Environment Maps: Structured Environmental Representations for Long-Horizon Agents
Exploring the potential of structured environmental representations to enhance long-horizon agent performance.
Summary
The paper, published on March 26, 2026, in ArXiv AI, addresses the persistent challenges in automating complex software workflows, particularly in long-horizon settings.
It highlights that while large language models (LLMs) have made significant advancements, the automation of intricate tasks remains problematic for agents operating over extended timeframes.
The research emphasizes the need for structured environmental representations to enhance the capabilities of long-horizon agents, potentially leading to improved performance in various applications.
Key Facts
| Fact | Value |
|---|---|
| Primary source | ArXiv AI |
| Source count | 2 |
| First published | 2026-03-26T04:00:00.000Z |
Updates
Update at 04:00 UTC on 2026-03-27
ArXiv AI reported Exploring the potential of structured environmental representations to enhance long-horizon agent performance.
Sources: ArXiv AI
Sources
- ArXiv AI: https://arxiv.org/abs/2603.23610