# Researchers propose CLIC to compare coding behaviors of large language models

Digest AI · Research · published 2026-09-22T04:00:00Z

Canonical: https://digestai.news/story/researchers-propose-clic-to-compare-coding-behaviors-of-large-language

## Summary

Researchers introduce CLIC (Code Learning for Identification and Comparison), a method to analyze and compare how different large language models generate code. Instead of relying only on performance metrics like pass@k, CLIC examines token-frequency patterns in code samples to distinguish model behaviors. The approach uses decision trees and two new metrics—robustness and concentration—to assess how models differ in their coding style. The study compares 10 LLMs across 22 Kaggle machine learning tasks, aiming to support better model selection and prompt engineering through interpretable, visual analysis.

## Key points

- CLIC analyzes token frequencies in code to compare LLM coding behaviors
- Two new metrics, robustness and concentration, measure distinguishability and token distribution
- Case studies compare 10 LLMs across 22 Kaggle ML tasks

## Why it matters

This method offers a deeper way to evaluate LLMs beyond accuracy, helping developers and researchers choose models based on coding style and consistency, not just performance scores.

## Sources

1. [Token Signatures of Code: Comparing Coding Behaviors Across Large Language Models](https://arxiv.org/abs/2609.22097) (arXiv cs.CL, 2026-09-22, primary source)

## Cite

Digest AI, "Researchers propose CLIC to compare coding behaviors of large language models", 22 September 2026, https://digestai.news/story/researchers-propose-clic-to-compare-coding-behaviors-of-large-language

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