Sustainable Intelligence for the Wild: Democratizing Ecological Monitoring via Knowledge-Adaptive Edge Expert Agents
Quick Answer
This paper shows that This research introduces a knowledge-adaptive architecture for ecological monitoring, separating visual perception from reasoning to enhance AI performance in diverse environments.
Quick Take
This research introduces a knowledge-adaptive architecture for ecological monitoring, separating visual perception from reasoning to enhance AI performance in diverse environments. By utilizing a dynamic knowledge base, it reduces reliance on cloud resources, making it suitable for remote deployments. Collaboration with biologists and Indigenous communities promotes ethical AI practices in ecosystem management.
Key Points
- Proposes a shift from model adaptation to knowledge adaptation for ecological monitoring.
- Introduces a dynamic knowledge base to enhance AI performance in variable environments.
- Reduces reliance on cloud resources, enabling effective remote deployments.
- Supports knowledge sustainability by preserving expert insights in structured forms.
- Advances ethical AI co-development through collaboration with biologists and Indigenous communities.
Paper Resources
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~2 min readAbstract:Rapid biodiversity loss underscore the urgency of effective monitoring, yet manual surveys remain resource-intensive. While on-device AI offers a scalable alternative, its performance in the wild is often challenged by environmental variability. Current methods rely heavily on cloud resource, which requires continuous uploading of field data for model retraining. This approach is unsuitable for remote deployments because it consumes limited power and network connectivity. To address these constraints, this research proposes a shift from model adaptation to knowledge adaptation. We introduce an architecture that separates visual perception from reasoning, combining a visual encoder with a dynamic knowledge base. We uses an explicit knowledge base to replace implicitly encoding expert knowledge into model parameters. This method also supports knowledge sustainability by preserving expert insights in a structured form. Through cross-disciplinary collaboration with biologists and Indigenous communities, this work advances ethical AI co-development, fostering responsible and culturally informed ecosystem management.
| Comments: | 10 pages |
| Subjects: | Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.16671 [cs.AI] |
| (or arXiv:2605.16671v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.16671 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jiaxing Li [view email]
[v1]
Fri, 15 May 2026 22:12:02 UTC (5,967 KB)
— Originally published at arxiv.org
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