# Learning Agent Routing from Early Experience: The Efficient Query Allocation Strategy of BoundaryRouter

> This paper proposes the BoundaryRouter framework, which achieves efficient routing between LLM inference and full agent execution in cold-start scenarios by leveraging early behavioral experience and scoring-criteria-guided reasoning. Experiments show that it can reduce inference time by 60.6% while improving performance by 28.6%.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-08T03:18:40.000Z
- 最近活动: 2026-05-11T04:23:22.936Z
- 热度: 75.0
- 关键词: 智能体路由, 冷启动, 查询分配, 成本优化, 经验学习, LLM智能体
- 页面链接: https://www.zingnex.cn/en/forum/thread/boundaryrouter
- Canonical: https://www.zingnex.cn/forum/thread/boundaryrouter
- Markdown 来源: floors_fallback

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## [Main Post/Introduction] BoundaryRouter: An Efficient Strategy for Agent Routing in Cold-Start Scenarios

This paper proposes the BoundaryRouter framework, which addresses the problem of efficient routing between LLM inference and full agent execution in cold-start scenarios by leveraging early behavioral experience and scoring-criteria-guided reasoning. Experiments show that this strategy can reduce inference time by 60.6% while improving performance by 28.6%.

## Background: Practical Dilemmas in Agent Deployment and Cold-Start Challenges

Large language model agents excel at complex reasoning tasks but have high latency and computational costs; many queries do not require full agent execution, and the mismatch in resource requirements has spurred the need for intelligent routing. Cold-start scenarios lack historical data, making traditional supervised learning methods difficult to apply, requiring routing solutions that are training-free, quickly deployable, and capable of gradual optimization.

## Core Design of BoundaryRouter: Learning from Early Experience

The core innovation of BoundaryRouter is 'learning from early experience': it builds a compact experience memory bank, obtains initial comparative experience by simultaneously executing lightweight LLM and full agent through a shared seed set; during the inference phase, it retrieves similar historical cases to guide routing decisions, combined with scoring-criteria-guided reasoning, which is transparent and interpretable, making it suitable for cold-start scenarios.

## RouteBench Evaluation Benchmark: Robustness Assessment Across Multiple Scenarios

The research team constructed the RouteBench benchmark, covering three key scenarios: in-domain, paraphrased, and out-of-domain, ensuring that evaluation results reflect the method's robustness under different distribution shifts and providing a standardized evaluation platform for agent routing research.

## Experimental Results: Dual Improvements in Performance and Efficiency

Compared to the baseline of always using agents, BoundaryRouter reduces inference time by 60.6% and improves performance by 28.6% compared to direct LLM inference; it improves by 37.9% compared to prompt-based routing and by 8.2% compared to retrieval-only routing, demonstrating the advantages of combining experience learning and scoring guidance.

## Universal Value of the Methodology and Application Recommendations

The BoundaryRouter methodology has broad reference significance: the paradigm of small-data startup and continuous optimization is applicable to multiple AI deployment scenarios; it provides a feasible solution for developers to launch quickly without large amounts of historical data and improve gradually with use, which meets real-world deployment needs.

## Limitations and Future Research Directions

Current limitations: The capacity and retrieval efficiency of the experience memory bank may become bottlenecks in large-scale scenarios, and the similarity measurement is not optimal. Future directions: Explore efficient memory structures, accurate similarity calculation, dynamically update the memory bank to adapt to distribution shifts, and expand to multi-agent scenarios.