Unverified50% confidenceSolutionExact time
Probabilistic Focal Search (PFS) follows the heuristic-guided selection of FS with probability p, and with probability 1-p expands the node with the minimum f value in the OPEN list.
1
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50%
Confidence
Long-term
Relevance
9/12/2026
First Seen
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Related Claims
UnverifiedThe cost of adapting PFS is low, requiring only the introduction of a probabilistic branch in the existing search loop, which can yield order-of-magnitude efficiency gains in specific scenarios.77% similarUnverifiedTop-p (nucleus sampling) limits the candidate token range by dynamically truncating probability mass, and has better adaptability than fixed-truncation Top-k69% similarUnverifiedThe benefit of PFS is notably smaller on the pancake sorting problem, because deterministic search already progresses efficiently in that domain and FOCAL inclusion is not a bottleneck.67% similarUnverifiedCoze's Top-P nucleus sampling mechanism starts from the highest-probability words and accumulates probabilities until the cumulative probability reaches the set P value, sampling only from those words.67% similarUnverifiedTemperature controls the flatness of the sampling distribution; a higher value tends toward selecting lower-probability words, while Top-p dynamically narrows the candidate vocabulary set through a cumulative probability threshold.66% similar
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