Rethinking Deep Research from the Perspective of Web Content Distribution Matching

• URL: http://arxiv.org/abs/2603.07241v1
• Date: Sat, 07 Mar 2026 14:51:17 GMT
• Title: Rethinking Deep Research from the Perspective of Web Content Distribution Matching
• Abstract summary: We propose WeDas, a Web Content Distribution Aware framework that incorporates search-space structural characteristics into the agent's observation space.<n>We introduce a few-shot probing mechanism that iteratively estimates the Query-Result Alignment Score.<n>As a plug-and-play module, WeDas consistently improves sub-goal completion and accuracy across four benchmarks.
• Score: 86.90255285277256
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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