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Rethinking Deep Research from the Perspective of Web Content Distribution Matching

Zixuan Yu, Zhenheng Tang, Tongliang Liu, Chengqi Zhang, Xiaowen Chu, Bo Han

TL;DR

This work proposes WeDas, a Web Content Distribution Aware framework that incorporates search-space structural characteristics into the agent's observation space, and introduces 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.

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.

Rethinking Deep Research from the Perspective of Web Content Distribution Matching

TL;DR

This work proposes WeDas, a Web Content Distribution Aware framework that incorporates search-space structural characteristics into the agent's observation space, and introduces 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.

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.
Paper Structure (25 sections, 1 theorem, 14 equations, 4 figures, 4 tables, 2 algorithms)

This paper contains 25 sections, 1 theorem, 14 equations, 4 figures, 4 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Search Engine
  4. Tool-Augmented Large Language Model
  5. Deep Search Agent
  6. Preliminary
  7. Large Language Model
  8. Search Engine
  9. Deep Search Agent Workflow
  10. Query-Result Alignment Score
  11. Definition of Information Gain
  12. Measuring the information gain of the search process
  13. Methodology
  14. Web Content Distribution Aware Search
  15. Distribution Estimation via Few-Shot Query Probing
  16. ...and 10 more sections

Key Result

Proposition 4.4

Under Assumptions ass:query_no_info--ass:delta_bounds, the Expected Information Gain is bounded above by the expected relevance:

Figures (4)

  • Figure 1: Distributions of query--observation alignment metrics (TF-IDF, Jaccard, and normalized Levenshtein similarity) for successful vs. failed trajectories, highlighting the structural misalignment between agent-generated queries and retrieved web content.
  • Figure 2: Framework of Web Content Distribution Aware Search (WeDAS)
  • Figure 3: System instruction for the Candidate Generation Operator ($\Gamma_{\text{gen}}$).
  • Figure 4: System instruction for the Meta-Evaluator ($\mathcal{M}_\theta$).

Theorems & Definitions (2)

  • Proposition 4.4: EIG Upper Bound via Relevance
  • proof : Proof sketch