| 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:
- Systematic framework or benchmark for agent evaluation and development
- Empirical findings on agent performance, efficiency, or capabilities
- 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
See the paper for comprehensive methodology, experimental protocols, benchmarks, and implementation details.
