Evidence Alignment and Transfer Boundaries in Latent Policy Optimization And Sequential And Multi-Behavior Recommendation

Abstract

Research spanning latent policy optimization and sequential and multi-behavior recommendation increasingly joins methods that were developed for different objects and decisions. Here, iterative information-bottleneck control of latent policy optimization is compared with dissipative Hamiltonian spectral-temporal dynamics for sequential recommendation to determine which claims can travel across those boundaries and which remain context dependent. Two target papers are triangulated against 12 locally validated publications. The comparison follows latent representations, mutual information, policy updates, reward alignment, training stability and deliberately separates mechanistic interpretation from performance ranking, because the latter can conceal incompatible experimental or operational conditions. The synthesis shows that latent representations cannot be interpreted independently of mutual information, while policy updates determines whether an apparent improvement remains meaningful outside the original setting. The strongest claims are therefore those that expose sensitivity, failure conditions, and residual uncertainty. On this basis, the review proposes an auditable pathway from focal mechanism to application claim, with explicit checkpoints for calibration, external validity, and responsible interpretation.