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The results show that standard virtual-sensing models exhibit substantial tail degradation hidden by global MAE, that existing DIR methods can improve balanced performance but transfer unevenly to multimodal time-series data, and that tail-region instability remains a largely hidden failure mode under current DIR evalu...
This work introduces a suite of synthetic multi-objective test problems with analytically controlled Pareto fronts and deliberately uninformative objective values, designed to decouple algorithmic behaviour from problem structure, allowing bias induced purely by algorithmic operators and design choices to be observed.
It is found that neutral framing of behavioural features, reporting the feature value without prescriptive advice, consistently outperforms the prescriptive variants, and that prescriptive feedback steers the median feature value in the advised direction in only 37% of cases despite empirically grounded advice.
EvoHIIT, an LLM-assisted evolutionary framework for the design of High-Intensity Interval Training (HIIT) programs, is introduced, and preference-based selection mechanisms are studied to provide empirical insight into human-aligned evolution of natural language solutions.
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