When Users Change Their Mind: Evaluating Interruptible Agents in Long-Horizon Web Navigation
As LLM agents transition from short, static problem solving to executing complex, long-horizon tasks in dynamic environments, the ability to handle user interruptions, such as adding requirement or revising goals, during mid-task execution is becoming a core requirement for realistic deployment. However, existing benchmarks largely assume uninterrupted agent behavior or study interruptions only in short, unconstrained language tasks. In this paper, we present the first systematic study of interruptible agents in long-horizon, environmentally grounded web navigation tasks, where actions induce persistent state changes. We formalize three realistic interruption types, including addition, revision, and retraction, and introduce InterruptBench, a benchmark derived from WebArena-Lite that synthesizes high-quality interruption scenarios under strict semantic constraints. Using a unified interruption simulation framework, we evaluate six strong LLM backbones across single- and multi-turn interruption settings, analyzing both their effectiveness in adapting to updated intents and their efficiency in recovering from mid-task changes. Our results show that handling user interruptions effectively and efficiently during long-horizon agentic tasks remains challenging for powerful large-scale LLMs. Code and dataset are available at https://github.com/HenryPengZou/InterruptBench.
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Henry Peng Zou
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Chunyu Miao
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Wei-Chieh Huang
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Yankai Chen
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Yue Zhou
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Hanrong Zhang
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Yaozu Wu
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Liancheng Fang
claimed_verified
2026-04-07T08:45:55.253Z
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Liancheng Fang
Liancheng2
user
Liancheng2
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Zhengyao Gu
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Zhen Zhang
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Kening Zheng
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Fangxin Wang
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Yi Nian
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Shanghao Li
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Wenzhe Fan
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Langzhou He
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Weizhi Zhang
69cebc79e02fdd68b91b7969
Xue Liu
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Philip S. Yu
When Users Change Their Mind: Evaluating Interruptible Agents in Long-Horizon Web Navigation
As LLM agents transition from short, static problem solving to executing complex, long-horizon tasks in dynamic environments, the ability to handle user interruptions, such as adding requirement or revising goals, during mid-task execution is becoming a core requirement for realistic deployment. However, existing benchmarks largely assume uninterrupted agent behavior or study interruptions only in short, unconstrained language tasks. In this paper, we present the first systematic study of interruptible agents in long-horizon, environmentally grounded web navigation tasks, where actions induce persistent state changes. We formalize three realistic interruption types, including addition, revision, and retraction, and introduce InterruptBench, a benchmark derived from WebArena-Lite that synthesizes high-quality interruption scenarios under strict semantic constraints. Using a unified interruption simulation framework, we evaluate six strong LLM backbones across single- and multi-turn interruption settings, analyzing both their effectiveness in adapting to updated intents and their efficiency in recovering from mid-task changes. Our results show that handling user interruptions effectively and efficiently during long-horizon agentic tasks remains challenging for powerful large-scale LLMs. Code and dataset are available at https://github.com/HenryPengZou/InterruptBench.
Large language model agents struggle to effectively and efficiently handle user interruptions during long-horizon web navigation tasks despite their strong performance in other domains.
LLM agents long-horizon tasks dynamic environments user interruptions web navigation InterruptBench semantic constraints interruption simulation framework agent behavior task adaptation