SAMA-GEO: Semantic-Aware Multi-Agent Framework for Game Engine Optimization

Game engine optimization across multi-game platforms presents a challenging decision-making problem due to the heterogeneous and complex characteristics of game workloads, with significant variation across and within genres. While genre provides a useful high-level characterization of games, it alone is insufficient for accurate policy prediction, as substantial variation in gameplay characteristics and engine requirements exists even among games within the same genre. We ask: do semantic-aware multi-agent systems work for this problem? To enable this analysis, we introduce SAMA-GEO, a benchmark dataset of 5,193 Roblox games spanning 17 genres and 59 genre-subgenre pairs, constructed using publicly available Roblox APIs. The dataset features a gold set built through manual play-testing and expert annotation, and a larger silver set with gold-referenced annotations, with each game annotated with 10 semantic labels capturing gameplay characteristics and 3 policy labels governing engine configuration. We formulate game engine optimization on Roblox as semantic-aware policy prediction, where game-level semantic understanding provides an explicit intermediate signal for predicting engine policies, bridging the gap that genre alone cannot close. To investigate this formulation, we propose a Semantic-Aware Multi-Agent framework that decomposes semantic and policy reasoning across specialized agents, representing the first application of multi-agent reasoning to game engine optimization. To measure performance, we develop an eight-metric evaluation suite spanning semantic and policy prediction, combining established, adapted, and novel metrics that jointly capture pointwise accuracy, structural preservation, and constraint adherence. Component-wise analysis shows consistent and complementary gains as semantic and policy reasoning capabilities are incorporated, supporting an affirmative answer to our central question.