# Parallel Predictive World Models for Accurate and Efficient Long-Horizon Planning

arXiv: https://arxiv.org/abs/2610.08627 · HTML: https://mlsanity.com/paper/2610.08627/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Latent predictive world models, one of 11 directions in World models.
- Builds on 12 of its 23 references that are in the map.
- First posted on arXiv 6 Oct 2026.

## Placed in

- World models / [Latent predictive world models](https://mlsanity.com/wm/latent-predictive-world-models/)

## Builds on (most cited first, 12 of 12)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Temporal Difference Learning for Model Predictive Control](https://mlsanity.com/paper/2203.04955/) (2022)
- [DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning](https://mlsanity.com/paper/2411.04983/) (2024)
- [Transformers are Sample-Efficient World Models](https://mlsanity.com/paper/2209.00588/) (2022)
- [LeWorldModel: Stable End-to-End Joint-Embedding Predictive Architecture from Pixels](https://mlsanity.com/paper/2603.19312/) (2026)
- [Fast LeWorldModel](https://mlsanity.com/paper/2606.26217/) (2026)
- [Parallel Stochastic Gradient-Based Planning for World Models](https://mlsanity.com/paper/2602.00475/) (2026)
- [Latent Energy Action Planning with World Models](https://mlsanity.com/paper/2609.03294/) (2026)
- [Grounding Generated Videos in Feasible Plans via World Models](https://mlsanity.com/paper/2602.01960/) (2026)
- [Traj-LeWM: Path-Aware World-Model Planning via Latent Trajectory Cost](https://mlsanity.com/paper/2608.14125/) (2026)
- [Koopman-BoxQP: Solving Large-Scale NMPC at kHz Rates](https://mlsanity.com/paper/2602.18331/) (2026)
- [Statistical Efficiency of Single- and Multi-step Models for Forecasting and Control](https://mlsanity.com/paper/2603.23465/) (2026)

## Abstract (arXiv)

Long-horizon world-model planning typically relies on autoregressive rollouts, where predicted states are repeatedly fed back into the model. This preserves temporal structure but creates a horizon-length sequential path and exposes later predictions to recursive decoded-state feedback. We introduce Parallel Predictive World Models (PPWM), which predict a finite-horizon trajectory in parallel while retaining causal interaction among future representations. Each horizon is conditioned on its causal action prefix, and future representations interact before decoding, separating temporal causality from state-by-state output recursion. We formalize this distinction by viewing autoregressive rollout as a causal trajectory map and identifying the decoded-state feedback pathway removed by PPWM. Across four visual-control tasks, PPWM achieves the lowest long-horizon prediction error and the highest Cross-Entropy Method (CEM) simulator success among the evaluated predictive interfaces. Meanwhile, PPWM achieves more than a 3× average CEM planning speedup over the autoregressive LeWM baseline. These results suggest that accurate and efficient long-horizon world-model planning does not require state-by-state autoregression, but can instead be achieved through parallel causal trajectory prediction.