In hierarchical reasoning, failures often originate at intermediate decision points where the agent commits to a wrong branch without recognizing that it lacks critical information. Rather than treating clarification as an external uncertainty trigger, we propose ACTION-RATING, a formulation that places it inside the agent's action space on a shared ordinal scale with navigation, so that asking competes directly with acting at every decision point and help-seeking becomes observable at intermediate states. Two structurally distinct information-seeking modes emerge from the agent's own ratings: mandatory (no viable branch) and opportunistic (residual uncertainty despite a leading candidate). On Harmonized Tariff Schedule classification (30,000-node taxonomy, three benchmarks, 9~LLMs across 4 families), we observe a regime shift from mandatory to opportunistic clarification, with Information-Seeking Effectiveness (ISE), a local diagnostic defined as the fraction of help interactions followed by a correct next navigation step (not a final-task metric), rising from 50% to 74%. Three diagnostic contrasts fail to reproduce this structure. A separability test shows that the information-seeking pattern (mode split, ISE ranking) persists when answer quality is degraded (-18.8% accuracy), supporting an empirical separation between where an agent seeks help and the quality of the help it receives. Under the controlled answer channel, accuracy gains reach +16.2% at 10-digit; we read this as an upper bound on what better localization could unlock, not a deployment estimate.

翻译：在分层推理中，失败通常源于中间决策点——智能体在未意识到自身缺乏关键信息时便错误地选定某个分支。我们并非将澄清视为外部不确定性触发器，而是提出ACTION-RATING这一框架，将其置于智能体的动作空间内，并与导航动作共享统一的有序量纲尺度。由此，在每一个决策点上，“提问”与“行动”直接竞争，使得在中间状态下可观测到求助行为。基于智能体自身评分，涌现出两种结构迥异的信息寻求模式：强制模式（无可行分支）与机会模式（尽管有领先候选，仍存在残余不确定性）。在协调关税表分类任务（含30,000节点分类体系、三个基准测试，覆盖4个族系的9种大语言模型）中，我们观察到从强制澄清向机会澄清的模式转变：局部诊断指标信息寻求效能（定义为求助交互后正确导航至下一步的比例，非任务最终指标）从50%提升至74%。三项诊断性对比实验均未能复现这一结构特征。可分离性测试表明，当答案质量下降（准确率降低18.8%）时，信息寻求模式（模式分化、信息寻求效能排序）依然保持稳定，这支持了“智能体寻求帮助的位置”与“所获帮助的质量”之间的经验性分离。在受控答案通道下，10位细度分类的准确率提升达16.2%；我们将此解读为更优定位策略所能释放的性能上限，而非部署环境下的实际估计值。