Towards Understanding What State Space Models Learn About Code

2026-02-06

AI Summary

State Space Models (SSMs) have demonstrated strong performance in code understanding tasks, yet their internal mechanisms lack systematic interpretability. This work proposes SSM-Interpret, the first interpretability framework tailored for SSM-based code models, which integrates frequency-domain analysis with syntactic and semantic probing tasks to systematically diagnose how models capture or forget long-range and short-range dependencies during pretraining and fine-tuning. Our analysis reveals that while SSMs outperform Transformers in pretraining by effectively modeling long-range dependencies, their spectral characteristics shift toward short-range dependencies during fine-tuning, leading to partial forgetting of long-range relationships. Guided by these insights, we refine the model architecture, achieving significant improvements on downstream code understanding tasks.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

State Space Models (SSMs) have emerged as an efficient alternative to the transformer architecture. Recent studies show that SSMs can match or surpass Transformers on code understanding tasks, such as code retrieval, when trained under similar conditions. However, their internal mechanisms remain a black box. We present the first systematic analysis of what SSM-based code models actually learn and perform the first comparative analysis of SSM and Transformer-based code models. Our analysis reveals that SSMs outperform Transformers at capturing code syntax and semantics in pretraining but forgets certain syntactic and semantic relations during fine-tuning on task, especially when the task emphasizes short-range dependencies. To diagnose this, we introduce SSM-Interpret, a frequency-domain framework that exposes a spectral shift toward short-range dependencies during fine-tuning. Guided by these findings, we propose architectural modifications that significantly improve the performance of SSM-based code model, validating that our analysis directly enables better models.

Problem

• State Space Models
• code understanding
• model interpretability
• syntax and semantics
• fine-tuning

Innovation

• State Space Models
• code understanding
• frequency-domain analysis
• model interpretability
• architectural modification