QQCWB

GV

Preact: Predicting Future In React Enhances Agent’S Planning Ability

Di: Ava

arXiv:2409.16686v2 [cs.AI] 9 Nov 2024 MSI-Agent: Incorporating Multi-Scale Insight into Embodied Agents for Superior Planning and Decision-Making Abstract The reasoning abilities of Large Language Models (LLMs) remain a topic of debate. Some methods such as ReAct-based prompting, have gained popularity for claiming to enhance sequential decision-making abilities of agentic LLMs. However, it is unclear what is the source of improvement in LLM reasoning with ReAct based prompting. [Feb 2024] „PreAct: Predicting Future in ReAct Enhances Agent’s Planning Ability“ [paper] [Apr 2024] „Aligning LLM Agents by Learning Latent Preference from User Edits“ [paper]

ReactAgent - The open-source React.js LLM Agent

PreAct: Prediction Enhances Agent’s Planning Ability (Coling2025) – PreAct/README.md at main · Fu-Dayuan/PreAct Unleashing the Potential of Large Language Models as Prompt Optimizers Xinyu Tang*, Xiaolei Wang*, Wayne Xin Zhao†, Siyuan Lu, Yaliang Li, Ji-Rong Wen AAAI 2025 PreAct: Predicting Future in ReAct Enhances Agent’s Planning Ability Dayuan Fu, Jianzhao Huang, Siyuan Lu, Guanting Dong, Yejie Wang, Keqing He, Weiran Xu† COLING 2025

PreAct: Predicting Future in ReAct Enhances Agent’s Planning Ability Dayuan Fu, Jianzhao Huang, Siyuan Lu, Guanting Dong, Yejie Wang, Keqing He, Weiran Xu CoRR 2024

Pre-Act: Multi-Step Planning and Reasoning Improves Acting in LLM Agents

PreAct: Predicting Future in ReAct Enhances Agent’s Planning Ability Addressing the discrepancies between predictions and actual outcomes often aids individuals in expanding their thought processes and engaging in reflection, thereby facilitating reasoning in View recent discussion. Abstract: Addressing the disparity between forecasts and actual results can enable individuals to expand their thought processes and stimulate self-reflection, thus promoting accurate planning. In this research, we present **PreAct**, an agent framework that integrates **pre**diction, **rea**soning, and **act**ion. By utilizing the information derived from

Our experiments demonstrate that PreAct outperforms the ReAct approach in accomplishing complex tasks and that PreAct can be co-enhanced when combined with Reflexion methods. We prompt the model with different numbers of historical predictions and find that historical predictions have a sustained positive effect on LLM planning. Cited by PreAct: Predicting Future in ReAct Enhances Agent’s Planning Ability J Jianzhao Huang +5 Unleashing the Potential of Large Language Models as Prompt Optimizers Xinyu Tang*, Xiaolei Wang*, Wayne Xin Zhao†, Siyuan Lu, Yaliang Li, Ji-Rong Wen AAAI 2025 PreAct: Predicting Future in ReAct Enhances Agent’s Planning Ability Dayuan Fu, Jianzhao Huang, Siyuan Lu, Guanting Dong, Yejie Wang, Keqing He, Weiran Xu† COLING 2025

We presented the model with varying quantities of historical predictions and discovered that these predictions consistently enhance LLM planning.The variances in single-step reasoning between PreAct and ReAct indicate that PreAct indeed has benefits in terms of diversity and strategic orientation over ReAct. Figure 4: Historical Prediction’s Influence. 0 refers to ReAct’s history, 1 refers to immediate mode history and all refers to permanent mode history. – „PreAct: Predicting Future in ReAct Enhances Agent’s Planning Ability“ Recommended citation: Fu, D., Huang, J., Lu, S., Dong, G., Wang, Y., He, K., & Xu, W. (2025). PreAct: Prediction Enhances Agent’s Planning Ability. In Proceedings of

  • PreAct/README.md at main · Fu-Dayuan/PreAct · GitHub
  • 論文の概要: PreAct: Prediction Enhances Agent’s Planning Ability
  • Title: PreAct: Prediction Enhances Agent’s Planning Ability

They propose a model that integrates prediction, reasoning, and action with other models to provide a wider range of reasoning and more efficient actions.

Can AI agents plan better by predicting the future? PreAct: Prediction Enhances Agent’s Planning Ability Published 12/5/2024 by Dayuan Fu, Jianzhao Huang, Siyuan Lu, Guanting Dong, yejie wang, Keqing He, Weiran Xu We presented the model with varying quantities of historical predictions and discovered that these predictions consistently enhance LLM planning.The variances in single-step reasoning between PreAct and ReAct indicate that PreAct indeed has benefits in terms of diversity and strategic orientation over ReAct.

