遏制大语言模型驱动的多机器人系统中被操纵声明引发的行为级联
Containing Behavioral Cascades from Manipulated Claims in LLM-Powered Multi-Robot Systems
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中文总结 AI 辅助
针对LLM驱动的多机器人系统易受语义操纵引发行为级联的问题，提出主动验证框架，通过“验证-适应-保持”计划遏制下游影响，实验证明其有效限制级联物理后果。
中文摘要 AI 辅助
大语言模型（LLM）驱动的多机器人系统易受语义操纵的影响：一个被接受的虚假世界状态声明可能触发全舰队范围的行为级联，导致不必要的重新规划、路径成本增加、拥堵或明显的任务不可行。在成功操纵的条件下，我们提出了一种主动验证框架，在其下游效应传播到整个舰队之前加以遏制。一个专门的验证模块生成结构化的“验证-适应-保持”计划：选定的机器人检查关键区域，有限子集在必要时临时适应，其余机器人保留其可信计划。我们在一个多机器人运输环境中评估该框架，使用注入的虚假障碍声明，涵盖不同的影响和团队规模。评估指标包括级联遏制、总成本、完工时间和覆盖率。结果表明，将妥协后验证视为团队级规划问题而非二元信任决策，能有效限制语义操纵的级联物理后果。
英文摘要
Large language model (LLM)-powered multi-robot systems are vulnerable to semantic manipulation: an accepted false world-state claim can trigger a fleet-wide behavioral cascade, causing unnecessary replanning, increased path costs, congestion, or apparent mission infeasibility. Conditioning on a successful manipulation, we propose an active verification framework that contains its downstream effects before they propagate across the fleet. A dedicated verification module generates a structured Verify-Adapt-Hold plan: selected robots inspect consequential regions, a limited subset provisionally adapts when necessary, and the remaining robots retain their trusted plans. We evaluate the framework in a multi-robot transportation environment using injected false obstacle claims across different impacts and team sizes. Evaluation measures cascade containment, Sum-of-Costs, makespan, and coverage ratio. Results show that treating post-compromise verification as a team-level planning problem, rather than a binary trust decision, effectively limits the cascading physical consequences of semantic manipulation.
发表机构
- Indiana University Bloomington（印第安纳大学布卢明顿分校）
机构由 AI 辅助整理，请以论文原文为准。
