agentic-r-learning-to-retrieve-for-agentic-search

name	agentic-r-learning-to-retrieve-for-agentic-search
title	Agentic-R: Learning to Retrieve for Agentic Search
version	0.0.2
engine	skillxiv-v0.0.2-claude-opus-4.6
license	MIT
url	https://arxiv.org/abs/2601.11888
keywords	["Agent","Learning"]
description	Agentic search has recently emerged as a powerful paradigm, where an agent interleaves multi-step reasoning with on-demand retrieval to solve complex questions. Despite its success, how to design a retriever for agentic search remains largely underexplored. Existing search agents typically rely on similarity-based retrievers, while similar passages are not always useful for final answer generation. In this paper, we propose a novel retriever training framework tailored for agentic search. Unlike...

Problem

Agentic-R addresses key challenges in autonomous agent development. This paper provides solutions for evaluating, building, or improving agent systems.

Key Approach

The paper introduces a novel framework, methodology, or benchmark for agentic-r. The core contributions include:

1. Systematic framework or benchmark for agent evaluation and development
2. Empirical findings on agent performance, efficiency, or capabilities
3. Generalizable principles applicable across domains

When to Use

Use this skill when you need to:

• Evaluate or benchmark autonomous agent systems
• Understand best practices in agent design and evaluation
• Learn empirical results on agent performance
• Improve agent efficiency, reasoning, or capabilities

When NOT to Use

• For non-agent-related tasks
• When seeking quick implementation code (see the paper for details)
• For general knowledge unrelated to autonomous agents

Resources

• ArXiv Abstract: https://arxiv.org/abs/2601.11888
• Full PDF: https://arxiv.org/pdf/2601.11888
• HTML Version: https://arxiv.org/html/2601.11888

See the paper for comprehensive methodology, experimental protocols, benchmarks, and implementation details.