---
title: "ReForest: Interpretability Meets Reasoning in Isolation Forest"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2610.37316v1"
permalink: "https://feed.astralor.com/items/itm_19ded9d2002933302c594c3e"
published: "2026-10-27T14:31:40.000Z"
score: 54
channel: "tech"
tags: ["LLM","RAG","ReForest","Interpretability Meets Reasoning","Isolation Forest Statistical","Isolation Forest","They","Statistical"]
---

# ReForest: Interpretability Meets Reasoning in Isolation Forest

## 速览

Statistical anomaly detection methods like Isolation Forest are efficient but suffer from a critical semantic gap. They identify numerical rarities but fail to understand the contextual meaning of data, often missing su…

## 关键点

- Statistical anomaly detection methods like Isolation Forest are efficient but suffer from a critical semantic gap.
- They identify numerical rarities but fail to understand the contextual meaning of data, often missing subtle, context-dependent anomalies.
- Conversely, Large Language Models possess strong semantic reasoning capabilities but are prohibitively expensive and slow for large-scale deployment.

## 全文

Statistical anomaly detection methods like Isolation Forest are efficient but suffer from a critical semantic gap. They identify numerical rarities but fail to understand the contextual meaning of data, often missing subtle, context-dependent anomalies. Conversely, Large Language Models possess strong semantic reasoning capabilities but are prohibitively expensive and slow for large-scale deployment. To bridge this gap, we propose ReForest, a novel framework that synergizes the statistical efficiency of Isolation Forest with the reasoning power of LLMs. ReForest transforms the latent decision paths of Isolation Forest into natural-language explanations, enabling an LLM to refine anomaly detection through an uncertainty-aware dynamic ensemble. This approach allows the model to adaptively weigh statistical and semantic signals based on uncertainty-driven instance-level confidence. Extensive experiments on four real-world datasets (Healthcare, Finance) demonstrate that ReForest significantly outperforms baselines, achieving an average 21% improvement in Recall and a 10% gain in F1-score. Our framework not only improves detection performance but also enhances model transparency by grounding semantic narratives in statistical evidence, providing interpretable rationales essential for high-stakes decision-making.

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