Large language models (LLMs) are increasingly used as autonomous agents, tackling tasks from robotics to web navigation. Their performance depends on the underlying base agent. Existing methods, however, struggle with long-context reasoning and goal adherence. We introduce StateAct, a novel and efficient base agent that enhances decision-making through (1) self-prompting, which reinforces task goals at every step, and (2) chain-of-states, an extension of chain-of-thought that tracks state information over time. StateAct outperforms ReAct, the previous best base agent, by over 10% on Alfworld, 30% on Textcraft, and 7% on Webshop across multiple frontier LLMs. We also demonstrate that StateAct can be used as a drop-in replacement for ReAct with advanced LLM agent methods such as test-time scaling, yielding an additional 12% gain on Textcraft. By improving efficiency and long-range reasoning without requiring additional training or retrieval, StateAct provides a scalable foundation for LLM agents. We open source our code to support further research at https://github.com/ai-nikolai/stateact .
Enhancing Granular Sentiment Classification with Chain-of-Thought Prompting in Large Language Models
John F Kennedy High school Fremont, USA（约翰·F·肯尼迪高中弗里蒙特分校）；Queensland Academy for Science Mathematics and Technology（昆士兰科学数学与技术学院）；Research Spark Hub Inc（Research Spark Hub公司）
Leveraging Large Language Models for Automated Causal Loop Diagram Generation: Enhancing System Dynamics Modeling through Curated Prompting Techniques
MGH Institute for Technology Assessment, Harvard Medical School（哈佛医学院MGH技术评估研究所）；Melbourne Business School, University of Melbourne（墨尔本大学墨尔本商学院）；Sloan School of Management, Massachusetts Institute of Technology（麻省理工学院斯隆管理学院）
Enhancing Text Annotation through Rationale-Driven Collaborative Few-Shot Prompting
Institute of Artificial Intelligence and Future Networks, Beijing Normal University（北京师范大学人工智能与未来网络研究院）；Hong Kong Baptist University（香港浸会大学）；BNU-HKBU United International College（北京师范大学-香港浸会大学联合国际学院）；College of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）
Enhancing Chain of Thought Prompting in Large Language Models via Reasoning Patterns
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Wuhan AI Research（武汉人工智能研究院）
Enhancing Depression Detection with Chain-of-Thought Prompting: From Emotion to Reasoning Using Large Language Models
College of Information Science and Engineering, Ritsumeikan University（立命馆大学信息科学与工程学院）；Fujita Health University（藤田医科大学）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
Enhancing Document-level Argument Extraction with Definition-augmented Heuristic-driven Prompting for LLMs
School of Engineering and Informatics, University of Sussex（萨塞克斯大学工程与信息学院）；International Business School Suzhou, Xi’an Jiaotong-Liverpool University（西交利物浦大学苏州国际商学院）；Management School, University of Liverpool（利物浦大学管理学院）
Understanding Before Reasoning: Enhancing Chain-of-Thought with Iterative Summarization Pre-Prompting
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science（电子工程学院，上海工程技术大学）；School of Information Science and Engineering, Ningbo University（信息科学与工程学院，宁波大学）
Transfer-Prompting: Enhancing Cross-Task Adaptation in Large Language Models via Dual-Stage Prompts Optimization
School of Artificial intelligence, Jilin University（吉林大学人工智能学院）；Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, Jilin University（吉林大学知识驱动人机智能工程研究中心）；International Center of Future Science, Jilin University（吉林大学未来科学国际中心）
Rethinking the Role of Prompting Strategies in LLM Test-Time Scaling: A Perspective of Probability Theory
MAIS, Institute of Automation, Chinese Academy of Sciences（自动化研究所，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；University of California, Santa Barbara（加州大学圣巴巴拉分校）；Beijing Wenge Technology Co., Ltd（北京文格科技有限公司）
"Sorry, Come Again?" Prompting -- Enhancing Comprehension and Diminishing Hallucination with [PAUSE]-injected Optimal Paraphrasing
AI Institute, University of South Carolina（南卡罗来纳大学AI研究院）；Indian Institute of Technology, Kharagpur（印度理工学院克勒格布尔分校）；Islamic University of Technology（伊斯兰科技大学）；Stanford University（斯坦福大学）；Amazon AI（亚马逊AI研究院）
RoT: Enhancing Large Language Models with Reflection on Search Trees
School of Information Science and Technology, ShanghaiTech（上海科技大学信息科学与技术学院）；Shanghai Engineering Research Center of Intelligent Vision and Imaging（上海智能视觉与成像工程技术研究中心）
Enhancing Vision-Language Models for Autonomous Driving through Task-Specific Prompting and Spatial Reasoning
University of Chinese Academy of Sciences（中国科学院大学）；Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences（中国科学院空间利用技术与工程中心）
