arXiv AI
Jun 29
Grounded Iterative Language Planning: How Parameterized World Models Reduce Hallucination Propagation in LLM Agents
arXiv:2606. 27806v1 Announce Type: new Abstract: World models for language agents come in two useful forms.
Agent vs. Parametric World Models: Hybrid Planning for Reliable Language Agents
arXiv:2606. 27806v3 Announce Type: replace Abstract: Language agents plan by generating not only actions but also implicit predictions of how the world will change.
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arXiv AI
Jul 13
GATS: Graph-Augmented Tree Search with Layered World Models for Efficient Agent Planning
arXiv:2607. 08894v1 Announce Type: new Abstract: Large Language Model (LLM) agents have shown promise in multi-step planning tasks, but existing approaches like LATS (Language Agent Tree Search) and ReAct rely heavily on LLM inference during planning, leading to high computational costs and stochastic behavior.
Hugging Face Trending Papers
Jul 9
GATS: Graph-Augmented Tree Search with Layered World Models for Efficient Agent Planning
Large Language Model (LLM) agents have shown promise in multi-step planning tasks, but existing approaches like LATS (Language Agent Tree Search) and ReAct rely heavily on LLM inference during planning, leading to high computational costs and stochastic behavior. We present \textbf{GATS} (Graph-Augmented Tree Search), a planning framework that combines systematic UCB1-based tree search with a layered world model to eliminate LLM calls during inference while achieving superior planning performance.
arXiv AI
Jun 30
Self-Evolving World Models for LLM Agent Planning
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.
arXiv AI
Jul 21
SAGE: Subgoal-Conditioned Action Generation for Latent World Model Planning
arXiv:2607. 17973v1 Announce Type: new Abstract: Latent world models have emerged as a powerful planning paradigm by learning action-conditioned predictive dynamics and using them as internal simulators to imagine and evaluate candidate action sequences.
arXiv AI
Jun 29
Internalizing the Future: A Unified Agentic Training Paradigm for World Model Planning
arXiv:2606. 27483v1 Announce Type: new Abstract: Large language model (LLM) agents have demonstrated strong capability in sequential decision-making, yet they remains fundamentally reactive in long-horizon tasks.
arXiv AI
Jun 3
A Close Look At World Model Recovery In Supervised Fine-Tuned LLM Planners
arXiv:2606. 03685v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) improves end-to-end classical planning in large language models (LLMs), but do these models also learn to represent and reason about the planning problems they are solving?
arXiv AI
Jun 2
COMAP: Co-Evolving World Models and Agent Policies for LLM Agents
arXiv:2606. 02372v1 Announce Type: new Abstract: Equipping language agents with world models enables them to anticipate environment dynamics and evaluate candidate actions before execution.
arXiv AI
Sep 4
CoMAP: Co-Evolving World Models and Agent Policies for LLM Agents
CoMAP introduces a framework that jointly evolves textual world models and agent policies through a closed‑loop interaction. At each decision step the world model forecasts future state feedback for candidate actions, while the agent reflects on the reliability of this feedback to refine its action. The resulting on‑policy trajectories are used to self‑distill and update the world model, improving prediction accuracy and long‑horizon decision‑making across embodied planning, web navigation, and tool‑use benchmarks.
arXiv AI
Sep 25
GRASP: Generating, Revising, and Assessing for Strategic Planning with Agentic AI
GRASP is a multi-stage planning framework that improves the reliability of large language models on complex tasks. It separates planning into three specialized modules—GenPlan for global macro-guidelines, RevPlan for exploring localized strategies, and VerPlan for multi-criteria evaluation—allowing context isolation and strict macro-regularization. Experiments show GRASP outperforms direct LLM planners by significant margins on datasets such as Natural Plan Calendar Scheduling, ZebraLogic, and SciBench Math, and it mitigates performance collapse in multi-task and dual-task settings.
arXiv AI
Sep 24
Agent-Editing World Model: Rethinking World Modeling for LLM Agents
The paper introduces the Agent-Editing World Model (AEWM), a new approach that models how reasoning and actions influence future task progress instead of simulating tool responses. AEWM includes an Action Judge that classifies decisions as Critical, Exploratory, or Noisy, and a State Revision mechanism that edits noisy reasoning–action continuations from the same observed history. The integrated system, EditAct, directly updates the underlying state during real execution, leading to significant performance gains across multiple benchmarks and agent backbones.
arXiv AI
3d ago
Consistent Plan-Act for Long-Horizon Agentic Tasks
The paper introduces Consistent Plan-Act (ConPAct), a method that addresses coordination failures between high-level planners and low-level actors in long-horizon agentic tasks. By prompting both agents to produce structured state assertions and programmatically detecting contradictions, the authors identify a systematic planner-actor state mismatch. ConPAct feeds these detected contradictions back to both agents, fine‑tunes them on consistent interactions, and achieves notable performance gains, such as raising MiniGrid success rates from 38.6% to 54.4% with GPT‑5.6‑sol/terra.