Less is More: Improving LLM Reasoning with Minimal Test-Time
  Intervention
Authors: name: Zhen Yang, name: Mingyang Zhang, name: Feng Chen, name: Ganggui Ding, name: Liang Hou, name: Xin Tao, name: Pengfei Wan, name: Ying-Cong Chen
Organization: HKUSTGZ

Recent progress in large language models (LLMs) has focused on test-time
scaling to improve reasoning via increased inference computation, but often at
the cost of efficiency. We revisit test-time behavior and uncover a simple yet
underexplored phenomenon: reasoning uncertainty is highly localized-only a
small subset of high-entropy tokens dominantly affects output correctness.
Motivated by this, we propose Minimal Test-Time Intervention (MTI), a
training-free framework that enhances reasoning accuracy and stability with
minimal overhead. MTI includes: (i) Selective CFG intervention, applying
classifier-free guidance only at uncertain positions; and (ii) Lightweight
negative-prompt guidance, reusing the main model's KV cache to approximate
unconditional decoding efficiently. MTI yields consistent gains across general,
coding, and STEM tasks-e.g., +1.35% average improvement on eight benchmarks for
Qwen3-8B-Base and +5% on AIME2024 using Qwen3-32B-Reasoning-while remaining
highly efficient.