LSRB-REC: SOURCE-SPECIFIC DECISION RIGHTS FOR SEQUENTIAL RECOMMENDATION

Sequential recommenders commonly fuse heterogeneous signals into one globally editable score field, granting every source authority over every cutoff. This conflates predictive value with permission to alter a decision: evidence useful at a deeper candidate frontier can disturb an already reliable shallow ranking. We frame adaptation instead as source-specific decision rights, controlling how evidence evolves, what information it contributes, and where it may alter the list. The Language-Semantic Ranking Barrier for Recommendation (LSRB-REC) treats a ranking as nested decisions. A frozen behavioral anchor establishes the initial order; contractive collaborative transport discovers context-dependent candidates at a primary frontier; and language supplies anchor-orthogonal innovation only beyond a protected boundary. This construction turns heterogeneous evidence into staged, auditable adaptation. We prove transient stability, invariance to anchor-aligned semantics, exact preservation of the completed prefix, and a finite outer intervention bound. Across five full-catalog benchmarks, LSRB-REC improves every mean HR, NDCG, and MRR metric over its matched anchor and wins 18 of 20 protocol-matched state-of-the-art comparisons. Controls preserve all nine protected cutoffs, rank the contractive forecast first in all 15 matched cells with 0.71 state amplification, and rank the orthogonal residual first across three datasets; three language encoders maintain the improvement direction.