LLM agents with similar success rates can follow different action sequences. An Agent Behavior Map represents LLM-harness combinations using centered action log-likelihoods on shared reference traject...
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:2602. 11619v2 Announce Type: replace Abstract: Running the same LLM agent on identical inputs yields 2.
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.
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.
KC-Bench is a dynamic interactive benchmark designed to evaluate how large language model agents reconcile user instructions, internal knowledge, and real‑time environmental observations. It contains 238 manually curated multi‑turn tasks that test world‑knowledge conflicts, input inconsistencies, and multi‑source temporal conflicts, using a user simulator, stateful tools, deterministic environment assertions, an open‑source natural‑language evaluator, and human trajectory verification. Evaluation of nine models—including DeepSeek‑V4‑Flash, GLM‑5.2, and MiniMax‑M3—reveals significant cross‑domain variation, with no model reliably handling factual correction, identity consistency checking, and temporal conflict resolution across all settings, and shows that missed conflicts can propagate to tool calls or synthetic protected‑data flows.
arXiv:2602.05842v3 Announce Type: replace Abstract: Large language models (LLMs) have achieved strong performance in language-centric tasks. However, in agentic settings, LLMs often struggle to antic...
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: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:2603. 28963v2 Announce Type: replace-cross Abstract: Simulation with realistic traffic agents is essential for validating autonomous driving systems.
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.
arXiv:2605.22164v2 Announce Type: replace Abstract: Latent world models can learn representations that contain information needed for control, while the downstream controller may still rank candidate...