Точка Синхронізації

Researchers specializing in artificial intelligence published a technical paper on the arXiv preprint server on February 12, 2025, introducing a new framework called the 'Wide and Deep' (W&D) research agent to significantly improve the efficiency of automated information seeking. The proposal addresses a critical bottleneck in current AI systems where agents perform tasks sequentially, leading to prolonged processing times. By integrating parallel tool calling, the researchers aim to expand the 'width' of an agent's reasoning capabilities, allowing it to execute multiple web-based searches and data processing tasks simultaneously rather than waiting for one step to finish before starting the next. The core of the research highlights that while existing deep research agents have become highly effective at complex intellectual tasks through multi-step reasoning, they have primarily evolved through 'depth'—simply increasing the number of sequential steps. This linear approach often results in diminishing returns in terms of speed and can lead to a 'reasoning overhead' that hampers real-time utility. The 'Wide and Deep' architecture seeks to balance this by maintaining the rigorous sequential thinking necessary for accuracy while utilizing parallel processing to broaden the scope of information gathered at each individual stage of the research process. Furthermore, the paper explores the potential of scaling width as a specialized alternative to the current industry focus on ever-deeper sequential thinking models. By enabling parallel tool calls, these research agents can compare multiple sources and verify data points concurrently, mimicking a more sophisticated human research workflow. This development suggests a shift in the AI industry toward more resource-efficient and horizontally scalable systems, potentially reducing the operational costs and time requirements for businesses and scholars who rely on automated AI research tools for high-level intellectual labor.