arXiv:2607. 18806v1 Announce Type: new Abstract: This paper proposes AI Tour Meeting, a group travel planning framework powered by multiple Large Language Model (LLM)-based agents.
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arXiv:2507.05723v1 Announce Type: cross Abstract: The emergence of Large Language Models (LLMs) with increasingly sophisticated natural language understanding and generative capabilities has sparked...
arXiv:2603. 02070v3 Announce Type: replace Abstract: When automating plan generation for a real-world sequential decision problem, the goal is often not to replace the human planner, but to facilitate an iterative reasoning and elicitation process, where the human's role is to guide the AI planner according to their preferences and expertise.
arXiv:2608.30924v1 Announce Type: new Abstract: Travel itinerary generation requires balancing strict spatio-temporal constraints with human preferences. Existing LLM-based planners mainly rely on st...
arXiv:2610.16140v1 Announce Type: new Abstract: Transportation planning has traditionally required substantial expertise in network modeling, traffic assignment, and optimization. As a result, strate...
arXiv:2509. 10317v2 Announce Type: replace-cross Abstract: The article describes the development of a hybrid social robot control architecture to overcome the limitations of traditional approaches, where behavior scripts manually synchronize the robot's actions and text, and existing methods focus primarily on short dialogue responses.
arXiv:2607. 05477v1 Announce Type: cross Abstract: Improving the task performance of Large Language Models (LLMs) is essential, yet scaling these models faces significant challenges such as diminishing returns and high costs.