Prompt-based spoken language understanding with large language models often suffers from inconsistent intent-slot structures due to decoding stochasticity, particularly in multi-intent scenarios. To address this, researchers propose Semantic Frame-Level Multi-Task Self-Consistency (SFL-MTSC), a novel structured aggregation framework operating at the semantic frame level. Instead of relying on output-level majority voting, SFL-MTSC decomposes predictions into intent-specific frames and applies domain-intent grouping alongside slot-level clustering. The framework evaluates cluster reliability using path support scoring to determine which frames are trustworthy. Reliable frames are retained and re-integrated to form the final prediction, ensuring greater structural consistency. Zero-shot experiments on the MAC-SLU benchmark dataset demonstrate improved slot F1 scores and overall accuracy compared to single-path inference. Intent accuracy remains largely stable across most settings while achieving these gains in slot-level performance.