6 papers
Notes to Self: Can LLMs Benefit from Experiential Abstractions?
Chang Liu, Xinyu Li, Artur Dubrawski
Humans distill experience into reusable abstractions, e.g., strategies and cautionary reminders, and apply them to gradually solve problems more effectively. We study whether Large…
FAVA: Formal Authorization for Verified Agents with Evidence-Backed Permission Graphs
Yifan Zhang, Xinkui Zhao, Sai Liu +3
Large language model (LLM) agents autonomously interleave semantic reasoning with complex system operations. In these dynamic environments, static tool-level permissions are fundam…
Towards Practical Multimodal Hospital Outbreak Detection
Chang Liu, Jieshi Chen, Alexander J. Sundermann +5
Rapid identification of outbreaks in hospitals is essential for controlling pathogens with epidemic potential. Although whole genome sequencing (WGS) remains the gold standard in o…
ProMAS: Proactive Error Forecasting for Multi-Agent Systems Using Markov Transition Dynamics
Xinkui Zhao, Sai Liu, Yifan Zhang +4
The integration of Large Language Models into Multi-Agent Systems (MAS) has enabled the so-lution of complex, long-horizon tasks through collaborative reasoning. However, this coll…
MUSE: A Heterogeneity-Aware Multimedia Search Engine for Mobile SoCs
Xinkui Zhao, Qingyu Ma, Yifan Zhang +6
On-device multimedia retrieval is vital for smartphones, enabling applications like cross-modal semantic search and multimodal personal AI agents. However, realizing efficient retr…
Multimodal Structure Preservation Learning
Chang Liu, Jieshi Chen, Lee H. Harrison +1
When selecting data to build machine learning models in practical applications, factors such as availability, acquisition cost, and discriminatory power are crucial considerations.…