데이터인텔리전스

Knowledge structuring and indexing: Automatically construct and refine knowledge structures, ontologies, and search indexes from heterogeneous documents. Agent-oriented retrieval interfaces: Design how agents query knowledge bases and receive evidence based on their current task state. Context and search state management: Organize retrieved evidence, search history, and unresolved questions to support continued exploration and reasoning. Web search and search-augmented LLMs: Develop methods for finding and integrating information from the web and other knowledge sources to support grounded reasoning and generation. Retrieval learning and evaluation: Generate queries and develop benchmarks to evaluate retrieval quality and its contribution to downstream agent performance. Data understanding and insight discovery: Reason over tables, time series, and heterogeneous datasets to identify patterns and generate actionable insights. Reasoning- and experience-based prediction: Combine data, external knowledge, and past experience for classification, forecasting, and anomaly detection. Explainable modeling and decision support: Connect predictions and recommendations to relevant evidence, conditions, and relationships in the data. Feedback-driven learning: Improve analytical strategies and decision policies through interaction with tools, environments, and observed outcomes. Personalization benchmarks and evaluation: Assess personalized search, recommendation, and agent behavior across users, tasks, and long-term interactions. User profiling and memory: Infer preferences and goals from interaction histories, and build memories that can be retrieved and updated over time. Personalized reward modeling: Learn user-specific criteria for evaluating and optimizing generated content and