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Introduces SFL-MTSC, a structured aggregation framework for robust multi-intent spoken language understanding using LLM self-consistency at the semantic frame level, showing improved slot F1 and overall accuracy on the MAC-SLU benchmark.
Proposes BindingSubspace (BSU), a representation-level framework that isolates and attenuates intent-conditioned directions in end-to-end spoken language understanding models to prevent capability persistence, where suppressing an intent still allows slot generation under forced prefixes. The method reduces forced-prefix recoverability while preserving retained performance on SLU benchmarks.