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

2025-10-21

AI Summary

Long-horizon agent search suffers from context window limitations, constrained tool budgets, and accumulating redundant information—leading to hallucinations and premature termination. To address these challenges, we propose SLIM, a novel framework that decouples retrieval from browsing and introduces a lightweight, periodic trajectory summarization mechanism to reduce contextual overhead while extending effective exploration depth. SLIM comprises three core components: stepwise retrieval, dynamic content summarization, and an automated fine-grained trajectory analysis pipeline, enabling multi-foundational-model consensus verification. Evaluated on the o3 model, SLIM achieves 56% and 31% accuracy on BrowseComp and HLE benchmarks, respectively—surpassing the best open-source baseline by 8 and 4 percentage points. Moreover, it reduces tool invocations by 4–6×, significantly improving efficiency, robustness, and scalability for long-horizon search tasks.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Long-horizon agentic search requires iteratively exploring the web over long trajectories and synthesizing information across many sources, and is the foundation for enabling powerful applications like deep research systems. In this work, we show that popular agentic search frameworks struggle to scale to long trajectories primarily due to context limitations-they accumulate long, noisy content, hit context window and tool budgets, or stop early. Then, we introduce SLIM (Simple Lightweight Information Management), a simple framework that separates retrieval into distinct search and browse tools, and periodically summarizes the trajectory, keeping context concise while enabling longer, more focused searches. On long-horizon tasks, SLIM achieves comparable performance at substantially lower cost and with far fewer tool calls than strong open-source baselines across multiple base models. Specifically, with o3 as the base model, SLIM achieves 56% on BrowseComp and 31% on HLE, outperforming all open-source frameworks by 8 and 4 absolute points, respectively, while incurring 4-6x fewer tool calls. Finally, we release an automated fine-grained trajectory analysis pipeline and error taxonomy for characterizing long-horizon agentic search frameworks; SLIM exhibits fewer hallucinations than prior systems. We hope our analysis framework and simple tool design inform future long-horizon agents.

Problem

• Overcoming context limitations in long-horizon agentic search
• Addressing accumulated noisy content and budget constraints
• Enabling longer focused searches through trajectory summarization

Innovation

• Separates retrieval into search and browse tools
• Periodically summarizes trajectory for concise context
• Reduces tool calls while maintaining performance