Planning in the LLM Era: Building for Reliability and Efficiency

arXiv:2605.15333v1 Announce Type: new Abstract: Large language models have recently reached near-parity with classical planners on well-known planning domains, yet this competence

arXiv:2610.21108v1 Announce Type: new Abstract: LLM-based planning agents commit to plans of different lengths before interacting with a partially observable environment. Some

arXiv:2605.11225v1 Announce Type: new Abstract: Large language model (LLM)-based agents frequently generate seemingly coherent plans that fail upon execution due to infeasible

arXiv:2605.00412v1 Announce Type: new Abstract: World models have recently re-emerged as a central paradigm for embodied intelligence, robotics, autonomous driving, and

arXiv:2603.19685v1 Announce Type: new Abstract: Large language model (LLM)-based agents have emerged as powerful autonomous controllers for digital environments, including mobile

arXiv:2605.23414v1 Announce Type: new Abstract: LLM-based multi-agent systems can fail even when planned actions are executed correctly because agents may misjudge their knowledge

arXiv:2609.06071v1 Announce Type: new Abstract: PDDL, the de-facto standard language in the AI Planning community, is designed to specify planning domains: sets of instances that

arXiv:2609.25187v1 Announce Type: new Abstract: Task planning bridges high-level instructions and executable behavior in long-horizon manipulation, yet modern