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

2026-07-25 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, extended

Summary

A study analyzing code model representations used a 2x2 design with Python and Rust across Qwen2.5-Coder-7B and DeepSeek-Coder-V1-6.7B to disentangle task, language, and model influences. Researchers measured 58 Python and 57 Rust grammatical concepts, finding that "what" concepts earn dedicated circuitry is task-determined, with models agreeing (Spearman ρ=0.638 for Python, 0.673 for Rust, both p<10⁻⁷). However, "where" these circuits reside is model-dependent, with Qwen processing concepts late (~L17–19) and DeepSeek early (L6–7), a 12–13 layer difference. "How" circuits grow also varies by model, with Qwen showing an early spike for atomic concepts. Rust constructs received 2–3× more concept-specific circuitry than Python. DeepSeek shared 1.94× more cross-language neurons than Qwen, and Qwen clustered Rust's type-and-trait machinery into a single neuron group (Jaccard 0.535, p<0.001).

Key takeaway

For AI Scientists porting interpretability results across code models, recognize that concept-level knowledge, like which concepts earn circuitry, is transferable. However, layer-indexed techniques, such as fixed-depth probing or patching, are model-specific. You must re-localize these methods for each model, as processing bands and circuit growth patterns diverge significantly (e.g., 12–13 layer offsets). This ensures your interpretability efforts remain accurate and effective.

Key insights

Code model circuit universality applies to representational content, not computational organization.

Principles

• Concept identity is task-dependent and transfers.
• Circuit placement and growth are model-dependent.
• Language design impacts concept-specific circuitry.

Method

The method uses concept and checker prompts, binarizes neuron activations, and marginalizes to extract concept-specific circuits for cross-model comparison.

In practice

• Reuse concept inventories across models.
• Re-localize layer-indexed techniques per model.

Topics

• Code Model Representations
• Mechanistic Interpretability
• Qwen2.5-Coder-7B
• DeepSeek-Coder-V1-6.7B
• Circuit Universality
• Cross-Model Comparison

Code references

• piotrwilam/Atlas2x2

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.