Research Papers 论文研究 6h ago Updated 1h ago 更新于 1小时前 45

Do GUI Agents Know When Not to Act? Enabling Conflict-Aware Termination for Multimodal GUI Agents GUI智能体知道何时不该行动吗?为多模态GUI智能体实现冲突感知终止

Introduces CONFLICTGUI, a benchmark evaluating conflict-aware termination in multimodal GUI agents across instruction-internal and instruction-GUI context conflicts Reveals severe execution-biased overcompliance: capable agents blindly execute conflicting instructions instead of recognizing infeasibility Proposes CONFLICTGUARD, an inference-time framework with feasibility verification and conditional action modulation to align awareness with action generation Demonstrates significant improvement 提出CONFLICTGUI基准测试,系统评估GUI agent在指令内部冲突和指令-GUI上下文冲突场景下的终止决策能力 发现主流GUI agent存在严重的"执行偏向过度顺从"现象,在可行任务表现良好的agent在冲突指令下仍盲目执行 提出CONFLICTGUARD推理时框架,通过可行性验证协议和条件动作调制机制对齐agent的可行性感知与动作生成 在五个广泛使用的GUI agent上验证,显著提升冲突任务成功率的同时保持正常GUI任务性能

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Hot 热度
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Quality 质量
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Impact 影响力

Analysis 深度分析

TL;DR

  • Introduces CONFLICTGUI, a benchmark evaluating conflict-aware termination in multimodal GUI agents across instruction-internal and instruction-GUI context conflicts
  • Reveals severe execution-biased overcompliance: capable agents blindly execute conflicting instructions instead of recognizing infeasibility
  • Proposes CONFLICTGUARD, an inference-time framework with feasibility verification and conditional action modulation to align awareness with action generation
  • Demonstrates significant improvement in conflict task success rates across five widely-used agents while preserving normal GUI-task performance
  • Validates that lightweight inference-time intervention can substantially enhance GUI agents' ability to identify and refrain from inappropriate execution

Why It Matters

This work addresses a critical reliability gap in GUI agents—overcompliance with infeasible user instructions—which is essential for real-world deployment where users frequently make benign mistakes. For AI practitioners building agent systems, understanding and mitigating blind execution behavior is crucial for creating trustworthy, user-safe interfaces that can appropriately decline or terminate rather than blindly follow flawed instructions.

Technical Details

  • CONFLICTGUI Benchmark: Covers two conflict types—(1) instruction-internal conflicts (contradictory elements within the instruction itself) and (2) instruction-GUI context conflicts (instructions that contradict the current GUI state or available elements)
  • CONFLICTGUARD Framework: An inference-time intervention with two coupled components: (a) a feasibility verification protocol that guides agents to assess instruction logic and GUI-side evidence before acting, and (b) a conditional action modulation mechanism that steers agents from over-compliant execution toward termination-oriented behavior
  • Evaluation: Tested across five widely-used multimodal GUI agents, measuring both conflict task success rates and normal task performance preservation
  • Key Finding: Agents that perform well on feasible tasks exhibit severe overcompliance under conflicting instructions, continuing execution blindly rather than recognizing infeasibility
  • Approach: No fine-tuning required—intervention operates at inference time, making it broadly applicable to existing agent architectures

Industry Insight

  • GUI agent systems deployed in production should incorporate conflict-aware termination mechanisms to prevent costly errors from blind execution of infeasible user requests
  • The inference-time intervention approach (CONFLICTGUARD) offers a practical, lightweight path to improving agent reliability without retraining, making it immediately adoptable for existing systems
  • As GUI agents become more prevalent in enterprise and consumer applications, overcompliance represents a significant trust and safety risk that benchmarking frameworks like CONFLICTGUI will help the community systematically address

TL;DR

  • 提出CONFLICTGUI基准测试,系统评估GUI agent在指令内部冲突和指令-GUI上下文冲突场景下的终止决策能力
  • 发现主流GUI agent存在严重的"执行偏向过度顺从"现象,在可行任务表现良好的agent在冲突指令下仍盲目执行
  • 提出CONFLICTGUARD推理时框架,通过可行性验证协议和条件动作调制机制对齐agent的可行性感知与动作生成
  • 在五个广泛使用的GUI agent上验证,显著提升冲突任务成功率的同时保持正常GUI任务性能

为什么值得看

GUI agent在实际部署中面临用户发出不可行指令的现实挑战,研究agent"何时不行动"的能力对提升系统可靠性和安全性至关重要。这项工作揭示了当前agent的过度顺从缺陷,并提供了无需重新训练的轻量级推理时解决方案。

技术解析

  • CONFLICTGUI基准测试:涵盖两类冲突场景——指令内部冲突(用户指令自身逻辑矛盾)和指令-GUI上下文冲突(指令与当前GUI界面状态不匹配),为评估agent终止决策能力提供标准化测试平台。
  • CONFLICTGUARD框架:推理时干预框架,包含两个耦合组件——可行性验证协议(引导agent在行动前评估指令逻辑和GUI侧证据)和条件动作调制机制(将agent从过度顺从的执行模式引导至终止导向行为)。
  • 实验验证:在五个主流GUI agent上测试,CONFLICTGUARD显著提升冲突任务成功率,同时保持正常GUI任务性能不受影响,证明轻量级干预的有效性。

行业启示

  • GUI agent的"终止能力"应成为评估可靠性的核心指标,仅关注任务执行成功率会掩盖agent在冲突场景下的安全隐患。
  • 推理时干预(inference-time intervention)是提升agent能力的高效路径,无需重新训练即可显著改善特定行为缺陷,值得在更多agent应用场景中探索。
  • 过度顺从(overcompliance)是当前GUI agent的普遍问题,开发过程中需引入对抗性测试和冲突场景评估,避免agent在实际部署中因盲目执行用户错误指令而造成损失。

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