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.
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?
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arXiv AI
Jun 15
SIMMER: Benchmarking Latent Failures in LLM Executable Planning with a World Model
arXiv:2606. 14574v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as planners for autonomous agents in household environments.
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
Jul 7
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.
arXiv AI
Jun 29
Towards Reliable and Robust LLM Planning: Symbolic Feedback-Driven Iterative Self-Refinement Framework
arXiv:2606. 27757v1 Announce Type: new Abstract: Large language models (LLMs) have attracted widespread attention from academia and industry, yet their deployment raises critical security concerns regarding robustness and reliability.
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 3
From Actions to Understanding: Conformal Interpretability of Temporal Concepts in LLM Agents
arXiv:2604. 19775v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly deployed as autonomous agents capable of reasoning, planning, and acting within interactive environments.
arXiv AI
Jul 1
Ask the World Before Acting: Budgeted Environment Probing for World-Model Calibration
arXiv:2606. 31422v1 Announce Type: new Abstract: Long-horizon language agents do not only choose actions; they carry a private model of the world from one decision to the next.
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.
Hugging Face Trending Papers
Jun 19
Training the Orchestrator: A Supervised Approach to End-to-End PDDL Planning with LLM Agents
Translating natural-language planning intent into verified plans is a longstanding challenge: people communicate goals in language, while classical planners require formal PDDL specifications. Recent agentic frameworks bridge this gap by orchestrating a pool of specialized repair agents inside a verifier-checked refinement loop, but the orchestrator at the centre is itself a prompted frontier LLM, paying a frontier-LLM API call at every refinement step.
arXiv AI
Jul 2
AGI Maze as a Benchmark Framework for World-Modeling Agents
arXiv:2607. 00627v1 Announce Type: new Abstract: Large language models (LLMs) are powerful pattern-completion systems, but their default operating mode - predicting the next token from a static context - does not reliably produce persistent, manipulable representations of an external world.
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.