NUS researchers' MRAgent framework reduces LLM agent memory retrieval to 118K tokens per query — vs. 3.26M for LangMem — using step-by-step reasoning.
A KAIST research team has developed a new technology that enables to process a large-scale graph algorithm without storing the graph in the main memory or on disks. Named as T-GPS (Trillion-scale ...
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New framework reduces memory usage and boosts energy efficiency for large-scale AI graph analysis
BingoCGN, a scalable and efficient graph neural network accelerator that enables inference of real-time, large-scale graphs through graph partitioning, has been developed by researchers at the ...
A research team has developed a new technology that enables to process a large-scale graph algorithm without storing the graph in the main memory or on disks. A KAIST research team has developed a new ...
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