Survey on Argus Framework for Vision Task Generalization

Abstract

Argus presents a modular and scalable framework designed to efficiently manage a broad spectrum of computer vision tasks through specialized expert models. At the core, the system employs Multimodal Large Language Models (MLLMs) as instruction-driven routers that intelligently delegate tasks such as image generation, object detection, video analysis, and 3D transformation. Unlike conventional monolithic approaches that struggle with task diversity[1], Argus enables flexible and optimized task handling by adopting a controlled, modular architecture. The framework is trained using supervised learning for vision-language tasks and further refined through reinforcement learning[2], improving routing strategies and overall execution. Task-specific routing tokens are incorporated to support multi-step workflows, allowing sequential task execution from a single user instruction. Key machine learning methods, including the Adam optimizer for efficient convergence and cross-entropy loss for accurate task-specific optimization, form the foundation of the training process. The architecture also allows seamless integration of new expert models, ensuring adaptability, scalability, and computational efficiency. With its modular design, Argus provides a robust and future-ready solution that can evolve alongside advancements in the computer vision domain.

Introduction

Recent progress in computer vision and multimodal AI highlights a shift from monolithic models toward modular systems that combine specialized expert models with a central multimodal large language model (MLLM) controller. Instead of one all-purpose model handling all tasks, the new paradigm treats the MLLM as an intelligent router that interprets user instructions and delegates subtasks to appropriate specialist vision models. This design addresses scalability, efficiency, and adaptability challenges faced by unified models, which are computationally expensive and brittle.

Argus exemplifies this modular, instruction-driven approach. It uses a trained MLLM controller that outputs structured routing commands to select and coordinate external expert models for diverse vision tasks like image synthesis, video processing, and 3D reconstruction. Argus’s training pipeline combines supervised instruction tuning with reinforcement learning (RL), allowing the controller to refine routing decisions based on task success, latency, and operational constraints. The system supports multi-step workflows through sequential routing and task tokens, enabling complex user requests to be executed in order.

Compared to prior frameworks such as Olympus and HuggingGPT, Argus improves adaptability by incorporating RL, which optimizes routing beyond supervised examples to handle ambiguous cases and real-world variability. Its modularity enables easy integration or replacement of specialists without retraining the entire model, making it scalable and future-proof. Experimental results show Argus achieves high routing accuracy, efficient multi-task handling, and better user satisfaction, balancing performance with computational cost.

Conclusion

The experimental evidence and qualitative evaluations reported in this work indicate that learned routing improves the correctness of chained actions and reduces cascading failures relative to prompt-only orchestration. Reinforcement learning in particular helps the controller favor routings that yield better downstream outputs and better trade off quality against latency or compute cost. At the same time, Argus’s orchestration layer, prompt templates, and fallback strategies contribute to operational reliability by handling specialist timeouts, rerouting, and human escalation when needed. These characteristics make Argus well suited to practical deployments where diverse vision tasks must be served reliably at scale. Despite these strengths, several important challenges remain. Success depends on the breadth and quality of the instruction corpus, careful reward engineering for RL, and the fidelity of specialist models; weaknesses in any of these areas can degrade overall performance. Ethical and safety concerns also require attention—automated routing should include audit trails, content filtering, and human review for sensitive requests. Future work should focus on standardized chain-of-action benchmarks, more robust few-shot adapter methods for hot-swapping experts, and deployment-aware policies that optimize for latency, cost, and fairness. In summary, Argus offers a practical pathway toward flexible, maintainable, and high-quality multimodal systems by combining learned instruction understanding with modular execution. Rather than trying to make one model do everything, Argus shows that smart orchestration plus targeted specialization can achieve scalable capability while remaining adaptable to rapid advances in computer vision and multimodal research.

References

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Copyright

Copyright © 2025 Suhas G N, Dr. Kamalakshi Naganna, Saransh Jaiswal , Hemanth H M, Jagadish M. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.