DeepResearch balances broad exploration and deep dives by first assessing the scope and complexity of the query. Initially, it casts a wide net to gather diverse perspectives, identify patterns, and pinpoint high-value sources. For example, when tackling a vague or multifaceted topic like "blockchain applications," the system might scan academic papers, industry reports, and news articles to map the landscape. This phase uses automated tools to filter sources by relevance, publication credibility, and recency. If the initial sweep reveals clusters of information around specific subtopics (e.g., supply chain use cases), the system shifts focus to analyze those areas in depth. Metrics like citation frequency, data consistency, and source authority guide this prioritization. The goal is to avoid premature depth, which risks missing critical context, while ensuring efficiency by not over-investing in low-value sources.
The strategy can be influenced by adjusting parameters tied to the user’s goals and constraints. For instance, setting time limits or specifying the desired output (e.g., a summary vs. a technical analysis) directly impacts the approach. If a user needs quick insights, the system prioritizes breadth, using summarization algorithms to extract key points from many sources. Conversely, a request for detailed technical validation of a specific claim (e.g., "Does AI model X outperform Y in image recognition?") triggers deep dives into methodologies, datasets, and peer reviews. Users can also manually flag priority areas or exclude certain sources, steering the system’s focus. Additionally, feedback loops—where the system refines its approach based on user corrections—improve future decisions. For example, if a user marks certain sources as irrelevant, the system learns to deprioritize similar content in subsequent searches.
Optimizing results requires balancing flexibility and specificity. Developers can integrate domain-specific rules (e.g., prioritizing clinical trials for medical queries) or leverage machine learning to predict which strategy suits a query type. For example, a tool analyzing cybersecurity threats might start broadly to detect emerging attack vectors but automatically drill down into vulnerabilities mentioned across multiple threat reports. Resource constraints (e.g., computational limits) also play a role: a cloud-based system might parallelize broad searches, while edge devices could default to depth on pre-indexed data. Testing different strategies on benchmark queries and measuring outcomes (accuracy, time) helps refine the approach. Ultimately, the system’s effectiveness hinges on aligning its exploration depth with the user’s intent, available data quality, and the problem’s inherent uncertainty.