# Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search

**Authors:** Howard Yen, Ashwin Paranjape, Mengzhou Xia, Thejas Venkatesh, Jack Hessel, Danqi Chen, Yuhao Zhang  
**arXiv:** [2510.18939](https://arxiv.org/abs/2510.18939)  
**Date:** 2025-10-21  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2510.18939v1/

## Key points

- Long-horizon agentic search involves exploring the web over extended trajectories and synthesizing information from multiple sources for advanced applications.
- Existing agentic search frameworks struggle with scaling to long trajectories due to context limitations, noisy content accumulation, exceeding context window and tool budgets, or premature termination.
- Authors introduce SLIM (Simple Lightweight Information Management) as a framework that separates retrieval into distinct search and browse tools, maintaining concise context for longer and more focused searches.
- Experimental evaluation shows that SLIM performs comparably at lower cost and with fewer tool calls compared to strong open-source baselines across various base models.
- When using o3 as the base model, SLIM achieves 56% on BrowseComp and 31% on HLE tasks, surpassing all open-source frameworks by 8 and 4 absolute points respectively while requiring only 4-6 times fewer tool calls.
- Authors release an automated fine-grained trajectory analysis pipeline along with an error taxonomy for characterizing long-horizon agentic search frameworks, showing that SLIM exhibits fewer hallucinations compared to previous systems.
- The hope is that their analysis framework and simple tool design will guide future developments in long-horizon agents.

## Summary

In their work titled "Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search," authors Howard Yen, Ashwin Paranjape, Mengzhou Xia, Thejas Venkatesh, Jack Hessel, Danqi Chen, and Yuhao Zhang address the challenges of long-horizon agentic search. This type of search involves iteratively exploring the web over extended trajectories and synthesizing information from multiple sources to enable advanced applications like deep research systems. The authors highlight that existing agentic search frameworks struggle to scale to long trajectories due to context limitations. These frameworks often accumulate lengthy and noisy content, exceed context window and tool budgets, or terminate prematurely. To address these issues, the authors introduce SLIM (Simple Lightweight Information Management), a straightforward framework that separates retrieval into distinct search and browse tools. SLIM periodically summarizes the trajectory to maintain concise context while facilitating longer and more focused searches. Through experimental evaluation on long-horizon tasks, SLIM demonstrates comparable performance at significantly lower cost and with fewer tool calls compared to strong open-source baselines across various base models. Notably, when using o3 as the base model, SLIM achieves 56% on BrowseComp and 31% on HLE tasks, surpassing all open-source frameworks by 8 and 4 absolute points respectively while requiring only 4-6 times fewer tool calls. Furthermore,the authors release an automated fine-grained trajectory analysis pipeline along with an error taxonomy for characterizing long-horizon agentic search frameworks. They find that SLIM exhibits fewer hallucinations compared to previous systems. The hope is that their analysis framework and simple tool design will guide future developments in long-horizon agents. For those interested in exploring further,the code and data related to this study are available at https://github.com/howard-yen/SLIM.