DeepResearch manages the exploration of new pages and consolidation of information through a balanced, iterative process. The system prioritizes exploration when it detects gaps in existing data or when user queries require fresh information. For example, if a user requests a report on a rapidly changing topic like AI regulation, DeepResearch first scans trusted sources (government sites, academic journals) using targeted web crawlers. These crawlers follow predefined rules to avoid irrelevant content, focusing on domains and keywords specified in the query. However, the system also allocates a portion of its resources to discover new domains or lesser-known sources that might contain critical insights, using metrics like backlink quality or semantic relevance to prioritize them. This ensures coverage of both established and emerging perspectives.
Once initial data is gathered, DeepResearch shifts toward consolidation. It uses natural language processing (NLP) pipelines to extract key entities, relationships, and themes. For instance, when compiling a report on renewable energy trends, the system might cluster documents by subtopics like solar panel efficiency or policy incentives, then cross-reference findings against existing databases to resolve contradictions. Redundancy checks filter duplicate information, while confidence scoring (based on source reliability and data consistency) ranks facts by trustworthiness. This phase also involves structuring data into templates—such as comparative tables for technology benchmarks or timelines for regulatory updates—to make the output actionable. Developers can customize these templates via APIs to align with specific use cases.
The trade-off between exploration and consolidation is dynamically adjusted using feedback loops. If the system detects low confidence in consolidated data (e.g., conflicting statistics from multiple sources), it triggers additional exploration to fill gaps. Conversely, when exploration yields diminishing returns (e.g., repeated content across pages), consolidation takes precedence. For example, in a cybersecurity threat analysis, DeepResearch might initially crawl forums and vulnerability databases broadly but switch to deeper analysis once it identifies a recurring exploit pattern. Developers can fine-tune this balance through parameters like crawl depth limits, confidence thresholds, or time budgets for each phase, ensuring the system adapts to scenarios where speed, accuracy, or comprehensiveness is prioritized.