Web2BigTable: Multi-Agent LLMs Enable Internet-Scale Information Search and Extraction

Introduction: The Dual Challenge Facing Intelligent Search

As large language model (LLM)-driven agent-based web search becomes increasingly prevalent, the industry faces two fundamentally different core requirements: deep reasoning about a single target, and structured aggregation across multiple entities and heterogeneous data sources. Current mainstream systems fall notably short on both fronts. A recent paper published on arXiv (arXiv:2604.27221v1) introduces a novel system called "Web2BigTable," designed to simultaneously tackle the challenges of breadth and depth through a bi-level multi-agent LLM architecture.

Core Technology: Bi-Level Multi-Agent Architecture Design

The central innovation of Web2BigTable lies in its bi-level multi-agent design philosophy. The system decomposes internet-scale information search and extraction tasks into two tiers:

Breadth Layer: Structured Aggregation and Schema Alignment

For breadth-oriented tasks, the system generates outputs aligned with predefined schemas, ensuring broad coverage across a large number of entities and cross-entity consistency. This means the system can consolidate heterogeneous information from across the internet into structured tables — much like building a "BigTable" — where each row represents an entity and each column represents an attribute dimension.

Depth Layer: Long-Chain Search Trajectory Reasoning

For depth-oriented tasks, the system performs coherent reasoning along long and branching search trajectories. This requires agents not only to initiate multi-round search requests but also to maintain the integrity of logical chains as information accumulates, progressively decomposing and deeply analyzing complex problems.

Multi-Agent Collaboration Mechanism

Multiple LLM agents assume different roles within the system — some handle search planning and query generation, others parse web content and extract information, while still others perform cross-source information verification and conflict resolution. This division of labor enables the system to process large-scale tasks in parallel while ensuring specialization at every stage.

Technical Analysis: Why Existing Approaches Fall Short

The main bottlenecks of current agent-based search systems are evident in several areas:

• Insufficient Breadth: Traditional RAG (Retrieval-Augmented Generation) systems typically revolve around a single query, making it difficult to simultaneously cover information needs for dozens or even hundreds of entities, let alone guarantee output format consistency.
• Limited Depth: A single search pass often only scratches the surface. When faced with complex problems requiring multi-hop reasoning or multi-round verification, these systems tend to stop at superficial results.
• Lack of Consistency: When information sources are diverse, contradictions and redundancies across different sources are difficult to effectively identify and resolve.

Web2BigTable addresses these issues through its elegantly designed bi-level architecture, organically combining macro-level task orchestration with micro-level information processing. The breadth layer is responsible for "casting a wide net," ensuring no critical entity is overlooked; the depth layer is responsible for "drilling deep," ensuring that information for each entity is thoroughly verified. While this design approach remains at the frontier of academic exploration, its engineering potential warrants close attention.

Application Prospects: From Academic Research to Industry Deployment

Web2BigTable's application scenarios are remarkably broad. In business intelligence, enterprises can leverage the system to batch-collect competitors' product specifications, pricing strategies, and market dynamics, automatically generating structured comparison reports. In academic research, scholars can use it to rapidly build literature review data tables for a given field. In financial analysis, the system can help analysts extract key financial metrics of publicly listed companies from open-source information and conduct cross-sectional comparisons.

Furthermore, this research provides an important reference for the future development of "Agent-as-a-Service." As LLM capabilities continue to advance, designing efficient multi-agent collaboration frameworks to handle internet-scale complex tasks will become a shared focus for both academia and industry.

Outlook: The Future Direction of Multi-Agent Search

The introduction of Web2BigTable marks a shift in LLM-driven web search from "single-point querying" to "systematic information engineering." In the future, we can expect to see more multi-agent systems that integrate breadth and depth — systems that are not merely search tools but "digital researchers" capable of autonomously completing complex information tasks.

However, significant challenges remain in this direction: the computational costs of large-scale agent coordination, ensuring information extraction accuracy, and handling the dynamic nature of web content all require further exploration in subsequent research. Regardless, Web2BigTable provides a highly inspiring technical framework for the field and deserves continued attention.