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This paper proposes TamGraph, a target-aware memory graph method that dynamically selects relevant historical conversations using an entropy-guided mechanism to enhance LLM performance in conversational stance detection tasks.
TAPS proposes a target-aware prefix tree selection method for diffusion-drafted speculative decoding, achieving up to 7.9x lossless end-to-end speedup by improving the acceptance-cost tradeoff over prior methods.