@inproceedings{park-etal-2026-llm,
title = "Do {LLM}-Generated Web Apps Behave Correctly? A Contract-Grounded Benchmark for Behavioral Conformance",
author = "Park, Chansung and
Jiang, Juyong and
Shen, Jiasi and
Kim, Sunghun",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2018/",
pages = "45959--45992",
ISBN = "979-8-89176-459-0",
abstract = "Large language models and coding agents can produce web applications that appear functional, yet surface plausibility is an unreliable proxy for behavioral conformance under interaction. We introduce ConformWeb, a contract-grounded protocol and benchmark that operationalizes public behavioral contracts to construct hidden interaction scenarios, evaluating whether agent-generated applications preserve strict conformance under dynamic execution. By decoupling hidden interaction sequences from the public behavioral scope, the protocol guarantees a fair held-out workflow evaluation without relying on undisclosed reference implementations. Evaluated across 24 validated instances scaling to 756 evaluation scenarios and 6,433 semantic steps, only 252 of the 2,160 total execution runs achieve full conformance, yielding a strict pass rate of 11.7{\%}. While scenario-level conformance reaches 36.7{\%} and step-level progress averages 49.4{\%}, this quantitative gap demonstrates that generative systems frequently achieve substantial partial progress but fail to maintain the critical behavioral invariants required for end-to-end conformance. All code and data are released at https://github.com/deep-diver/ConformWeb."
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%0 Conference Proceedings
%T Do LLM-Generated Web Apps Behave Correctly? A Contract-Grounded Benchmark for Behavioral Conformance
%A Park, Chansung
%A Jiang, Juyong
%A Shen, Jiasi
%A Kim, Sunghun
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F park-etal-2026-llm
%X Large language models and coding agents can produce web applications that appear functional, yet surface plausibility is an unreliable proxy for behavioral conformance under interaction. We introduce ConformWeb, a contract-grounded protocol and benchmark that operationalizes public behavioral contracts to construct hidden interaction scenarios, evaluating whether agent-generated applications preserve strict conformance under dynamic execution. By decoupling hidden interaction sequences from the public behavioral scope, the protocol guarantees a fair held-out workflow evaluation without relying on undisclosed reference implementations. Evaluated across 24 validated instances scaling to 756 evaluation scenarios and 6,433 semantic steps, only 252 of the 2,160 total execution runs achieve full conformance, yielding a strict pass rate of 11.7%. While scenario-level conformance reaches 36.7% and step-level progress averages 49.4%, this quantitative gap demonstrates that generative systems frequently achieve substantial partial progress but fail to maintain the critical behavioral invariants required for end-to-end conformance. All code and data are released at https://github.com/deep-diver/ConformWeb.
%U https://aclanthology.org/2026.emnlp-main.2018/
%P 45959-45992
Markdown (Informal)
[Do LLM-Generated Web Apps Behave Correctly? A Contract-Grounded Benchmark for Behavioral Conformance](https://aclanthology.org/2026.emnlp-main.2018/) (Park et al., EMNLP 2026)
ACL