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Braintrust's Topics feature uses LLM summarization to make production agent traces tractable for clustering and classification at scale, inspired by Anthropic's Clio approach.
This paper investigates a critical disconnect in trace-based knowledge distillation for LLMs, revealing that semantically correct Chain-of-Thought traces are not reliably correlated with correct final answers and that traces optimized for model performance are often least interpretable to end users.