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TeleGen addresses a critical gap in autonomous coding by leveraging runtime telemetry to diagnose hidden interaction failures that standard error messages miss. By compressing raw logs into concise briefs, the framework enables Large Language Models to effectively repair complex web applications involving navigation and form workflows without incurring excessive computational costs. This observability-enhanced approach significantly boosts task success rates on established benchmarks, marking a practical advancement for reliable AI-generated software development.
TeleGen: Improving LLM-Based Web Application Generation via Runtime Telemetry
arXiv:2610.04981v1 Announce Type: cross 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: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.
19 pages, 3 figures. Accepted to Findings of EMNLP 2026
Software Engineering (cs.SE); Artificial Intelligence (cs.AI)
arXiv:2610.04981 [cs.SE]
(or arXiv:2610.04981v1 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2610.04981 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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