Enterprise Deep Research: Steerable Multi-Agent Deep Research for Enterprise Analytics

• Paper: Enterprise Deep Research: Steerable Multi-Agent Deep Research for Enterprise Analytics
• Authors: Akshara Prabhakar, Roshan Ram, Zixiang Chen, Silvio Savarese, Frank Wang, Caiming Xiong, et al. (8 authors)
• Published: October 2025, arXiv
• Section: Deep Research

Key points

• Proposes Enterprise Deep Research, a steerable multi-agent framework for deep research in enterprise analytics scenarios.
• Addresses long-standing challenges in agentic search: efficiency, scalability, and understanding user intent in the LLM era, where traditional pipelines separate retrieval, ranking, and generation.
• Targets core scenarios including open-domain information access, enterprise knowledge retrieval, conversational search, semantic understanding in recommendation, and end-to-end architectures that combine external knowledge sources with generative models.
• Discusses interfaces with emerging paradigms such as LLM tool calling, reinforcement learning, and multi-agent collaboration.
• Explicitly lists open problems: evaluation trustworthiness, latency and cost, hallucination and safety, and cross-lingual / multimodal extension.

Method overview

The methodology follows a four-step pattern: problem formalization → model/system design → training or construction → inference pipeline.

1. Input & representation: encoding queries, documents, and user context into dense or sparse representations, or structured prompts. 2. Core modules: retrievers, rerankers, planners, memory modules, and tool interfaces, chained or combined per task. 3. Learning strategies: supervised fine-tuning, contrastive learning, distillation, reinforcement learning (including process rewards), and bootstrapped data synthesis. 4. Inference strategies: single-pass retrieval, iterative retrieval, parallel sub-queries, early stopping, and budget control.

Notes for practitioners

• Architecture: cascade retrieval + rerank + generation remains mainstream, but agentic paradigms treat retrieval count and policy themselves as learnable.
• Data: high-quality instruction data and click/session logs matter as much as model design; synthetic data requires guarding against knowledge leakage and distribution shift.
• Evaluation: the gap between offline metrics and online satisfaction is widening; LLM-as-judge should be cross-validated with human evaluation.
• Production: latency, cost, explainability, and safety are hard deployment constraints — do not optimize academic benchmarks alone.

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