Plan Once, Predict All: Latent Planning for Non-Autoregressive User Behavior Forecasting

Modeling future user behavior trajectories is crucial for capturing evolving preferences and enabling proactive recommendations. However, most sequential recommenders either focus on next-item prediction or extend to multi-step forecasting via autoregressive rollout, which often incurs error accumulation and high inference cost. To alleviate these problems, we propose LapTop, a latent planning framework for user behavior trajectory prediction that generates multi-step actions through parallel trajectory decoding without autoregressive rollout. LapTop introduces a compact set of latent plan tokens that operate in the hidden space to encode long-term intent and global preference structure, together with learnable future query slots that decode all future steps simultaneously. To capture the inherent uncertainty of user behaviors, LapTop adopts a variational learning scheme that aligns a history-conditioned prior with a future-aware posterior during training, while using only the prior at inference time. This design enables diverse and coherent trajectory generation without iterative decoding. Experiments on real-world benchmarks demonstrate that LapTop consistently outperforms state-of-the-art baselines in multi-step accuracy and trajectory quality, while also reducing inference latency.