# Verbosity Control

Long LLM answers raise inference latency and compute cost, yet prompt-only conciseness is hard to control and early stopping can cut sentences in half. MinimalLM separates when stopping becomes desirable from whether stopping is structurally appropriate: during decoding it adjusts stop-token scores by length and uncertainty, and only lets them take effect at sentence-ending punctuation, closed code fences or balanced brackets. Across three open-source models and 330 questions, generated tokens fell 47.6% with an average quality score of 8.04/10 versus 8.42/10 for unmodified models, and no structurally truncated outputs appeared.

Long LLM answers raise inference latency and compute cost, yet prompt-only conciseness is hard to control and early stopping can cut sentences in half. MinimalLM separates when stopping becomes desirable from whether stopping is structurally appropriate: during decoding it adjusts stop-token scores by length and uncertainty, and only lets them take effect at sentence-ending punctuation, closed code fences or balanced brackets. Across three open-source models and 330 questions, generated tokens fell 47.6% with an average quality score of 8.04/10 versus 8.42/10 for unmodified models, and no structurally truncated outputs appeared.