PulseBound: Future-Beat State Forecasting Under an Explicit Information Boundary

Predictive representation learning from photoplethysmography (PPG) has a subtle failure mode: causal attention does not guarantee one-sided information access when normalization, nonlocal transforms, or companion views can depend on withheld samples. We introduce PulseBound, a PPG representation learner that couples physiologically structured future-beat prediction with an explicit stored-window information boundary. A content-independent cutoff separates a visible prefix from the prediction target; prefix-only normalization, suffix replacement before derived-view construction, and aligned masking make every encoder input a function of the visible prefix and cutoff alone. This yields stored-suffix invariance: for fixed model state, randomness, prefix, and cutoff, changing the stored suffix cannot change the forecast context. Within this contract, a shared horizon-conditioned head predicts nine deterministic rhythm and morphology descriptors for up to four extractor-valid future beats, with elementwise validity masks and optional ECG-derived pulse-arrival-time supervision confined to training.On groups held out from PulseBound backbone pretraining within MIMIC and VitalDB, PulseBound reduces nine-state transformed-space MAE relative to last-visible-beat persistence by 28.06% and 22.22%, respectively, and improves MAE, MAE-Skill, and Spearman correlation in all 40 source–cutoff–horizon cells. In a separate seven-model comparison across 13 downstream tasks, PulseBound attains the best mean on nine frozen linear-probe tasks and seven full-fine-tuning tasks. Stored-suffix interventions produce zero recorded change in forecast contexts or predictions, with zero suffix-input gradient at audited precision under the declared stored-window interface. Together, these results frame predictive physiological representation learning around three separately testable choices: what information is accessible, what future structure is supervised, and how the learned representation transfers.