Deep research agents, AI systems that autonomously gather, synthesize, and report on complex topics, represent a significant advance in information synthesis, yet ensuring the completeness of their outputs remains an open challenge. A key bottleneck is query generation: current systems decompose research questions into subqueries via prompt engineering alone, offering no formal guarantees on diversity or coverage, which leads to redundant retrieval and gaps in the resulting reports. This paper presents HERO (High Enrichment Retrieval Orchestrator), a hierarchical deep research architecture that addresses this limitation through two complementary mechanisms. First, submodular optimization via a facility location objective provides mathematically grounded control over the relevance–diversity trade-off during query selection, replacing ad-hoc generation with provably diverse query sets. Second, a hierarchical enrichment stage independently analyzes each subquery pipeline’s intermediate synthesis for information gaps and issues targeted follow-up queries, enabling adaptive depth without cross-pipeline interference. We evaluate HERO across academic (ScholarQABench) and general-domain (DeepResearchGym) benchmarks. HERO achieves state-of-the-art coverage (Key Point Recall: 67.63), grounding (Citation F1: 91.57), and presentation quality on DeepResearchGym, and the highest scores on multi-paper synthesis tasks in ScholarQABench.

Existing dense retrieval models for scientific documents have been optimized for either retrieval by short queries, or for document similarity, but usually not for both. In this paper, we explore the space of combining multiple objectives to achieve a single representation model that presents a good balance between both modes of dense retrieval, combining the relevance judgements from MS MARCO with the citation similarity of SPECTER, and the self-supervised objective of independent cropping. We also consider the addition of training data from document co-citation in a sentence context and domain-specific synthetic data. We show that combining multiple objectives yields models that generalize well across different benchmark tasks, improving up to 73% over models trained on a single objective.

Expert search aims to find and rank experts based on a user’s query. In academia, retrieving experts is an efficient way to navigate through a large amount of academic knowledge. Here, we study how different distributed representations of academic papers (i.e. embeddings) impact academic expert retrieval. We use the Microsoft Academic Graph dataset and experiment with different configurations of a document-centric voting model for retrieval. In particular, we explore the impact of the use of contextualized embeddings on search performance. We also present results for paper embeddings that incorporate citation information through retrofitting. Additionally, experiments are conducted using different techniques for assigning author weights based on author order. We observe that using contextual embeddings produced by a transformer model trained for sentence similarity tasks produces the most effective paper representations for document-centric expert retrieval. However, retrofitting the paper embeddings and using elaborate author contribution weighting strategies did not improve retrieval performance.

To provide AI researchers with modern tools for dealing with the explosive growth of the research literature in their field, we introduce a new platform, AI Research Navigator, that combines classical keyword search with neural retrieval to discover and organize relevant literature. The system provides search at multiple levels of textual granularity, from sentences to aggregations across documents, both in natural language and through navigation in a domain specific Knowledge Graph. We give an overview of the overall architecture of the system and of the components for document analysis, question answering, search, analytics, expert search, and recommendations.