In generative recommendation, everyone has assumed they know what makes a "good" semantic ID design. So what happens when you actually put that assumption to a large, controlled reproducibility test? 🔍 Generative recommenders turn items into discrete token sequences called semantic IDs, then generate the next item autoregressively. Designs like RQ-VAE, OPQ, and RQ-Kmeans have proliferated, but prior comparisons used different datasets and backbones, so nobody could say which design actually wins. So the authors reran 12 methods side by side under the exact same data splits and evaluation protocol, and the assumptions started falling apart. No single SID design dominates across datasets. Codebook utilization balance barely correlates with recommendation quality. Longer codes don't always help, and bigger backbones can even hurt performance. And when you measure how well an SID preserves an item's local semantic neighborhood, the winning method flips depending on whether you use Jaccard or RBO. The lesson from What Makes a Good Semantic ID for Generative Recommendation? A Reproducibility Study is that good SID design isn't about chasing one metric, it's about balancing several at once. URL: #RecSys# #Reproducibility#