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

• URL: http://arxiv.org/abs/2606.05250v1
• Date: Wed, 03 Jun 2026 12:56:11 GMT
• Title: Towards Persistent Case-Based Memory for Autonomous Data Science: A CBR-Augmented R&D-Agent with a Locally Deployable Small Language Model
• Abstract summary: 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.<n>Cases are stored as structured records with executable code snapshots and quality metadata.<n>Heuristic reuse detection across 108 retrieval events shows high semantic relevance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Architecture-Dependent Causal Transfer of Activation States Across Large Language Models [51.56484100374058]
  Internal language activation states can be transferred causally between different large language models.<n>Injecting projected activations into a target model during generation produces a statistically significant, preregistered causal effect.<n>We conclude that end-to-end activation-state transfer between LLMs, as currently implemented, is architecture-dependent rather than universal.
  arXiv  Detail & Related papers  (2026-08-17T09:53:27Z)
• A-SR: Self-Evolving Agentic LLMs for Symbolic Regression via Hierarchical Coordination [51.06539604709775]
  We propose A-SR, a self-evolving agentic framework that shifts the control unit from expression edits to role-conditioned evidence views.<n>A-SR coordinates formula discovery through routing among coordination protocols, an online evaluator-reward role policy, and state-routed process memory.
  arXiv  Detail & Related papers  (2026-08-05T14:01:10Z)
• Do Transformers Actually Help Intrusion Detection? A Temporal Sequence Evaluation on CIC-IDS2017 [1.2934180951771597]
  We reformulate CIC-IDS 2017 as a temporal intrusion-detection task by constructing ordered flow sequences from network conversations.<n>The central finding is that padding convention, not architecture, determines the Transformer's performance.<n>We advocate leakage-free splits, explicit padding disclosure, and sequence-aware benchmarking as standard practice in future IDS research.
  arXiv  Detail & Related papers  (2026-06-09T16:57:10Z)
• Convergence Theory for Iterative LLM-Based Neural Architecture Search: A Parametric Cross-Entropy Framework with Closed-Form Proxy Reliability [48.83701310501069]
  Large language models (LLMs) are increasingly used as generators in iterative neural architecture search (NAS)<n>We model iterative LLM-NAS as a parametric Cross-Entropy (CE) method over executable programs.<n>We prove six results: (1) iterative LLM fine-tuning on elite architectures is equivalent to the CE update restricted to the LLM family; (2) expected architecture quality is monotonically non-decreasing across cycles; and (3) elite-set probability converges to a fixed point at a geometric rate.
  arXiv  Detail & Related papers  (2026-05-28T15:45:19Z)
• BLAgent: Agentic RAG for File-Level Bug Localization [2.2917707112773593]
  BLAgent is a novel agentic RAG framework for file-level bug localization.<n> BLAgent attains over 78% Top-1 accuracy with open-source models.<n> BLAgent improves end-to-end repair success by over 20%.
  arXiv  Detail & Related papers  (2026-05-18T07:20:13Z)
• Label-Free Cross-Task LoRA Merging with Null-Space Compression [50.63908869296697]
  We introduce Null-Space Compression (NSC) Merging, a label-free, output-agnostic method that sets merge weights from adapter geometry.<n>NSC achieves state-of-the-art performance across twenty heterogeneous vision tasks with balanced gains where prior methods overfit subsets of tasks.
  arXiv  Detail & Related papers  (2026-03-27T11:34:41Z)
• OrgForge-IT: A Verifiable Synthetic Benchmark for LLM-Based Insider Threat Detection [0.0]
  We present a verifiable synthetic benchmark in which a deterministic simulation engine maintains ground truth and language models generate only surface prose.<n>The corpus spans 51 simulated days, 2,904 telemetry records at a 96.4% noise rate, and four detection scenarios designed to defeat single-surface and single-day triage strategies.
  arXiv  Detail & Related papers  (2026-03-23T19:03:53Z)
• Cloud-OpsBench: A Reproducible Benchmark for Agentic Root Cause Analysis in Cloud Systems [51.2882705779387]
  Cloud-OpsBench is a large-scale benchmark that employs a State Snapshot Paradigm to construct a deterministic digital twin of the cloud.<n>It features 452 distinct fault cases across 40 root cause types spanning the full stack.
  arXiv  Detail & Related papers  (2026-02-28T05:04:42Z)
• ShortcutBreaker: Low-Rank Noisy Bottleneck with Global Perturbation Attention for Multi-Class Unsupervised Anomaly Detection [59.89803740308262]
  ShortcutBreaker is a novel unified feature-reconstruction framework for MUAD tasks.<n>It features two key innovations to address the issue of shortcuts.<n>The proposed method achieves a remarkable image-level AUROC of 99.8%, 98.9%, 90.6%, and 87.8% on four datasets.
  arXiv  Detail & Related papers  (2025-10-21T06:51:30Z)
• Ranking Free RAG: Replacing Re-ranking with Selection in RAG for Sensitive Domains [13.58151841630302]
  We propose a novel method METEORA that replaces re-ranking in RAG with a rationale-driven selection approach.<n>We show METEORA improves generation accuracy by 33.34% while using approximately 50% fewer chunks than state-of-the-art re-ranking methods.<n>In adversarial settings, METEORA significantly improves the F1 score from 0.10 to 0.44.
  arXiv  Detail & Related papers  (2025-05-21T20:57:16Z)
• Adaptive and Robust DBSCAN with Multi-agent Reinforcement Learning [53.527506374566485]
  We propose a novel Adaptive and Robust DBSCAN with Multi-agent Reinforcement Learning cluster framework, namely AR-DBSCAN.<n>We show that AR-DBSCAN not only improves clustering accuracy by up to 144.1% and 175.3% in the NMI and ARI metrics, respectively, but also is capable of robustly finding dominant parameters.
  arXiv  Detail & Related papers  (2025-05-07T11:37:23Z)
• Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning [122.62311703151215]
  Divide and Contrast (DaC) aims to connect the good ends of both worlds while bypassing their limitations. DaC divides the target data into source-like and target-specific samples, where either group of samples is treated with tailored goals. We further align the source-like domain with the target-specific samples using a memory bank-based Maximum Mean Discrepancy (MMD) loss to reduce the distribution mismatch.
  arXiv  Detail & Related papers  (2022-11-12T09:21:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.