Modular Multi-Agent Vision-to-Code

• The paper presents a modular framework that decomposes vision-to-code tasks into dedicated agents for perception, planning, and code synthesis, improving interpretability and error localization.
• Agent specialization assigns clear roles—like grounding, layout planning, and adaptive code generation—that enhance scalability, robustness, and precise task execution.
• Real-world applications in UI automation, robotics, and document understanding demonstrate significant improvements in code quality, operational efficiency, and error resilience.

Modular Multi-Agent Vision-to-Code Frameworks refer to a class of systems in which specialized agents—typically combining vision, language, and program synthesis capabilities—collaboratively translate raw visual input into executable code or structured workflows. These frameworks emphasize modularity in both network architecture and computational roles, enabling scalability, robustness, and collaborative extensibility across a diverse range of vision-to-code problem domains, including robotics, UI automation, document understanding, and autonomous mission planning.

1. Architectural Principles of Modular Multi-Agent Vision-to-Code Frameworks

A defining feature is the explicit modularization of functional components, separating perception (vision), reasoning (planning, decision-making), code synthesis, and validation across independent, interacting agents. Each agent is responsible for a distinct subtask—such as visual content recognition, scene analysis, task decomposition, code generation, or testing—with well-defined communication interfaces and state representations.

For example, in ScreenCoder (Jiang et al., 30 Jul 2025), the framework implements three modular agents: (1) a grounding agent (VLM-based component detection and labeling), (2) a planning agent (hierarchical layout tree construction using engineering priors such as CSS grid cues), and (3) a generation agent (adaptive prompt-driven HTML/CSS synthesis). The pipeline promotes interpretability and error localization, as each agent’s output is introspectable and debuggable before being passed to downstream modules.

Similarly, the AgentCoder framework (Huang et al., 2023) divides responsibilities among a Programmer Agent (code drafting and iterative refinement), a Test Designer Agent (test case synthesis), and a Test Executor Agent (execution and feedback), forming an iterative feedback loop for robust code generation. Separation of concerns and well-scoped roles improve both code quality and error resilience.

2. Agent Specialization and Role Assignment

Agent specialization is central to these frameworks, with agents tailored for highly granular roles. In MaCTG (Zhao et al., 25 Oct 2024), agent roles (Team Leader, Module Leader, Function Coordinator, Development Group members) are dynamically assigned in a tree-structured “thought graph”, recursively decomposing a project into hierarchically nested modules, functions, and subtasks. If the project requires, for example, image input, object detection, and text recognition, each agent or group is allocated a target node, coordinating through a shared “thought pool” for centralized memory and error tracing.

GeoCoder (Sharma et al., 17 Oct 2024) demonstrates specialization for modular mathematical reasoning: vision-language processing produces structured function calls to an explicit function library, and a retrieval-augmented agent (RAG-GeoCoder) supplies relevant function definitions from memory to minimize misapplication of formulas. This design reduces stochasticity and augments interpretability, as execution is grounded in deterministic code blocks.

The VideoMultiAgents framework (Kugo et al., 25 Apr 2025) further illustrates specialization: a Text Analysis Agent generates question-guided captions, a Video Analysis Agent leverages a VLM for pixel-level interpretation, and a Graph Analysis Agent builds temporal scene graphs. Their independent inferences are consolidated by an Organizer Agent, enabling query-specific fusion of multimodal insights.

3. Modularity, Scalability, and Asynchronous Collaboration

Explicit modularity in both software and agent roles allows these frameworks to scale in the number of agents, computational resources (CPUs, GPUs, TPUs), and tasks. The asynchronous multiagent methodology in (Gesmundo, 2022) (μ2Net+/μ3Net) exemplifies scalability by decoupling model evolution across parallel agents, each improving a distinct task by evolving its own module pathway. Synchronization is maintained via a shared system state updated on disk/cloud; immutability of parent modules during mutations preserves knowledge while allowing safe concurrent adaptation.

Concurrency and modularity are operationalized through agent pooling and dynamic assignment. MaCTG (Zhao et al., 25 Oct 2024) improves cost-effectiveness by hybridizing high-level reasoning (proprietary LLMs for decomposition) with local, open-source code generation models for routine subtasks, yielding a reported operational cost reduction of 89.09% versus single-model competitors. This separation also mitigates latency bottlenecks as seen in Being-0 (Yuan et al., 16 Mar 2025), where onboard computation handles reflexive skill execution and high-level cloud resources are reserved for instruction interpretation and long-horizon planning.

4. Communication, Coordination, and Feedback Mechanisms

Agent communication is managed through structured channels or message-passing frameworks—with both centralized critics (e.g., in multi-agent RL) and decentralized peer-to-peer exchanges.

In (Yoon et al., 2018), agents share compressed visual features, sensor data, and optimized virtual communication actions; a centralized critic uses concatenated encoded streams to evaluate state-action values for collaborative task learning. In UAV-CodeAgents (Sautenkov et al., 12 May 2025), communication between the Airspace Management Agent and UAV Agents is implemented via lightweight messaging. Real-time adaptability is achieved by iterative ReAct (Reason + Act) loops, supporting incremental mission updates and reflection in dynamic environments.

