TESA-REC: EVIDENCE–AUTHORITY SEPARATION FOR SEMANTIC SEQUENTIAL RECOMMENDATION

Semantic recommenders commonly allow an auxiliary source to modify the final score whenever that source provides useful information. This conflates two distinct objects: evidence quality and decision authority. We introduce an evidence–authority separation principle and instantiate it as Temporal Entropic Semantic Adaptation for Recommendation (TESA-REC), a zero-trainable-parameter interface around a frozen sequential anchor. The anchor defines a behaviorally admissible set and the finite rank capacity available for intervention. Within that set, temporally partitioned candidate evidence performs a minimum-information update of a recency prior, producing a candidate-conditioned Gibbs belief over temporal explanations. One explanation overtakes another only after crossing a prior-odds evidence barrier, while reversing an anchor preference requires a separate behavioral-gap threshold. A constrained assignment allocates the anchor’s existing rank slots according to the resulting priorities. We show universal optimality for nonincreasing position values, maximal invariance to monotone score calibration, exact rank-capacity conservation, and a deterministic global perturbation bound. Across five full-catalog benchmarks, every matched-anchor mean improves and TESA-REC wins all 20 protocol-matched comparisons with the listed baselines; 31 of 35 default metric gains remain significant after correction, and the direction transfers across three language encoders. Matched controls further show that constrained allocation consistently improves upon additive score fusion, while empirical rankings satisfy the predicted invariance and conservation laws exactly.