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CLIC tool maps how 10 LLMs differ in coding style, not just pass rate

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Most evaluation of large language models on coding tasks has relied on performance metrics such as pass@k: does the generated code work or not. The authors argue that this is losing its usefulness, because many current models already clear baseline performance requirements, which narrows the gap that pass-based scoring can detect. Their question is different: how do LLMs differ in the way they write code, independent of whether that code passes.

To answer it, the authors built CLIC (Code Learning for Identification and Comparison), a visual analytics approach that profiles coding behavior through token-frequency analysis. CLIC turns each code sample into a feature vector of token frequencies, then trains an interpretable decision tree that learns to tell two LLMs' code sets apart. On top of plain classification accuracy, the authors introduce two new metrics for reading that separation: robustness, which checks whether two models stay distinguishable as their most distinctive tokens are stripped out one by one, and concentration, which checks whether the gap between two models rests on a handful of dominant tokens or is spread across many.

Because a full analysis means running this comparison across many LLM pairs, many coding tasks and multiple tokenization levels, and then tracing the resulting chain of evidence, the authors also built an interactive visual analytics system for it: a way to navigate the comparison landscape, pick out pairs of interest, and drill down from a high-level difference into the specific discriminative tokens and the code contexts they appear in. They demonstrate the approach with case studies comparing 10 LLMs across 22 Kaggle machine-learning tasks, which the authors say surface actionable insights for choosing between models and for prompt engineering. The abstract does not name the 10 models or the 22 tasks, and it reports no specific accuracy, robustness or concentration scores for the case studies.

Key facts

  • CLIC (Code Learning for Identification and Comparison) profiles LLM coding behavior through token-frequency analysis rather than pass@k performance scores.
  • It represents each code sample as a feature vector of token frequencies and trains an interpretable decision tree to separate two LLMs' code sets.
  • The authors define two new metrics: robustness (whether two models stay distinguishable as their most-discriminative tokens are removed) and concentration (whether the difference is driven by a few tokens or spread across many).
  • An interactive visual analytics system lets users navigate comparisons across LLM pairs, tasks and tokenization levels, then drill into discriminative tokens and their code contexts.
  • Case studies compare 10 LLMs across 22 Kaggle ML tasks, which the authors say yield actionable insights for LLM selection and prompt engineering.

Why it matters

Coding-focused LLM evaluation has mostly asked one question: does the code pass? The authors argue that question is running out of room, because enough current models already clear baseline performance requirements that pass-rate alone stops separating them. CLIC reframes the comparison around how models write code, not just whether the result works, using token-frequency patterns that a decision tree can learn to tell apart.

Who it affects

The intended users are people who have to choose between LLMs for coding work and people who write prompts for them: the authors frame the case studies explicitly as surfacing insights for LLM selection and prompt engineering. It is also aimed at researchers who evaluate or compare coding models and want a way to look past aggregate pass-rate numbers.

How to use it

CLIC pairs an automated pipeline (feature vectors of token frequencies, an interpretable decision tree, the robustness and concentration metrics) with an interactive visual analytics system for exploring the results. That system is built for a multi-scale, hypothesis-driven workflow: scan many pairwise comparisons across LLM pairs, tasks and tokenization levels, identify a pair worth a closer look, then drill down to the specific tokens driving the difference and the code contexts they show up in. The demonstration runs this over 10 LLMs and 22 Kaggle ML tasks.

How solid is it

The source is an arXiv abstract describing a proposed method and system, backed by case studies across 10 LLMs and 22 Kaggle ML tasks. The abstract itself does not report the specific accuracy, robustness or concentration figures those case studies produced, and it does not name the 10 models or the 22 tasks used, so the strength of the result cannot be checked from this text alone.

Risks and caveats

Beyond the missing numeric results, the abstract does not identify the authors, their institutions, or whether code, data or the CLIC tool itself will be released. Readers who want to judge or reuse the method will need to consult the full paper, which is not covered here.