LOTUS-UCC: UNITARY SEMANTIC COCYCLES FOR SINGLE-MODEL SEQUENTIAL RECOMMENDATION

LLM-enhanced recommendation has improved semantic representation, but largely leaves semantic evolution unspecified: knowing what an item means does not explain how an interaction moves intent or when that motion changes a ranking decision. We introduce LOTUS-UCC (Language-Oriented Transport with Unitary Semantic Cocycles), a single-model semantics-to-decision dynamical system in which language defines the coordinate space of user intent. Causal spherical forecasting and persistent particle transport maintain multiple latent semantic states; a unitary cocycle carries the resulting representation without distorting its geometry; and realification produces one dense full-catalog score. Our analysis follows perturbations through the complete chain from semantic history and persistent state to particles, semantic endpoints, catalog scores, and Top-K decisions. It also quantifies the curvature error of Euclidean updates and proves that CNDB upper-controls cutoff failure risk. Under chronological leave-one-out and exhaustive ranking, LOTUS-UCC establishes new best results on 19 of 20 metrics across Beauty, Sports, Toys, Yelp, and Online Retail, with a 25.08% average relative gain over the strongest published result in each setting. Structural ablations provide evidence for the geometric and decision-aware mechanisms, and the proposed dynamics generalize consistently across four language representation spaces.