Reasoning and planning critically rely on a predictive dynamics model. In symbolic domains such as mathematics and code, large language models (LLMs) internalize transition rules during pretraining, allowing reinforcement learning or test-time scaling to effectively elicit and generalize their reasoning ability. Embodied decision making is fundamentally different: agents must reason from sparse visual evidence under partial observability, while coping with environment-specific dynamics and affordances not captured by language priors. Here we propose IMPLEMENT, a model-based reasoning framework that enables frozen LLMs to perform imaginative planning. A lightweight world model converts raw pixels into object-centric symbolic states amenable to language-based reasoning, and predicts their evolution under hypothetical actions. To address partial observability, we perform Monte Carlo state prediction via temperature sampling, enabling decision evaluation over multiple plausible futures. To support adaptation to unseen environments, we integrate Meta In-Context Learning, conditioning the world model on interaction history to continuously refine its predictions. At inference time, the LLM and world model form a tight co-reasoning loop: the LLM proposes candidate actions, the world model simulates future trajectories, and the LLM refines its decisions, effectively inducing an online policy iteration scheme. Extensive experiments in ALFWorld demonstrate consistent advantages over finetuning-based and strong test-time scaling approaches, validating IMPLEMENT as an effective framework for grounding language agents in visual embodied environments.

Knowledge graph (KG) completion adds new facts to a KG by making inferences from existing facts. Most existing methods ignore the time information and only learn from time-unknown fact triples. In dynamic environments that evolve over time, it is important and challenging for knowledge graph completion models to take into account the temporal aspects of facts. In this paper, we present a novel time-aware knowledge graph completion model that is able to predict links in a KG using both the existing facts and the temporal information of the facts. To incorporate the happening time of facts, we propose a time-aware KG embedding model using temporal order information among facts. To incorporate the valid time of facts, we propose a joint time-aware inference model based on Integer Linear Programming (ILP) using temporal consistencyinformationasconstraints. Wefurtherintegratetwomodelstomakefulluseofglobal temporal information. We empirically evaluate our models on time-aware KG completion task. Experimental results show that our time-aware models achieve the state-of-the-art on temporal facts consistently.