A net-value-per-byte curator governs memory lifecycle in on-device LLM agents, cutting memory 2.7x and uplink 2.4x while driving injection success to zero on task-drift benchmarks and Jetson hardware.
SPRInG: Continual LLM Personalization via Selective Parametric Adaptation and Retrieval-Interpolated Generation,
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.LG 2years
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Position paper advocating personalized preference learning in LLMs over aggregated approaches, grounded in social choice theory and demographic variation.
citing papers explorer
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Forget to Improve: On-Device LLM-Agent Continual Learning via Budget-Curated Memory
A net-value-per-byte curator governs memory lifecycle in on-device LLM agents, cutting memory 2.7x and uplink 2.4x while driving injection success to zero on task-drift benchmarks and Jetson hardware.
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Large Language Models Should Learn Personalized Rather Than Aggregated Human Preferences
Position paper advocating personalized preference learning in LLMs over aggregated approaches, grounded in social choice theory and demographic variation.