TLDR The experiments demonstrate that PreAct outperforms the ReAct approach in accomplishing complex tasks and that PreAct can be co-enhanced when combined with Reflexion methods, and the differences in single-step reasoning between PreAct and ReAct show that PreAct indeed offers advantages in terms of diversity and strategic directivity over

Title: PreAct: Prediction Enhances Agent’s Planning Ability

Using LLM with ReAct Agent. Introduction | by Nisarg Mehta | Medium

PreAct: Predicting Future in ReAct Enhances Agent’s Planning Ability TOOLLLM: FACILITATING LARGE LANGUAGE MODELS TO MASTER 16000+ REAL-WORLD APIS ⭐ -AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls

For the πagent(·) part, PreAct will prompt the LLM to gen-erate a prediction p of future observation(s) and measurements in each step and hint the LLM to reflect or change its plan direction based on the difference between the predict observation(s) and the real observation. 基本信息 -文档不存在

PreAct: Predicting Future in ReAct Enhances Agent’s Planning Ability Addressing the discrepancies between predictions and actual outcomes often aids individuals in expanding their thought processes and engaging in reflection, thereby facilitating reasoning in As a result, MSI insight can improve an agent’s planning and decision-making ability in both single-turn plans (TEACh) and multi-turn plans (Alfworld). This showcases its extensive versatility and potential applications across different contexts. Article „PreAct: Prediction Enhances AgentOn J-GLOBAL, this item will be available after more than half a year after the record posted. In addtion, medical articles require to login to MyJ-GLOBAL. 記憶装置 About 記憶装置 Search „記憶装置“ Detailed information , 計画 About 計画 Search „計画“ Detailed information , 戦略 About 戦略 Search „戦略“ Detailed information

PreAct:Prediction Enhances Agent’s Planning Ability Addressing the disparity between forecasts and actual results can enable individuals to expand their thought processes and stimulate self-reflection, thus promoting accurate planning. In this research, we present PreAct, an agent framework that PreAct: Prediction Enhances Agent’s Planning Ability. In Owen Rambow, Leo Wanner, Marianna Apidianaki, Hend Al-Khalifa 0001, Barbara Di Eugenio, Steven Schockaert, editors, Proceedings of the 31st International Conference on Computational Linguistics, COLING 2025, Abu Dhabi, UAE, January 19-24, 2025. pages 1-16, Association for Computational We presented the model with varying quantities of historical predictions and discovered that these predictions consistently enhance LLM planning.The variances in single-step reasoning between PreAct and ReAct indicate that PreAct indeed has benefits in terms of diversity and strategic orientation over ReAct.

arXiv:2409.16686v2 [cs.AI] 9 Nov 2024

Our experiments demonstrate that PreAct outperforms the ReAct approach in accomplishing complex tasks and that PreAct can be co-enhanced when combined with Reflexion methods. We prompt the model with different numbers of historical predictions and find that historical predictions have a sustained positive effect on LLM planning. PreAct: Predicting Future in ReAct Enhances Agent’s Planning Ability 类型：论文; 推荐星：4 类别：Agent; 解读 „PreAct: Predicting Future in ReAct Enhances Agent’s Planning Ability“是一篇关于增强智能代理规划能力的论文。 它介绍了一个名为PreAct的代理框架，该框架将预测与推理和行动

Specifically, after making an action, PreAct requires further predict the possible observa- tions and corresponding measures at a higher level. This mode can enhance LLMs’ directional strategy in reasoning to assist planning in the right way. It can also guide LLMs to conduct more diverse rea- soning, thereby leading LLMs to explore thinking PreAct: Predicting Future in ReAct Enhances Agent’s Planning Ability arXiv – CS – Computation and LanguagePub Date : 2024-02-18, DOI: arxiv-2402.11534 Dayuan Fu, Jianzhao Huang, Siyuan Lu, Guanting Dong, Yejie Wang, Keqing He, Weiran Xu ‪Beijing University of Posts and Telecommunications‬ – ‪‪Dikutip 598 kali‬‬ – ‪Natural Language Processing‬

Addressing the disparity between forecasts and actual results can enable individuals to expand their thought processes and stimulate self-reflection, thus promoting accurate planning. In this research, we present **PreAct**, an agent framework that integrates **pre**diction, **rea**soning, and **act**ion. By utilizing the information derived from PreAct: Prediction Enhances Agent’s Planning Ability. In Owen Rambow, Leo Wanner, Marianna Apidianaki, Hend Al-Khalifa 0001, Barbara Di Eugenio, Steven Schockaert, editors, Proceedings of the 31st International Conference on Computational Linguistics, COLING 2025, Abu Dhabi, UAE, January 19-24, 2025. pages 1-16, Association for Computational

‪Beijing University of Posts and Telecommunications‬ – ‪‪引用次数：626 次‬‬ – ‪Natural Language Processing‬ PreAct: Predicting Future in ReAct Enhances Agent’s Planning Ability Addressing the discrepancies between predictions and actual outcomes often aids individuals in expanding their thought processes and engaging in reflection, thereby facilitating reasoning in

TLDR The experiments demonstrate that PreAct outperforms the ReAct approach in accomplishing complex tasks and that PreAct can be co-enhanced when combined with Reflexion methods, and the differences in single-step reasoning between PreAct and ReAct show that PreAct indeed offers advantages in terms of diversity and strategic directivity over