AI RESEARCH
COMI: Coarse-to-fine Context Compression via Marginal Information Gain
arXiv CS.CL
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ArXi:2602.01719v3 Announce Type: replace Large Language Models (LLMs) have nstrated exceptional capabilities across diverse tasks. However, their deployment in long context scenarios remains hindered by computational inefficiency and information redundancy. Context compression methods address these challenges by significantly reducing input length and eliminating redundancy. We propose COMI, a coarse-to-fine adaptive context compression framework that jointly optimizes for semantic relevance and diversity under high compression rates. We