MemRL separates stable reasoning from dynamic memory, giving AI agents continual learning abilities without model fine-tuning ...
The development of humans and other animals unfolds gradually over time, with cells taking on specific roles and functions ...
A new learning paradigm developed by University College London (UCL) and Huawei Noah’s Ark Lab enables large language model (LLM) agents to dynamically adapt to their environment without fine-tuning ...
Physical learning environments (PLEs)—including classrooms, schools, and networks of facilities—play a critical role in shaping educational outcomes. The World Bank’s RIGHT+ framework offers guidance ...
Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...
The model is projected to influence learning ecosystems across more than 17,000 universities globally as education systems ...
A new study presents a zero-shot learning (ZSL) framework for maize cob phenotyping, enabling the extraction of geometric traits and estimation of yields in both laboratory and field settings without ...
Crowdsourcing efficiently delegates tasks to crowd workers for labeling, though their varying expertise can lead to errors. A key task is estimating worker expertise to infer true labels. However, the ...
A deep learning framework combines convolutional and bidirectional recurrent networks to improve protein function prediction from genomic sequences. By automating feature extraction and capturing long ...
Gas sensing material screening faces challenges due to costly trial-and-error methods and the complexity of multi-parameter ...
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