@@ -18,13 +18,7 @@ Most LLM-based classification pipelines use a large language model for every sin
 
 TRACER learns the decision boundary between "easy" and "hard" inputs directly from your LLM's own classification traces. It fits a fast, non-LLM surrogate on the easy partition, gates it with a calibrated acceptor, and defers only the uncertain inputs back to the LLM. Every deferred call produces a new trace, which feeds the next refit - coverage grows automatically over time. The result: **90%+ of classification calls routed to traditional ML, with formal parity guarantees against the teacher LLM and a self-improving routing policy**.
 
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 ```bash
 pip install tracer-llm