What, Where, and How: Disentangling the Roles of Task, Language, and Model in Code Model Representations

2026-07-23 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, quick

Summary

A study investigating code model representations found that independently trained language models, specifically Qwen2.5-Coder-7B and DeepSeek-Coder-V1-6.7B, exhibit both agreement and divergence in how they process grammatical concepts in Python and Rust. Using an extended concept-circuit extraction method across a 2x2 design (Python/Rust x Qwen/DeepSeek), researchers measured 58 Python and 57 Rust concepts. The models largely agree on "which" concepts receive dedicated circuitry, with Spearman ρ values of 0.638 for Python and 0.673 for Rust (both p < 10⁻⁷). However, "where" these circuits are located in the model layers and "how" they develop across layers are model-specific; Qwen processes concepts late (~L17-19) with an early spike for atomic concepts, while DeepSeek processes them early (L6-7) without such a spike. Notably, Rust constructs receive 2-3x more concept-specific circuitry than Python equivalents, and both models share neurons between languages, with DeepSeek sharing 1.94x more than Qwen. Qwen also forms a tight neuron cluster for nine Rust type-and-trait keywords.

Key takeaway

For AI Scientists and Machine Learning Engineers developing or evaluating code models, understanding that representational content is universal while computational organization is model-specific is crucial. If you are designing multilingual code models, consider that different architectures like Qwen and DeepSeek exhibit distinct circuit locations and growth patterns, and vary in cross-language neuron sharing. This insight suggests you should tailor interpretability and optimization strategies to specific model architectures, rather than assuming uniform internal representations.

Key insights

Code model representations show content universality but diverge in computational organization across models and languages.

Principles

• Task dictates which code concepts form dedicated circuits.
• Model architecture determines circuit location and growth.
• Language complexity influences circuit allocation.

Method

An extended concept-circuit extraction method was applied to a 2x2 design (Python/Rust x Qwen2.5-Coder-7B/DeepSeek-Coder-V1-6.7B) to inventory grammatical concepts and analyze their representation.

In practice

• Analyze model-specific circuit locations for interpretability.
• Compare circuit growth profiles across different architectures.
• Investigate cross-language neuron sharing for multilingual models.

Topics

• Language Model Interpretability
• Concept Circuits
• Qwen2.5-Coder-7B
• DeepSeek-Coder-V1-6.7B
• Multilingual Code Models
• Code Representation

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.