Despite the integration of search tools, Deep Search Agents often suffer from a misalignment between reasoning-driven queries and the underlying web indexing structures. Existing frameworks treat the search engine as a static utility, leading to queries that are either too coarse or too granular to retrieve precise evidence. We propose WeDas, a Web Content Distribution Aware framework that incorporates search-space structural characteristics into the agent's observation space. Central to our method is the Query-Result Alignment Score, a metric quantifying the compatibility between agent intent and retrieval outcomes. To overcome the intractability of indexing the dynamic web, we introduce a few-shot probing mechanism that iteratively estimates this score via limited query accesses, allowing the agent to dynamically recalibrate sub-goals based on the local content landscape. As a plug-and-play module, WeDas consistently improves sub-goal completion and accuracy across four benchmarks, effectively bridging the gap between high-level reasoning and low-level retrieval.

翻译：尽管集成了搜索工具，深度搜索智能体仍常面临推理驱动查询与底层网络索引结构之间的错位问题。现有框架将搜索引擎视为静态工具，导致查询要么过于粗略，要么过于精细，难以获取精确证据。我们提出WeDas——一种网络内容分布感知框架，将搜索空间结构特征纳入智能体的观测空间。我们方法的核心是查询-结果对齐分数，该指标量化了智能体意图与检索结果之间的匹配度。为克服动态网络索引的不可行性，我们引入了小样本探测机制，通过有限次查询访问迭代估计该分数，使智能体能够基于局部内容分布动态重新校准子目标。作为即插即用模块，WeDas在四个基准测试中持续提升了子目标完成度与准确率，有效弥合了高层级推理与低层级检索之间的鸿沟。