budget-aware

BAP-SQL presents a budget-aware observation planning approach for agentic text-to-SQL, treating SQL query choice as a budget-control decision to improve tight-budget success while reducing token usage.

Proposes FedEAS, a budget-aware policy for synthetic data augmentation in federated learning that assigns each client an entropy-adaptive per-class generation budget, recovering most accuracy gains of full class balancing while reducing generation cost by 94.1%.

Introduces SEVRA, a selective verification controller for budget-aware reasoning that decides when to accept a model's initial answer versus spending extra compute on verification, improving accuracy and reducing unnecessary tokens on benchmarks like MATH500 and GSM8K.