Evaluating Test-Time Compute for Conversational Artifact Revision
September 2, 2026
This study investigates how LLMs handle dependency propagation during iterative artifact revisions in conversation. The authors introduce a new benchmark and evaluate nine methods, including parallel sampling, using models like gpt-oss-20b and gpt-5.4-mini to optimize cost-effective test-time compute.
HOW THIS AFFECTS YOU
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builderYou can use these findings to optimize how your agent handles complex, multi-turn editing tasks.
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designerThis impacts how conversational interfaces handle structural changes to generated content.