@inproceedings{luo-etal-2026-telegen,
title = "{T}ele{G}en: Improving {LLM}-Based Web Application Generation via Runtime Telemetry",
author = "Luo, Yujia and
Zhang, Haonan and
Shen, Jiasi and
Ding, Zishuo and
Shang, Weiyi",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1860/",
pages = "39697--39715",
ISBN = "979-8-89176-462-0",
abstract = "Large language models can generate runnable web applications from natural-language requirements, but many generated applications still fail interactive tasks. Existing generate-execute-repair pipelines execute the generated application and use task outcomes or error messages to guide code revision. However, this feedback often misses the runtime behavior between a browser action and the final task outcome, making interaction-level failures difficult to diagnose. Therefore, we propose TeleGen, an observability-enhanced framework for LLM-based web application generation. TeleGen instruments generated applications, collects runtime telemetry during task execution, and compresses raw telemetry logs into concise briefs for repair. We evaluate TeleGen on WebGen-Bench and Web-Bench. On WebGen- Bench, TeleGen improves task success from 67.7{\%} with repair without telemetry to 76.2{\%}, an increase of 8.5 percentage points. On Web-Bench, it improves cumulative Pass@2 from 21.7{\%} to 29.8{\%}. Ablation results show that runtime telemetry provides a useful diagnostic signal, while telemetry briefs make this signal more effective and less costly to use. Further analysis shows that telemetry is especially helpful for failures involving hidden execution paths, such as navigation, form workflows, and frontend-backend coordination."
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<abstract>Large language models can generate runnable web applications from natural-language requirements, but many generated applications still fail interactive tasks. Existing generate-execute-repair pipelines execute the generated application and use task outcomes or error messages to guide code revision. However, this feedback often misses the runtime behavior between a browser action and the final task outcome, making interaction-level failures difficult to diagnose. Therefore, we propose TeleGen, an observability-enhanced framework for LLM-based web application generation. TeleGen instruments generated applications, collects runtime telemetry during task execution, and compresses raw telemetry logs into concise briefs for repair. We evaluate TeleGen on WebGen-Bench and Web-Bench. On WebGen- Bench, TeleGen improves task success from 67.7% with repair without telemetry to 76.2%, an increase of 8.5 percentage points. On Web-Bench, it improves cumulative Pass@2 from 21.7% to 29.8%. Ablation results show that runtime telemetry provides a useful diagnostic signal, while telemetry briefs make this signal more effective and less costly to use. Further analysis shows that telemetry is especially helpful for failures involving hidden execution paths, such as navigation, form workflows, and frontend-backend coordination.</abstract>
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%0 Conference Proceedings
%T TeleGen: Improving LLM-Based Web Application Generation via Runtime Telemetry
%A Luo, Yujia
%A Zhang, Haonan
%A Shen, Jiasi
%A Ding, Zishuo
%A Shang, Weiyi
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F luo-etal-2026-telegen
%X Large language models can generate runnable web applications from natural-language requirements, but many generated applications still fail interactive tasks. Existing generate-execute-repair pipelines execute the generated application and use task outcomes or error messages to guide code revision. However, this feedback often misses the runtime behavior between a browser action and the final task outcome, making interaction-level failures difficult to diagnose. Therefore, we propose TeleGen, an observability-enhanced framework for LLM-based web application generation. TeleGen instruments generated applications, collects runtime telemetry during task execution, and compresses raw telemetry logs into concise briefs for repair. We evaluate TeleGen on WebGen-Bench and Web-Bench. On WebGen- Bench, TeleGen improves task success from 67.7% with repair without telemetry to 76.2%, an increase of 8.5 percentage points. On Web-Bench, it improves cumulative Pass@2 from 21.7% to 29.8%. Ablation results show that runtime telemetry provides a useful diagnostic signal, while telemetry briefs make this signal more effective and less costly to use. Further analysis shows that telemetry is especially helpful for failures involving hidden execution paths, such as navigation, form workflows, and frontend-backend coordination.
%U https://aclanthology.org/2026.findings-emnlp.1860/
%P 39697-39715
Markdown (Informal)
[TeleGen: Improving LLM-Based Web Application Generation via Runtime Telemetry](https://aclanthology.org/2026.findings-emnlp.1860/) (Luo et al., Findings 2026)
ACL