Year: 2025
Type: conference-paper
Abstract: Intelligent agents designed for interactive environments face significant challenges in textbased games, a domain that demands complex reasoning and adaptability.While agents based on large language models (LLMs) using selfreflection have shown promise, they struggle when initially successful and exhibit reduced effectiveness when using smaller LLMs.We introduce Sweet&Sour, a novel approach that addresses these limitations in existing reflection methods by incorporating positive experiences and managed memory to enrich the context available to the agent at decision time.Our comprehensive analysis spans both closed-and open-source LLMs and demonstrates the effectiveness of Sweet&Sour in improving agent performance, particularly in scenarios where previous approaches fall short.
Institution: Delft University of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4412944480