Environment Maps: Structured Environmental Representations for Long-Horizon Agents
📰 ArXiv cs.AI
Environment Maps provide a structured representation for long-horizon agents to overcome cascading errors and environmental stochasticity
Action Steps
- Identify the limitations of current large language models (LLMs) in long-horizon settings
- Develop a persistent and agent-agnostic representation of the environment
- Implement Environment Maps to reduce cascading errors and environmental stochasticity
- Evaluate the performance of Environment Maps in various long-horizon tasks
Who Needs to Know This
AI engineers and researchers working on long-horizon agents and automation of complex software workflows can benefit from Environment Maps to improve agent performance and reduce errors
Key Insight
💡 Environment Maps provide a structured representation of the environment to improve agent performance in long-horizon settings
Share This
💡 Environment Maps help long-horizon agents avoid cascading errors and stochasticity
Key Takeaways
Environment Maps provide a structured representation for long-horizon agents to overcome cascading errors and environmental stochasticity
Full Article
Title: Environment Maps: Structured Environmental Representations for Long-Horizon Agents
Abstract:
arXiv:2603.23610v2 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 tha
Abstract:
arXiv:2603.23610v2 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 tha
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