Comparative evaluation demonstrates 100% section coverage in web code generation tasks, indicating that structured requirements analysis drives model completeness.
Large Language Models (LLMs) can generate web code, but their outputs are probabilistic and often incomplete. This paper presents HDAFA (Hyper-Deterministic Atomic Agentic Architecture), a multi-agent pipeline that separates requirements analysis, code generation, and deterministic validation. We evaluate HDAFA against three baselines on 14 completed web-generation tasks across five categories, using the same model (Gemini 3.5 Flash-Lite) and temperature 0.0. Systems that include a Requirements Analyzer achieve 100% average section coverage, compared with approximately 85% for direct-generation baselines — a 15 percentage-point gap. These results indicate that explicit structural decomposition of requirements is the primary driver of content completeness under the evaluated setting, while deterministic validation remains a safety layer whose practical value requires harder tasks with higher failure rates. Keywords: Multi-Agent Systems, LLM, Code Generation, Deterministic Verification, Requirements Analysis Code and data available upon request: afracosh12@gmail.com
No takes yet. Share an insight, caveat, or question.
Mustafa Fathy Youssef (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: