PlanGPT: Enhancing Urban Planning with a Tailored Agent Framework (2025.acl-industry)
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| Challenge: | Empirical tests demonstrate that PlanGPT framework has achieved advanced performance, providing comprehensive support that significantly enhances professional planning efficiency. |
| Approach: | They propose a specialized AI agent framework tailored for urban and spatial planning that integrates a customized local database retrieval system and domain-specific knowledge activation capabilities. |
| Outcome: | Empirical tests show that PlanGPT framework significantly improves planning efficiency . it integrates a customized database retrieval system, domain-specific knowledge activation capabilities, and advanced tool orchestration mechanisms. |
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PlanGPT-VL: Enhancing Urban Planning with Domain-Specific Vision-Language Models (2025.emnlp-industry)
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| Challenge: | Existing Vision-Language Models (VLMs) fail to analyze planning maps . specialized visual representations of land use zones, transportation networks, and development policies are needed to interpret complex planning maps. |
| Approach: | They propose a domain-specific VLM tailored for urban planning maps that employs three innovations: PlanAnno-V framework for high-quality VQA data synthesis, Critical Point Thinking (CPT) and PlanBench-V benchmark for systematic evaluation. |
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UrbanLLM: Autonomous Urban Activity Planning and Management with Large Language Models (2024.findings-emnlp)
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| Challenge: | UrbanLLM is a fine-tuned large language model designed to tackle diverse urban problems. |
| Approach: | They propose a fine-tuned large language model to tackle diverse urban problems . UrbanLLM decomposes urban-related queries into manageable sub-tasks . |
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RaDA: Retrieval-augmented Web Agent Planning with LLMs (2024.findings-acl)
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| Challenge: | Agents powered by large language models inherit important limitations such as the restricted context length, dependency on human-engineered exemplars, and insufficient generalization. |
| Approach: | They propose a novel planning method for Web agents that disentangles planning into two stages: for a new given task, it decomposes tasks into high-level subtasks; and then iteratively synthesizes actions based on dynamically retrieved exemplars. |
| Outcome: | The proposed method decomposes tasks into high-level subtasks and iteratively synthesizes actions based on dynamically retrieved exemplars. |
DeepPlanner: Scaling Planning Capability for Deep Research Agents via Advantage Shaping (2026.findings-acl)
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| Challenge: | Existing approaches to planning involve implicit planning or introduce explicit planners without systematically optimizing the planning stage. |
| Approach: | They propose an end-to-end RL framework that enhances the planning capabilities of deep research agents. |
| Outcome: | Experiments show that DeepPlanner improves planning quality and achieves state-of-the-art results under a lower training budget. |
PlanGEN: A Multi-Agent Framework for Generating Planning and Reasoning Trajectories for Complex Problem Solving (2025.emnlp-main)
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Mihir Parmar, Xin Liu, Palash Goyal, Yanfei Chen, Long Le, Swaroop Mishra, Hossein Mobahi, Jindong Gu, Zifeng Wang, Hootan Nakhost, Chitta Baral, Chen-Yu Lee, Tomas Pfister, Hamid Palangi
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| Approach: | They propose a model-agnostic and easily scalable agent framework with three key components: constraint, verification, and selection agents. |
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Planning with Multi-Constraints via Collaborative Language Agents (2025.coling-main)
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| Challenge: | Recent advances in neural language models have sparked a new surge of intelligent agent research. |
| Approach: | They propose a method for collaborative LLM-based multi-agent systems that simplifies complex task planning with constraints by decomposing it into a hierarchy of subordinate tasks. |
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LLMAP: LLM-Assisted Multi-Objective Route Planning with User Preferences (2025.findings-emnlp)
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| Challenge: | a recent study shows that large language models (LLMs) are limited in understanding natural language preferences. |
| Approach: | They propose a novel LLM-as-Parser-based route planning system that utilizes an LLM to comprehend natural language, extract user preferences and recognize task dependencies. |
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MapGPT: Map-Guided Prompting with Adaptive Path Planning for Vision-and-Language Navigation (2024.acl-long)
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| Challenge: | Embodied agents equipped with GPT as their brains have extraordinary decision-making and generalization abilities across various tasks. |
| Approach: | They propose a map-based agent that introduces an online linguistic-formed map to encourage global exploration. |
| Outcome: | The proposed agent achieves state-of-the-art zero-shot performance on R2R and REVERIE simultaneously. |
PlanGenLLMs: A Modern Survey of LLM Planning Capabilities (2025.acl-long)
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| Challenge: | Existing studies have focused on developing LLMs to automate complex planning tasks. |
| Approach: | They propose to provide a comprehensive overview of current LLM planners to fill this gap . they examine performance criteria including completeness, executability, optimality, representation, generalization, and efficiency . |
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PlanningArena: A Modular Benchmark for Multidimensional Evaluation of Planning and Tool Learning (2025.acl-long)
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| Challenge: | Recent studies have shown that LLMs can be significantly improved by integrating external tools. |
| Approach: | They propose a framework that integrates external tools into large language models to evaluate their ability to generate action plans. |
| Outcome: | The proposed framework evaluates the ability of large language models to generate action plans and generate action plan templates. |