arXiv:2610.30014v1 Announce Type: new Abstract: LLM agents with similar success rates can follow different action sequences. An Agent Behavior Map represents LLM-harness combinations using centered a...
MAPF-World introduces an autoregressive action world model for multi‑agent path finding that predicts short‑horizon local observations and neighboring agents’ action intentions, enabling decisions beyond immediate local views. The model incorporates a spatio‑agent positional encoding within Transformer architectures to enhance spatial awareness and agent semantics, fostering coordinated behaviors. Experiments on diverse maps, including a new automated map generator based on real‑world urban layouts, show that MAPF‑World outperforms existing learned solvers, achieving strong performance, robust zero‑shot generalization, and high success rates even at high agent densities.
arXiv:2603. 28963v2 Announce Type: replace-cross Abstract: Simulation with realistic traffic agents is essential for validating autonomous driving systems.
The paper presents a reward‑free continual learning framework for space robots that uses latent‑state world models to adapt to severe hardware degradation. By pre‑training a model‑based agent in diverse simulations, the world model learns to predict reward structure in latent space. During deployment, the observation encoder and reward predictor are frozen while only the transition dynamics are updated via unsupervised rollouts, allowing the policy to adapt using imagined trajectories without new rewards.
The paper introduces a Latent World Model (LWM) for robot navigation that predicts action‑conditioned latent feature compatibility instead of reconstructing future observations. By exploiting the correlation between spatial proximity and latent feature similarity, the model evaluates action consequences directly in latent space and supports counterfactual training using sampled action sequences. The learned world model can supervise policy learning from unlabeled video and further improve policies via reinforcement learning entirely within the model, eliminating the need for action annotations and additional environment interaction.
World models enable agents to reason about future outcomes and learn policies from their knowledge of state transition, but existing approaches primarily focus on reconstructing future observations or...
arXiv:2602. 11619v2 Announce Type: replace Abstract: Running the same LLM agent on identical inputs yields 2.
arXiv:2605.17467v2 Announce Type: replace Abstract: Large language model-driven multi-agent systems (LLM-MAS) excel at complex tasks, yet unreliable agents remain a key bottleneck to system-level rel...
arXiv:2606. 30639v1 Announce Type: new Abstract: World models offer a principled way to equip long-horizon LLM agents with foresight: predictions of action consequences before execution.
The paper investigates whether latent world models truly capture the underlying dynamics of their environments. It introduces the idea of faithful latent world models, defined as an equivariance between the environment’s transition map and the learned latent dynamics, and proposes latent rollout consistency (LRC) as a metric for evaluating action‑conditioned latent predictions. Experiments show that none of the state‑of‑the‑art latent world models studied actually faithfully model the environment’s dynamics, despite solving the tasks they were trained on.
Mine Odyssey is a new benchmark that tests agentic spatial intelligence by having agents navigate Minecraft reconstructions of real-world locations. The benchmark includes 180 tasks across 30 sites in 20 countries, featuring 20 outdoor and 10 indoor settings with varied scales, layouts, and terrains. Each task gives a natural‑language instruction to visit specific waypoints in order, requiring agents to find routes, open doors, and manage navigation errors.
EVEWorld introduces a physical evolution-supervision framework for embodied world models, addressing the issue of Model Laziness by focusing on physical consistency rather than visual fidelity. The framework comprises Instance-Guided Restoration (IGR) to enforce instance consistency and Temporal Instance Alignment (TIA) to align target instances across adjacent frames. Experiments on DreamGenBench, EWMBench, and PBench show an 87.5% reduction in the Model Laziness Rate (MLR) compared to GigaWorld-0, and the model ranks 6th in JEPA Similarity on the WorldArena 2.0 Track 1 leaderboard.