Lookahead World Models: A Unified Framework for Predictive World-State Simulation, Counterfactual Planning, Action Selection, and Long-Horizon Environment Control

World models aim to represent and predict how environments evolve over time. They can support planning, simulation, robotics, video prediction, autonomous systems, scientific discovery, and interactive agents. However, predicting a plausible future state does not, by itself, determine which action an agent should execute. An agent may need to compare several possible actions, forecast their downstream consequences, account for uncertainty, evaluate long-term objectives, and determine whether additional prediction or simulation is worth its computational cost. This paper proposes Lookahead World Models (LWM), a unified research framework for evaluating alternative future trajectories before selecting actions in dynamic environments. The framework represents the current environment state, generates candidate actions or action sequences, predicts their possible consequences using a learned or hybrid world model, evaluates the predicted outcomes, and selects actions subject to task objectives, uncertainty, computational budgets, and operational constraints. The framework accommodates learned latent dynamics, visual prediction, physical simulation, counterfactual forecasting, model-predictive control, multi-agent interaction, and adaptive simulation horizons. To support systematic research development, this paper introduces a modular architecture, a standardized research-slot registry, an experimental methodology, and 300 candidate research directions organized into fifteen categories. Each direction receives a persistent identifier and can be expanded into a formal research question, implementation, controlled experiment, and replication record. The proposed directions span physical environments, robotics, autonomous navigation, video, three-dimensional worlds, digital twins, scientific experimentation, multi-agent systems, uncertainty estimation, and world-model training. The framework is a research proposal rather than a claim of demonstrated superiority. Its central hypothesis is that explicit prediction and comparison of alternative future trajectories can improve selected planning decisions when the world model is sufficiently accurate and the benefits justify the additional computation. Individual research directions require literature review, implementation, controlled evaluation, and independent validation.