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. |
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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. |
Benchmarking GPT-4 on Algorithmic Problems: A Systematic Evaluation of Prompting Strategies (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) have revolutionized the field of natural language processing . however, it has been shown that they lack systematic generalization, which allows to extrapolate the learned statistical regularities outside the training distribution. |
| Approach: | They propose to benchmark a LLM with two parameters to find out its performance . they compare it to a variant of the Transformer-Encoder architecture to find the same problem . |
| Outcome: | The proposed model outperforms the previous model on three algorithmic tasks with two parameters. |
CorNav: Autonomous Agent with Self-Corrected Planning for Zero-Shot Vision-and-Language Navigation (2024.findings-acl)
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| Challenge: | Existing vision-and-language navigation methods do not incorporate environmental feedback into their decision-making processes. |
| Approach: | They propose a framework that incorporates environmental feedback into decision-making and a 3D simulator that renders realistic scenarios using Unreal Engine 5. |
| Outcome: | The proposed framework outperforms existing vision-and-language navigation methods in a zero-shot multi-task setting by 28.1% on average. |
MAPRO: Recasting Multi-Agent Prompt Optimization as Maximum a Posteriori Inference (2026.findings-eacl)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities across diverse tasks. |
| Approach: | They propose a framework that optimizes MAS prompts as a maximum a posteriori problem and then iteratively updates agent prompts. |
| Outcome: | The proposed framework surpasses manual and automated benchmarks in multiple tasks and provides general guidelines for building more reliable and principled multi-agent systems in the future. |
Follow the Beaten Path: The Role of Route Patterns on Vision-Language Navigation Agents Generalization Abilities (2025.naacl-long)
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| Challenge: | Vision and language navigation (VLN) is a challenging task towards the creation of embodied agents. |
| Approach: | They propose a solution that combines visual and linguistic features to enable VLN . they propose augmentation of the training data to fill the gap in missing patterns . |
| Outcome: | The proposed solution fills the gap in missing patterns of training data. |
GesNavi: Gesture-guided Outdoor Vision-and-Language Navigation (2024.eacl-srw)
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| Challenge: | Existing datasets for outdoor Vision-and-Language Navigation (VLN) tasks do not include verbal instructions for communicating with mobility. |
| Approach: | They propose a dataset for gesture-guided outdoor VLN instructions with demonstrative expressions that incorporates gestures and linguistic commands. |
| Outcome: | The proposed datasets are compared against existing datasets and analysed in detail. |
AgentTuning: Enabling Generalized Agent Abilities for LLMs (2024.findings-acl)
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| Challenge: | Open large language models (LLMs) with great performance in various tasks are far inferior to commercial models such as ChatGPT and GPT-4 when acting as agents to tackle complex tasks in the real world. |
| Approach: | They propose a method to enhance the agent capabilities of LLMs while maintaining their general abilities. |
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Prompt-Based Monte-Carlo Tree Search for Goal-oriented Dialogue Policy Planning (2023.emnlp-main)
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| Challenge: | Optimal policy planning is a difficult task, authors say . many goal-oriented conversations require subjective strategies, they say - a problem in goal-orientated settings . |
| Approach: | They propose an approach to perform goal-oriented dialogue policy planning without model training. |
| Outcome: | The proposed approach performs goal-oriented dialogue policy planning without model training. |
MTGP: Multi-turn Target-oriented Dialogue Guided by Generative Global Path with Flexible Turns (2023.findings-acl)
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| Challenge: | Existing approaches focus on global planning, which plans toward the target before the conversation. |
| Approach: | They propose to generate a global path as a natural language sentence instead of a sequence of nodes. |
| Outcome: | The proposed method has fewer turns, more coherent semantics, and higher success rate than baselines. |
MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization (2023.findings-emnlp)
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Yuyan Chen, Zhihao Wen, Ge Fan, Zhengyu Chen, Wei Wu, Dayiheng Liu, Zhixu Li, Bang Liu, Yanghua Xiao
| Challenge: | Existing research emphasizes the importance of adapting prompts to specific tasks, rather than specific LLMs. |
| Approach: | They propose a model-adaptive prompt optimizer method that optimizes original prompts for each LLM in downstream tasks. |
| Outcome: | The proposed method can optimize prompts for an LLM in downstream tasks. |