EEFOLLM - LLM-guided reward shaping for electric eel foraging optimization in robot path planning: From Benchmark Results to External Validity

Abstract

Benchmark performance is useful only when the evaluation setting represents the populations and operating conditions to which the result will be transferred. This structured evidence review evaluates "EEFOLLM: LLM-guided reward shaping for electric eel foraging optimization in robot path planning" alongside nine author-disjoint, topically matched publications in robotic path optimization. It compares construct definitions, evaluation choices, operating assumptions, and reported limitations instead of treating bibliographic similarity as empirical equivalence. Viewed through benchmark transfer and external validity, the map separates claims supported by the available record from questions that still require full-text extraction, replication, or new experiments. The synthesis is interpretive rather than meta-analytic and therefore does not present a pooled effect estimate or a new causal result. The resulting agenda prioritizes cross-site replication, explicit eligibility criteria, and reporting of performance across materially different settings.