# Universal Deep Research: Bring Your Own Model and Strategy

arXiv: https://arxiv.org/abs/2509.00244 · HTML: https://mlsanity.com/paper/2509.00244/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 1 of its 26 references that are in the map.
- First posted on arXiv 29 Aug 2025.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 1 of 1)

- [The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey](https://mlsanity.com/paper/2404.11584/) (2024)

## Built on by (most cited first, 4 of 4)

- [Lost in the Maze: Overcoming Context Limitations in Long-Horizon Agentic Search](https://mlsanity.com/paper/2510.18939/) (2025)
- [Deep Research with Open-Domain Evaluation and Multi-Stage Guardrails for Safety](https://mlsanity.com/paper/2510.10994/) (2025)
- [Procedural Knowledge Improves Agentic LLM Workflows](https://mlsanity.com/paper/2511.07568/) (2025)
- [Experiments in Agentic AI for Science](https://mlsanity.com/paper/2605.26305/) (2026)

## Abstract (arXiv)

Deep research tools are among the most impactful and most commonly encountered agentic systems today. We observe, however, that each deep research agent introduced so far is hard-coded to carry out a particular research strategy using a fixed choice of tools. We introduce Universal Deep Research (UDR), a generalist agentic system that wraps around any language model and enables the user to create, edit, and refine their own entirely custom deep research strategies without any need for additional training or finetuning. To showcase the generality of our system, we equip UDR with example minimal, expansive, and intensive research strategies, and provide a user interface to facilitate experimentation with the system.