arXiv cs.MA· Yuran Wu, Mengkang Hu, Qionglin Qiu, Fanqing Meng, Qiguang Chen, Yuhang Zhou, Qiudan Zhang, Xu Wang·· 3 天前AI 评分40
LAGO：将专用语言智能体作为可复用可增长选项
LAGO: Language Agents with Reusable Growing Options
AI 导读
LAGO 将专用语言智能体视为可复用的时序扩展选项，由元智能体从持久库中检索或新建选项、派发子任务并验证结果，库可跨任务增长而无需更新模型权重。
正文 · 原文
Abstract:Long-horizon tasks expose a weakness of monolithic tool-use loops: one agent must decompose, execute, and verify a task within a single trajectory. We introduce LAGO, which treats specialized language agents as reusable temporally extended options. A meta-agent retrieves options from a persistent library or spawns new ones, dispatches sub-tasks, verifies results, and admits successful novel options. The library grows across tasks without updating model weights. On 500 SWE-bench Verified tasks, frozen Qwen3.5-35B-A3B in both roles resolves 58.6% under single-attempt evaluation with the official Docker evaluator. On a fixed 150-task subset, we re-run mini-SWE-agent, Terminus 2, and LAGO with the same policy, temperature, task IDs, and evaluator; their resolve rates are 49.3%, 57.3%, and 60.3%, respectively. Cross-model experiments provide evidence consistent with a role for decomposition quality. A 50-task component ablation finds four fewer resolved tasks without decomposition, two fewer with shuffled reused content, and six fewer without the library. These results support reusable agent-options as a practical abstraction for task streams, while leaving component-level causality task- and model-dependent.
| Comments: | 16 pages, 7 figures. Equal contribution: Yuran Wu, Mengkang Hu. Corresponding author: Xu Wang |
| Subjects: | Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2610.19843 [cs.AI] |
| (or arXiv:2610.19843v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.19843 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yuran Wu [view email]
[v1]
Wed, 14 Oct 2026 09:12:47 UTC (573 KB)
来源：arXiv cs.MA · arxiv.org