Benchmarking MindControl: Guided Reasoning Budgets for llama.cpp
Benchmark results for 'MindControl', a sampler-level guided reasoning budget tool for llama.cpp, demonstrate that nudging LLMs to wrap up their thinking gracefully reduces token consumption without degrading performance. The tool was tested on Qwen3.6-27B using HumanEval+ and LiveCodeBench. Controlling the reasoning length of LLMs prevents them from overthinking or entering infinite loops, which saves significant computational costs. By avoiding abrupt hard-truncation, developers can optimize inference efficiency without sacrificing the quality of the model's output. The most guided configuration (intro+soft+hard) cut token usage by half on LiveCodeBench while maintaining the same score, and achieved a peak score of 95.7% on HumanEval+. However, extremely complex problems still suffer an accuracy penalty under tight budgets, suggesting that difficult tasks inherently require more reasoning tokens.
## BACKGROUND
Large language models often use chain-of-thought reasoning to solve complex tasks, but this can lead to excessive token usage. Budget guidance techniques steer the model's reasoning process toward a target token limit at inference time without requiring expensive fine-tuning.