Scaffold, Not Vocabulary? A Controlled, Two-Tier, Pre-Registered Study of a Popperian Code-Generation Skill

Problem
This paper addresses the gap in understanding the efficacy of Popperian code-generation skills in large language models (LLMs), particularly in distinguishing whether performance improvements stem from the content of the skills or the structural scaffolding they provide. The study is pre-registered and aims to clarify the contributions of these skills amidst concerns about biases in LLM evaluations.

Method
The author, Mehmet Iscan, conducts a controlled, two-tier ablation study using two models: Claude Sonnet 4.6 and Qwen2.5-Coder-0.5B. The study employs three control conditions: a length-matched placebo, a labels-only scaffold retaining Popperian headers but omitting procedural details, and an execution oracle (HumanEval+ unit tests). The evaluation metrics include correctness rates, specifically focusing on the F@8 score, which measures the proportion of correct outputs among the top eight generated responses. The study also incorporates a self-judge mechanism to assess the model’s adherence to the Popperian rubric.

Results
In the evaluation of Claude Sonnet 4.6 (N=163), all conditions approached the benchmark ceiling, yielding no significant separation in performance, thus failing to support the pre-registered +5-point improvement claim. Conversely, the smaller model, Qwen2.5-Coder-0.5B (N=164), demonstrated a notable increase in correctness, with structured arms achieving a 20-22 point improvement in best-of-eight correctness. However, the full Popperian skill did not show a statistically significant advantage over the labels-only scaffold, with both achieving an aggregate F@8 of 34.8%. The placebo condition trailed by only 2.4 points. Additionally, a self-judge using the Popperian framework did not outperform random selection, with a bias towards one index, indicating a lack of effective discrimination.

Limitations
The authors acknowledge that the findings are limited to the specific models and conditions tested, and the results may not generalize to other LLM architectures or different prompt-skill families. The study also highlights the potential for ceiling effects in the evaluation of the larger model, which may obscure the detection of meaningful differences. Furthermore, the self-judge’s performance raises questions about the reliability of LLMs in self-evaluation tasks.

Why it matters
This research provides critical insights into the role of scaffolding in LLM performance, suggesting that structural elements may be more influential than the content of prompts in code generation tasks. The calibrated negative result contributes to the discourse on prompt engineering and the evaluation of LLM capabilities, emphasizing the need for rigorous testing protocols. This work is significant for future research in prompt design and LLM evaluation methodologies, as it delineates the boundaries of claims regarding specific prompt-skill families, as published in arXiv cs.CL.