Unverified50% confidenceFactExact time
The paper uses sequential Monte Carlo (SMC) to correct the error accumulation caused by particles deviating from the original unconditional marginal distribution after guidance, so that the sampler converges to the true target density as the number of particles goes to infinity.
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50%
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Long-term
Relevance
10/15/2026
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Related Claims
UnverifiedSequential annealing methods based on sequential Monte Carlo (SMC) are theoretically exact and can approximate the target distribution without bias, but require maintaining a large particle population, resulting in high computational overhead.72% similarUnverifiedMany existing SMC (Sequential Monte Carlo) inference-time sampling schemes rely on equal-weight resampling, which aggressively prunes low-weight trajectories and causes a sharp decline in lineage diversity of the search space.70% similarUnverifiedWhen guiding discrete diffusion models with sequence-level goals, enumerating all possible completion combinations leads to computation that grows exponentially with the number of unresolved positions.67% similarUnverifiedParticle filtering approximates a continuous probability distribution using a finite set of samples (particles) to represent the belief state.65% similarUnverifiedWhen the target distribution has finite support, the paper gives an explicit particle count threshold, beyond which no spurious local minima exist and every spurious stationary point is unstable.65% similar
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