DARE: Aligning LLM Agents with the R Statistical Ecosystem via Distribution-Aware Retrieval
#DARE #LLM agents #R statistical ecosystem #distribution-aware retrieval #AI alignment #statistical computing #retrieval enhancement
📌 Key Takeaways
- DARE introduces a method to align LLM agents with the R statistical ecosystem using distribution-aware retrieval.
- The approach enhances LLM agents' ability to interact with R's statistical functions and data structures.
- Distribution-aware retrieval improves accuracy and relevance in retrieving R-specific information for LLMs.
- This alignment aims to bridge the gap between LLMs and specialized statistical computing environments.
📖 Full Retelling
arXiv:2603.04743v1 Announce Type: cross
Abstract: Large Language Model (LLM) agents can automate data-science workflows, but many rigorous statistical methods implemented in R remain underused because LLMs struggle with statistical knowledge and tool retrieval. Existing retrieval-augmented approaches focus on function-level semantics and ignore data distribution, producing suboptimal matches. We propose DARE (Distribution-Aware Retrieval Embedding), a lightweight, plug-and-play retrieval model
🏷️ Themes
AI Integration, Statistical Computing
📚 Related People & Topics
AI alignment
Conformance of AI to intended objectives
In the field of artificial intelligence (AI), alignment aims to steer AI systems toward a person's or group's intended goals, preferences, or ethical principles. An AI system is considered aligned if it advances the intended objectives. A misaligned AI system pursues unintended objectives.
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--> Computer Science > Information Retrieval arXiv:2603.04743 [Submitted on 5 Mar 2026] Title: DARE: Aligning LLM Agents with the R Statistical Ecosystem via Distribution-Aware Retrieval Authors: Maojun Sun , Yue Wu , Yifei Xie , Ruijian Han , Binyan Jiang , Defeng Sun , Yancheng Yuan , Jian Huang View a PDF of the paper titled DARE: Aligning LLM Agents with the R Statistical Ecosystem via Distribution-Aware Retrieval, by Maojun Sun and 7 other authors View PDF HTML Abstract: Large Language Model agents can automate data-science workflows, but many rigorous statistical methods implemented in R remain underused because LLMs struggle with statistical knowledge and tool retrieval. Existing retrieval-augmented approaches focus on function-level semantics and ignore data distribution, producing suboptimal matches. We propose DARE (Distribution-Aware Retrieval Embedding), a lightweight, plug-and-play retrieval model that incorporates data distribution information into function representations for R package retrieval. Our main contributions are: RPKB, a curated R Package Knowledge Base derived from 8,191 high-quality CRAN packages; DARE, an embedding model that fuses distributional features with function metadata to improve retrieval relevance iii) RCodingAgent, an R-oriented LLM agent for reliable R code generation and a suite of statistical analysis tasks for systematically evaluating LLM agents in realistic analytical scenarios. Empirically, DARE achieves an NDCG at 10 of 93.47%, outperforming state-of-the-art open-source embedding models by up to 17% on package retrieval while using substantially fewer parameters. Integrating DARE into RCodingAgent yields significant gains on downstream analysis tasks. This work helps narrow the gap between LLM automation and the mature R statistical ecosystem. Comments: 24 pages,7 figures, 3 tables Subjects: Information Retrieval (cs.IR) ; Artificial Intelligence (cs.AI); Computation and Language (cs.CL) Cite as: arXiv:2603.04743 [cs....
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