arXiv:2607. 11197v1 Announce Type: new Abstract: When LLMs exhibit uneven performance across planning tasks, these gaps are often attributed to task difficulty.
By Sukai Huang, Chenyuan Zhang, Fucai Ke, Zhixi Cai, Naim Rastgoo, Gholamreza Haffari, Hamid Rezatofighi
arXiv:2610. 26964v1 Announce Type: new Abstract: Large language models (LLMs) struggle with classical planning, but benchmarks rarely distinguish structural difficulty from the difficulty of interpreting symbolic vocabulary.
By Mayukh Debnath, Adeyinka Adedigba, Rammohan Mallipeddi
The paper introduces a verifiable framework for measuring the innate planning efficiency of large language models (LLMs) on sliding puzzles, focusing on complete plans generated without external tools. It defines a Success-Weighted Plan Efficiency (SPE) metric as the product of execution reliability, planning coordination, and action economy, and validates these factors through trajectory replay and certified optimality. Experiments on 4×4 puzzles show that models can finish all trials yet become less economical over longer horizons, and that high efficiency among completed plans can coexist with poor goal attainment, highlighting distinct coordination and completion issues.
By Yezhou Cheng, Charles Ling
The paper introduces the Dual-Latent World Model (Dual-WM), which separates local execution and long-range planning into distinct latent spaces and dynamics models. A new learning method, Long-Horizon Representation Learning with Weighted Rollout (LoRe), supervises predictions at both levels using exponential horizon weights. Experiments on five goal-conditioned visual control tasks show that Dual-WM improves success rates over strong baselines, especially at longer horizons.
By Delin Zhao, Zhengrong Yue, Shaobin Zhuang, Junlin He, Xiaoyu Chen, Zikang Wang, Yuxin Liu, Limin Wang, Yali Wang
arXiv:2608. 16287v1 Announce Type: new Abstract: Joint-embedding predictive world models plan by scoring predicted terminal embeddings against a goal embedding using a cost defined on the representation itself.
By Jiaming Hu, Yan Zheng, Tian Wang
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.
By Arunabh Srivastava (Amir), Mohammad A. (Amir), Khojastepour, Srimat Chakradhar, Sennur Ulukus