# Towards Persistent Case-Based Memory for Autonomous Data Science: A CBR-Augmented R&D-Agent with a Locally Deployable Small Language Model

arXiv: https://arxiv.org/abs/2606.05250 · HTML: https://mlsanity.com/paper/2606.05250/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 24 of its 43 references that are in the map.
- First posted on arXiv 3 Jun 2026.

## 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, 24 of 24)

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- [Data Interpreter: An LLM Agent For Data Science](https://mlsanity.com/paper/2402.18679/) (2024)
- [Memento: Fine-tuning LLM Agents without Fine-tuning LLMs](https://mlsanity.com/paper/2508.16153/) (2025)
- [DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning](https://mlsanity.com/paper/2402.17453/) (2024)
- [Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science](https://mlsanity.com/paper/1603.06212/) (2016)
- [CBR-RAG: Case-Based Reasoning for Retrieval Augmented Generation in LLMs for Legal Question Answering](https://mlsanity.com/paper/2404.04302/) (2024)
- [AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions](https://mlsanity.com/paper/2410.20424/) (2024)
- [MLE-STAR: Machine Learning Engineering Agent via Search and Targeted Refinement](https://mlsanity.com/paper/2506.15692/) (2025)
- [Toward Ultra-Long-Horizon Agentic Science: Cognitive Accumulation for Machine Learning Engineering](https://mlsanity.com/paper/2601.10402/) (2026)
- [ML-Agent: Reinforcing LLM Agents for Autonomous Machine Learning Engineering](https://mlsanity.com/paper/2505.23723/) (2025)
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- [MARS: Modular Agent with Reflective Search for Automated AI Research](https://mlsanity.com/paper/2602.02660/) (2026)
- [AutoMind: Adaptive Knowledgeable Agent for Automated Data Science](https://mlsanity.com/paper/2506.10974/) (2025)
- [Review of Case-Based Reasoning for LLM Agents: Theoretical Foundations, Architectural Components, and Cognitive Integration](https://mlsanity.com/paper/2504.06943/) (2025)
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## Abstract (arXiv)

Most top-performing autonomous data-science agents rely on frontier cloud models and lack persistent, cross-session memory. This paper addresses two open gaps: (1) the underexplored use of formally structured, quality-controlled Case-Based Reasoning (CBR) case bases coupling symbolic case records with executable code artefacts; and (2) the untested viability of Small Language Models (SLMs) as locally deployable agent backbones. We present CBR-augmented R&D-Agent, integrating a persistent CBR layer into Microsoft's R&D-Agent framework with a custom backend for Gemma 4 31B Dense -- the first published end-to-end evaluation of Gemma 4 as an autonomous data-science agent backbone. The CBR layer overrides three R&D loop phases via a surgical subclass toggled by a single environment variable. Cases are stored as structured records with executable code snapshots and quality metadata; a five-gate quality filter and a heuristic reuse-detection mechanism assess knowledge transfer by combining embedding similarity, code-fingerprint overlap, and injection provenance. Evaluated on two Kaggle competitions (NOMAD 2018, Spaceship Titanic) with four seeds over eight improvement loops each, CBR achieves directionally higher accuracy than the CBR-disabled baseline on Spaceship Titanic (0.8147 vs. 0.8098, d = -1.41) with substantially lower variance. Heuristic reuse detection across 108 retrieval events shows high semantic relevance (mean embedding similarity 0.882) alongside variable structural proximity (mean code-fingerprint similarity 0.305), consistent with conceptual guidance rather than verbatim code copying.