherent planning (Feng et al., 7 Aug 2025).
• Machine Learning Engineering: RL-trained explorer agents for ML optimize across the ML lifecycle, unifying fine-tuning, stepwise RL, and structured reward signals for generalization across diverse ML tasks (Liu et al., 29 May 2025).

6. Limitations, Safety, and Future Directions

Several practical and theoretical challenges are identified:

• Adaptation and Update: Although transition-aware mining enables zero-fine-tuning adaptation, long-term navigation of highly dynamic or obfuscated GUIs remains nontrivial (Xie et al., 22 May 2025, Wang et al., 29 Jan 2024).
• Vulnerabilities: MLLM societies present transfer and security risks, as single compromised agents can propagate malice via adversarial prompt construction (Tan et al., 20 Feb 2024).
• Exploration Path-Dependence: Knowledge acquisition and final discoveries exhibit strong path-dependence, both in experimental science and software exploration, suggesting the need for multi-agent diversity or human-in-the-loop counterbalancing (Werbrouck et al., 30 Sep 2025).
• Resource Balancing: Trade-offs exist between per-step inference cost, knowledge update fidelity, and exploration breadth, particularly salient in edge-cloud scenarios (Yi et al., 8 May 2025).
• Ethical and Societal Concerns: Issues include privacy-respecting prompts for sensitive operations, robustness against prompt injection, and responsible release of autonomous exploratory systems.

Ongoing research explores modular multi-agent decompositions, more adaptive reward and feedback modeling, and generalization to new modalities (e.g., audial, physical robot navigation). Practical directions include tighter integration of memory, continual learning, and real-time, intent-aware personalization.

7. Summary Table of Recent Key Contributions

Paper/Agent	Primary Contribution	Distinguishing Features
MobileAgent (Ding, 4 Jan 2024)	SOP-driven mobile automation	In-context SOP, privacy-aware interactive tasks
LLM-Explorer (Zhao et al., 15 May 2025)	Efficient GUI exploration	Abstraction graph, minimal LLM use
GUI-explorer (Xie et al., 22 May 2025)	Training-free GUI knowledge mining	Function/transition-aware mechanisms
EcoAgent (Yi et al., 8 May 2025)	Edge-cloud collaborative automation	Pre-Understanding module, memory-reflection
ML-Agent (Liu et al., 29 May 2025)	RL-based ML engineering explorer	Stepwise RL, agentic reward, exploration-enriched SFT
Knowledge Discovery (Werbrouck et al., 30 Sep 2025)	Scientific black-box exploration	Hypothesis generation, persistent experimentation

A plausible implication is that MLLM Explorer Agents represent a generalizable blueprint for automated, knowledge-driven, and cost-efficient exploration across digital and scientific domains, unifying the strengths of advanced LLMs, multimodal perception, and structured autonomous reasoning.