Internal solitary wave (ISW) remote sensing requires task parameters and spatial information to be transferred across image acquisition, detection, stripe extraction, and propagation prediction. Here, we present ISW-Agent, which integrates large language model (LLM) interpretation with n8n workflows and independent sci...
Hao Zhang (First Institute of Oceanography, Ministry of Natural Resources, Qingdao et al.· 0 citations
Language-model agents can accumulate interaction feedback, but reliably turning that feedback into reusable experience remains challenging. Existing methods either optimize external experience scaffolds or train experience-related behavior on specific task distributions, leaving open whether experience learning itself...
Renhao Li, Xinjie Shen, Yang Su et al.· 0 citations
Large language models can produce fluent answers that the supplied context does not support. A compact frozen proxy model can detect such answers by reading the context and another model's response, even from a proprietary generator. Attention-based detectors use summary statistics of the proxy's attention weights, but...
Andrei Volodichev, Maksim Savichev, Nikita Sviridov et al.· 0 citations
ARTIFICIAL INTELLIGENCE (AI) has demonstrated its potential across a wide range of humanitarian tasks, offering scalable and data-driven solutions to some of the world's most pressing challenges. For instance, in addressing climate change, AI-powered models are being used for faster and more accurate global weather for...
Noora Al-Emadi, Muhammad Imran, Yin Yang et al.· 0 citations
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Medical calculators translate raw patient observations into clinically actionable values and scores. However, applying these calculators directly to free-form patient notes requires precise variable extraction combined with the exact execution of complex formulas, scoring rules, and output conventions. Relying solely o...
Yang Zhong, Ling Luo, Xuanlyu Zhu et al.· 0 citations
Ontologies are widely used to represent and organize complex knowledge in domains such as biomedicine and healthcare. However, constructing reliable ontologies remains challenging, as class hierarchies, whether manually or automatically constructed, may contain subtle subsumption errors that require careful refinement....
Large language models (LLMs) can reason over both structured measurements and free-text records, which makes them attractive for emergency department (ED) prediction. However, most existing LLMs still fall short of conventional ML methods like trees on EHR-based prediction tasks. Our controlled analysis traces this gap...
Pengfei Hu, Chenwei Wu, Qiao Jin et al.· 0 citations
Learning from interaction experience is essential for enabling large language models to continually improve during deployment. Context distillation offers a promising path: privileged information extracted from experience is provided to a teacher, whose behavior is then distilled into a student model that has no access...
Zhe-Zheng Hao, Yong-Kang Yang, Xia-Ling-Zhang-Yu-Xin Xia et al.· 0 citations
Scientific agents must connect research problems, methods, and findings dispersed through narrative articles. We introduce SPRA (Structured Paper Representation for Agents), a paper-level representation of persistent research objects and typed relations. Its empirical-AI profile organizes Problems, Contributions, Compo...
Yu-Tong Leng, Hao-Jiong Chen, Fu-Kai Shang et al.· 0 citations
Large language model (LLM) agents now fix software, operate computers and serve customers, but their harnesses, the programs that run them, call a large LLM at every step, which makes long tasks slow and costly. Most of these calls end in a multiple-choice decision, such as which tool to call next. Decision models such...
Kun-Lun Zhu, Tao Feng, Xue-Ning Han et al.· 0 citations
Jev-LiteMAS is introduced, a multi-agent framework that bridges both gaps by combining a lightweight decision model (Jev) with generative execution and feedback, and reduces generative-LLM tokens by 12.7-54.6% over LAS.
Kai-Xiang Wang, Zhai-He Zhou, Jiong Lou et al.· 0 citations
Long-term memory has become a crucial component of large language model (LLM) agents, with most research focusing on retrieving relevant information accurately and efficiently. However, a dependable memory system must also forget sensitive user data upon request. To evaluate this capability, we introduce MemCut, a syst...
Cheng Zhang, Xuan-Ning Cao, Zhi-Cheng Zheng et al.· 2 citations