Feedback loops are integrated for iterative improvement. AgentCoder (Huang et al., 2023) formalizes the loop as:

$$C_{i+1} = \text{Refine}\left( C_i, V( e(C_i, T) ) \right)$$

where $C_{i}$ is the code at iteration $i$, $T$ is a test suite, $e$ is the execution environment, and $V$ is a validation function returning feedback. AgentCoder reports higher pass@1 metrics and line coverage via these iterative refinement cycles.

The judgment agent in MACT (Yu et al., 5 Aug 2025) specializes in error checking, flagging mistakes for re-routing, distinguishing itself from monolithic, end-to-end VLM approaches by providing robust self-correction through agent-level verification and feedback.

5. Data Augmentation, Supervision, and Reward Modeling

Several frameworks leverage scalable data engines to generate large image–code pairs (e.g., ScreenCoder (Jiang et al., 30 Jul 2025): 50,000 UI screenshots paired with code). Such datasets enable supervised fine-tuning and reinforcement learning, with composite rewards designed to reflect both semantic fidelity and visual accuracy. Block-matching rewards, text similarity measures, and position alignment penalties are used to optimize model behavior in code synthesis and spatial layout tasks.

MACT (Yu et al., 5 Aug 2025) implements a mixed reward modeling approach:

$$R_{\text{total}} = \alpha \cdot (\text{Local Agent Reward}) + \beta \cdot (\text{Global Outcome Reward})$$

balancing immediate step-wise agent optimization with an overarching objective for collaborative accuracy. Test-time scaling strategies (agent-wise parallelism, sequential selection, budget forcing) maximize the likelihood of correct outputs without excessive parameter scaling, showing competitive results on long-context and complex reasoning tasks.

6. Applications and Benchmarks

Modular multi-agent vision-to-code frameworks are deployed in a variety of domains:

• UI Automation and Front-End Code Generation: ScreenCoder (Jiang et al., 30 Jul 2025) achieves state-of-the-art metrics in block matching, structural coherence, and code correctness for web UI image-to-HTML/CSS synthesis.
• Autonomous Robotics and Mission Planning: Frameworks such as Being-0 (Yuan et al., 16 Mar 2025) and UAV-CodeAgents (Sautenkov et al., 12 May 2025) demonstrate closed-loop integration of perception, planning, skill execution, and feedback for complex navigation, manipulation, and UAV trajectory design—achieving high success rates (e.g., 84.4% for complex embodied tasks, 93% for UAV mission scenarios).
• Code Generation and Validation: AgentCoder (Huang et al., 2023) achieves 96.3% (HumanEval) and 91.8% (MBPP) pass@1 accuracy, outperforming non-modular prompt engineering techniques, with efficient token overhead.
• Workflow Automation for Non-Experts: AIAP (An et al., 4 Aug 2025) uses query decomposition, entity extraction, and plan refinement to generate modular, interpretable workflows from natural language, reducing workload and boosting usability among non-programmers.
• Visual Document Understanding: MACT (Yu et al., 5 Aug 2025) stands out in document-based VQA tasks under long visual contexts, placing in the top three on 13 out of 15 evaluated benchmarks.

7. Technical Challenges, Limitations, and Future Directions

Despite their advantages, several challenges persist. Asynchronous agent collaboration requires strategies for managing state immutability, avoiding negative transfer, and synchronizing shared system states without excessive overhead (Gesmundo, 2022). Modularity can fragment context, requiring standardized interfaces, robust data pipelines, and error tracing mechanisms.

Test-time scaling and agent-level competition must be carefully balanced to avoid resource waste or degenerate solutions; mixed reward models address this by aligning local agent incentives to global objectives (Yu et al., 5 Aug 2025). In real-time systems, trade-offs between central (cloud-based) and distributed (onboard) computation impact efficiency and latency (e.g., Being-0’s cloud/FM split (Yuan et al., 16 Mar 2025)).

A plausible implication is that future research will emphasize further modularization (e.g., plug-and-play agent pools), improved cross-agent communication protocols, adaptive reward assignment, and integration with domain-specific symbolic tools (geometry, logic, or API libraries) to extend the scope of vision-to-code capabilities. Additionally, open-source releases and synthetic data generation, as seen in ScreenCoder and UAV-CodeAgents, will accelerate reproducibility and wider adoption.

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In summary, Modular Multi-Agent Vision-to-Code Frameworks are characterized by decomposing vision-to-code tasks into specialized, interacting agent modules—with each module optimized for perception, reasoning, code generation, or validation—employing structured communication protocols, iterative feedback, and domain-informed priors. This modular approach yields scalable, interpretable, and efficient solutions across a spectrum of applications, as evidenced by empirical results on robotics, UI automation, document analysis, autonomous vehicles, and general workflow synthesis (Yoon et al., 2018, Gesmundo, 2022, Huang et al., 2023, Sharma et al., 17 Oct 2024, Zhao et al., 25 Oct 2024, Mahmud et al., 24 Jan 2025, Wang et al., 9 Mar 2025, Yuan et al., 16 Mar 2025, Kugo et al., 25 Apr 2025, Sautenkov et al., 12 May 2025, Jiang et al., 30 Jul 2025, An et al., 4 Aug 2025, Yu et al., 5 Aug 2025).