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Jiangchuan Liu

5 accepted papers

2026

Sustainable Intelligence for the Wild: Democratizing Ecological Monitoring via Knowledge-Adaptive Edge Expert Agents

IJCAI 2026

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

Cited by 0Scholar
2025

Crucible: Quantifying the Potential of Control Algorithms through LLM Agents

NeurIPS 2025poster

Control algorithms in production environments typically require domain experts to tune their parameters and logic for specific scenarios. However, existing research predominantly focuses on algorithmic performance under ideal or default configurations, overlooking the critical aspect of Tuning Poten…

Cited by 0SourcecodeScholar
2025

Exploring Multimodal Foundation AI and Expert-in-the-Loop for Sustainable Management of Wild Salmon Fisheries in Indigenous Rivers

IJCAI 2025

Wild salmon are essential to the ecological, economic, and cultural sustainability of the North Pacific Rim. Yet climate variability, habitat loss, and data limitations in remote ecosystems that lack basic infrastructure support pose significant challenges to effective fisheries management. This pro

Cited by 0SourcePDFScholar
2025

Generative AI for Immersive Video: Recent Advances and Future Opportunities

IJCAI 2025

Immersive video serves as a key component of eXtended Reality (XR) that aims to create and interact with simulated virtual or hybrid environments. Such a technology allows users to experience immersive sensations that transcend time and space, and meanwhile continuously providing training data for e

Cited by 0SourcePDFScholar
2021

Personalized Cross-Silo Federated Learning on Non-IID Data

AAAI 2021technical

Non-IID data present a tough challenge for federated learning. In this paper, we explore a novel idea of facilitating pairwise collaborations between clients with similar data. We propose FedAMP, a new method employing federated attentive message passing to facilitate similar clients to collaborate…

Cited by 744SourcePDFScholar