A paper proposes adaptive test-time scaling using a lightweight fuzzy controller that maps interpretable signals (estimated prompt complexity and model confidence) to a per-query sampling budget, assigning fewer samples to easier prompts and more to harder ones. Evaluated on question-answering and mathematical reasoning tasks under a fair-alignment protocol, it improves over fixed-budget baselines while reducing average samples.
Test-time scaling improves LLM reasoning by generating multiple candidate answers, but fixed per-query budgets waste compute on easy prompts and lack explainability. This work introduces an adaptive fuzzy controller that uses interpretable signals like prompt complexity and model confidence to dynamically allocate sampling budgets, making inference-time compute inspectable. Experiments show it outperforms standard baselines and approaches a full-budget control with fewer samples.
The fuzzy controller provides a transparent mechanism for compute allocation, contrasting with black-box adaptive methods. Its reliance on interpretable signals (complexity, confidence) could enable debugging and trust in deployment. The fair-alignment protocol ensures controlled comparisons, highlighting the efficiency gains from dynamic budgeting.
Adaptive sampling could reduce inference costs for LLM providers by avoiding over-sampling on simple queries, while maintaining accuracy on complex ones. The interpretability aspect may appeal to enterprises needing auditable AI decisions. Next signals include adoption in production LLM APIs or integration with existing test-time scaling frameworks.
Reduces inference compute costs without sacrificing quality, directly improving margins for LLM-based services. Interpretability aids compliance and user trust, potentially unlocking regulated industry applications.
If validated across diverse tasks and models, this approach could become a standard component of cost-efficient LLM serving. Future work may extend the controller to multi-step reasoning or agentic workflows. Watch for open-source implementations or benchmarks comparing adaptive methods.