Environment Maps: Structured Environmental Representations for Long-Horizon Agents
#environment maps #structured representations #long-horizon agents #AI planning #robotics #autonomous agents #environmental interaction
📌 Key Takeaways
- Environment Maps are structured representations designed for long-horizon agent tasks.
- They provide a framework for agents to navigate and interact with complex environments over extended periods.
- The approach aims to improve planning and decision-making in dynamic or large-scale settings.
- This research contributes to advancements in AI and robotics for sustained autonomous operation.
📖 Full Retelling
arXiv:2603.23610v1 Announce Type: new
Abstract: Although large language models (LLMs) have advanced rapidly, robust automation of complex software workflows remains an open problem. In long-horizon settings, agents frequently suffer from cascading errors and environmental stochasticity; a single misstep in a dynamic interface can lead to task failure, resulting in hallucinations or trial-and-error. This paper introduces $\textit{Environment Maps}$: a persistent, agent-agnostic representation th
🏷️ Themes
AI Representation, Robotics Navigation
Entity Intersection Graph
No entity connections available yet for this article.
Original Source
arXiv:2603.23610v1 Announce Type: new
Abstract: Although large language models (LLMs) have advanced rapidly, robust automation of complex software workflows remains an open problem. In long-horizon settings, agents frequently suffer from cascading errors and environmental stochasticity; a single misstep in a dynamic interface can lead to task failure, resulting in hallucinations or trial-and-error. This paper introduces $\textit{Environment Maps}$: a persistent, agent-agnostic representation th
Read full article at source