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SMC is a class of inference algorithms based on sequential importance sampling, originally used for Bayesian filtering and state-space models, and later introduced into the field of natural language generation.
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10/30/2026
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Related Claims
UnverifiedThe design of SMC is inspired by Speculative Decoding technology and elevates it from the token level to the action-chain level.70% similarVerifiedMamba 等基于状态空间模型（SSM）的架构以线性复杂度实现序列建模，更适合实时推理63% similarUnverifiedThe study proposes using sequential Monte Carlo (SMC) methods to guide a small model's dialogue generation toward grammar goals set by an expert.62% similarUnverifiedDuring generation, SMC maintains a set of weighted candidate sequences (particles), reweighting and resampling them at each step according to the potential functions.61% similarUnverifiedTypical representatives of state space models include S4, Mamba and others, which write the recurrence as a continuous differential equation or discrete linear recurrence and achieve efficient parallel training with the help of convolution or selective scan.60% similar
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