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A Multi-Agent Framework for High-Interaction Terminal Simulation
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:53
摘要
Terminal simulation, framed as a terminal command-level Turing test, is a long-standing problem of symbolic language generation in dialogue and interactive systems. Prior scripted simulators lack the flexibility needed for complex, multi-turn interactions, while LLM-based approaches often misinterpret commands, break output formats, drift from system state, and remain vulnerable to prompt injection. In this work, we propose MANTIS, a terminal simulation framework that improves realism, consistency, and robustness in command-language generation. MANTIS integrates a multi-agent architecture with a filter-based routing model that safely dispatches commands to external tools or an LLM-based agent, enabling support for interactive commands while defending against prompt injection attacks. In addition, we design an agentic file system with history pruning to preserve long-term state consistency. We release three datasets: 28,045 real terminal input-output pairs, a 1,000-session multi-turn interaction dataset, and a 25,849-instance labeled classification dataset. MANTIS outperforms state-of-the-art baselines by more than 9%, achieving over 95% accuracy on multi-turn terminal simulation. The dataset and source code are available at https://github.com/kaiwei666a/MANTIS_Terminal_Simulation
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记录 #1 · 2026-07 · 最新观测版本
A Multi-Agent Framework for High-Interaction Terminal Simulation
Terminal simulation, framed as a terminal command-level Turing test, is a long-standing problem of symbolic language generation in dialogue and interactive systems. Prior scripted simulators lack the flexibility needed for complex, multi-turn interactions, while LLM-based approaches often misinterpret commands, break output formats, drift from system state, and remain vulnerable to prompt injection. In this work, we propose MANTIS, a terminal simulation framework that improves realism, consistency, and robustness in command-language generation. MANTIS integrates a multi-agent architecture with a filter-based routing model that safely dispatches commands to external tools or an LLM-based agent, enabling support for interactive commands while defending against prompt injection attacks. In addition, we design an agentic file system with history pruning to preserve long-term state consistency. We release three datasets: 28,045 real terminal input-output pairs, a 1,000-session multi-turn interaction dataset, and a 25,849-instance labeled classification dataset. MANTIS outperforms state-of-the-art baselines by more than 9%, achieving over 95% accuracy on multi-turn terminal simulation. The dataset and source code are available at https://github.com/kaiwei666a/MANTIS_Terminal_Simulation
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