SIDInspector: Diagnostic toolkit exposes hidden flaws in Semantic-ID tokenizer mappings

SIDInspector addresses a critical gap in generative recommendation systems: Semantic-ID (SID) tokenizers are increasingly reused as standalone artifacts, but until now there was no common interface to inspect exported item-to-code mappings for issues like coverage gaps, aliasing, weak prefixes, and tail compression. The tool defines a small adapter contract over item mappings, metadata, interactions, and optional generator traces, then validates the contract and outputs mapping-level probes covering utilization, aliasing, neighborhood alignment, popularity allocation, and structural cost. The released resource covers four tokenizer artifact lines—a GRID/RQ-KMeans-style and ReSID/GAOQ comparison on 23,742 Musical items, plus LETTER and LC-Rec item-index artifacts.

In the Musical contrast, the GRID-style export had 3,749 unique full codes with a staggering 97.7% full-code aliasing rate, while ReSID/GAOQ was completely alias-free. Yet the most striking finding was that the strongest prefix–co-occurrence alignment came from a deterministic category-prefix control (0.447), not from either learned export row (0.154 and 0.055–0.080). This demonstrates that addressability and behaviorally meaningful prefixes must be inspected separately. Cross-domain, fixed-reranker, and mechanism-probe checks confirm that prefix alignment is a candidate-exposure signal, while final ranking quality remains a downstream model question. SIDInspector thus provides inspectable artifact profiles before leaderboard scores, saving researchers from discovering tokenizer flaws only after expensive downstream training.