In the field of urban planning, general-purpose large language models often struggle to meet the specific needs of planners. Tasks like generating urban planning texts, retrieving related information, and evaluating planning documents pose unique challenges. To enhance the efficiency of urban professionals and overcome these obstacles, we introduce PlanGPT, the first specialized AI agent framework tailored for urban and spatial planning. Developed through collaborative efforts with professional urban planners, PlanGPT integrates a customized local database retrieval system, domain-specific knowledge activation capabilities, and advanced tool orchestration mechanisms. Through its comprehensive agent architecture, PlanGPT coordinates multiple specialized components to deliver intelligent assistance precisely tailored to the intricacies of urban planning workflows. Empirical tests demonstrate that PlanGPT framework has achieved advanced performance, providing comprehensive support that significantly enhances professional planning efficiency.

Instruction tuning stands as a crucial advancement in leveraging large language models (LLMs) for enhanced task performance. However, the annotation of instruction datasets has traditionally been expensive and laborious, often relying on manual annotations or costly proprietary LLMs. Recent works explore approaches to synthesize data with open-sourced LLMs but require high-quality human-crafted seed data. In this work, we introduce , an end-to-end framework to synthesize high-quality instruction data with open-sourced LLMs and sampled unlabeled documents, eliminating the necessity for seed data. Starting from diverse pre-screened documents, the framework synthesizes complex and diverse high-quality instruction and response pairs in different stages. We propose a tagging-based prompt method to generate diverse and complex seed data and a UCB-based approach to augment more instruction data with the seed data. A novel Think Different prompt is proposed to address the distributional limitations of the seeds, further boosting the data diversity. Experiments prove that the can generate diverse and complex high-quality data even with a opensource small teacher model. The synthesized instruction data demonstrates performance that is comparable to, or even surpasses, baseline annotation methods with proprietary LLMs or open-sourced LLMs while requiring fewer instruction data samples.

High-quality instruction data is crucial for developing large language models (LLMs), yet existing approaches struggle to effectively control instruction complexity. We present Tag-Instruct, a novel framework that enhances instruction complexity through structured semantic compression and controlled difficulty augmentation. Unlike previous prompt-based methods operating on raw text, Tag-Instruct compresses instructions into a compact tag space and systematically enhances complexity through RL-guided tag expansion. Through extensive experiments, we show that Tag-Instruct outperforms existing instruction complexity augmentation approaches. Our analysis reveals that operating in tag space provides superior controllability and stability across different instruction synthesis frameworks.

In this paper, we introduce NarrativePlay, a novel system that allows users to role-play a fictional character and interact with other characters in narratives in an immersive environment. We leverage Large Language Models (LLMs) to generate human-like responses, guided by personality traits extracted from narratives. The system incorporates auto-generated visual display of narrative settings, character portraits, and character speech, greatly enhancing the user experience. Our approach eschews predefined sandboxes, focusing instead on main storyline events from the perspective of a user-selected character. NarrativePlay has been evaluated on two types of narratives, detective and adventure stories, where users can either explore the world or increase affinity with other characters through conversations.