FutureWay: Adaptive Future-Informed Waypoints for Long-Horizon Diffusion Prediction

Long-horizon diffusion prediction aims to forecast how information spreads on social networks, which requires resolving local ambiguity and correcting trajectory drift during autoregressive rollout. Yet existing methods primarily learn next-participant prediction on ground-truth histories, leaving them poorly equipped to correct from rollout errors. To address this challenge, we propose FutureWay, a framework that turns recorded futures into decision-specific guidance on model-generated histories. Future information can help distinguish locally plausible participants and correct decisions after rollout deviation, but the useful guidance horizon depends on the current diffusion state. During training, multi-horizon future-informed teachers provide complementary corrective signals. A prefix-only projection analysis characterizes the optimal approximation to future-informed guidance using observable history, motivating student-aware horizon weighting that balances corrections beyond a pretrained anchor against compatibility with the evolving student. Representation-aware on-policy self-distillation transfers this guidance into student decisions and representations. At inference, FutureWay uses only the current cascade prefix and social graph without future information. Experiments on three real-world benchmarks show consistent improvements in both long-horizon future-participant coverage and order-sensitive trajectory recovery, with an average relative gain of 10.56% over the strongest comparators.