LookStep is proposed, a unified end-to-end framework that combines Language Centric Future State Modeling and Event Driven Rolling Memory that uses language labels to generate coarse-grained navigation progress and future states for each candidate action, while autonomously deciding whether to write each observation in...
Kun-Yang Yu, Ying-Zhe Li, Hongyu Xu et al.· 2 citations
Vision-Language Navigation (VLN) requires an embodied agent to follow natural-language instructions in unseen environments. Recent progress has been largely driven by Multimodal Large Language Models (MLLMs). Existing methods follow a next-step action prediction paradigm, supervising only the expert action, which requi...
Kun-Yang Yu, Ying-Zhe Li, Hongming Xu et al.· 0 citations
Experiments show that NeSy-Spatial consistently improves reasoning accuracy with more precise tool utilization, and is proposed as a neuro-symbolic framework for self-evolving spatial skills.
S. Tian, Zhuoxi Wang, Xuan Zhu et al.· 2 citations
This paper combines symbolic regression with LLMs for feature engineering (SymboLLM-FE) to solve the dual challenges of poor interpretability and numerous iterations by employing a statistical prior-grounded LLM refinement mechanism and single-digit LLM calls.
Zi-Jian Cheng, Zi-Yi Jia, Zhi Zhou et al.· 0 citations
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