Problem
Research questions and friction points this paper is trying to address.
veterinary disease screening
zero-shot classification
multimodal system
edge-cloud computing
vision-language model
VetClaw: An Edge-Cloud Multimodal Agentic System for Veterinary Disease Screening
🤖 AI Summary
This work addresses the limitations of static-image reliance, insufficient multimodal integration, and lack of systematic workflows in early veterinary disease screening by proposing an edge-cloud collaborative multimodal agent system. The system captures images via edge devices and fuses them with symptom-related text, leveraging a cloud-based vision-language model for zero-shot disease classification. It innovatively decouples agent interaction from workflow orchestration, enabling dynamic tool invocation, enforcement of safety protocols, failure recovery, and escalation of uncertain cases to human experts. Experimental results demonstrate that multimodal inputs significantly outperform unimodal image-based classification, and the overall system exhibits practical diagnostic assistance capabilities suitable for real-world deployment.
📝 Abstract
We present VetClaw, an edge-cloud multimodal agentic system for early veterinary disease screening. VetClaw uses a camera module as an edge sensing device and sends captured images, together with optional symptom descriptions, to a server-hosted vision-language model for zero-shot disease classification. The system separates agent interaction from workflow orchestration: OpenClaw provides scheduling, tool access, user interaction, and notification services on the edge device, while LangGraph manages the stateful screening workflow, including input validation, image transmission, model invocation, safety checks, conditional routing, failure handling, and structured logging. This design moves beyond static image classification by enabling the system to collect visual evidence, invoke external models, apply deterministic safety rules, and generate diagnostic-support alerts. Results show that image-only VLM prediction remains limited, whereas symptom-guided and multimodal inputs improve zero-shot classification performance. Thus, VetClaw transforms a static prediction model into a coordinated, safety-aware system that can use tools, manage workflows, handle failures, and escalate uncertain cases.
Innovation
Methods, ideas, or system contributions that make the work stand out.
edge-cloud system
multimodal agentic system
zero-shot classification
workflow orchestration
vision-language model
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