ADu2021/agentic-r-learning-to-retrieve-for-agentic-search
skills/skillxiv-v0.0.2-claude-opus-4.6/agentic-r-learning-to-retrieve-for-agentic-search/SKILL.md
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...
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SKILL.md
- 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
- 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.
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