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Research questionHow can LLMs propagate local conversational revisions across dependent artifact components without excessive test-time computation?A local change specified in a conversation can require updates to multiple dependent parts of an artifact whose structure and dependencies are distributed across the conversation history. Additional test-time computation may improve revision consistency but increases inference cost.
AI
Evaluation & Benchmarks
Inference Optimization
Natural Language Processing
Reasoning
Latest papersRecent research connected to this question, newest first.What Else Needs Fixing? Exploring Cost-Effective Test-Time Compute for Revision Propagation in Artifacts Generated Through ConversationThe setting concerns conversationally generated artifacts with dependencies embedded in conversation history. Evidence comes from a benchmark evaluating nine revision methods across several LLMs, with reported accuracies of 68.3–93% and improvements from selecting among three parallel samples; the results are benchmark-based.research paper · Sep 3, 2026
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