UNO distills user logs into semi-structured rules and preferences, applies query-and-feedback clustering to handle heterogeneity, quantifies cognitive gaps to filter noise, and builds primary and reflective modules that outperform RAG and memory baselines.
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CTT is a compression pipeline for LLMs that achieves up to 49x memory reduction, 10x faster inference, 81% lower CO2 emissions, and retains 68-98% accuracy on code clone detection, summarization, and generation tasks.
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Improve Large Language Model Systems with User Logs
UNO distills user logs into semi-structured rules and preferences, applies query-and-feedback clustering to handle heterogeneity, quantifies cognitive gaps to filter noise, and builds primary and reflective modules that outperform RAG and memory baselines.
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Carbon-Taxed Transformers: A Green Compression Pipeline for Overgrown Language Models
CTT is a compression pipeline for LLMs that achieves up to 49x memory reduction, 10x faster inference, 81% lower CO2 emissions, and retains 68-98% accuracy on code clone detection, summarization, and generation tasks.