arXiv:2510.13940v4 Announce Type: replace Abstract: 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
The continuous drive to improve large language model performance and efficiency, especially at scale, motivates innovations in test-time interventions.
Improving LLM reasoning with minimal computational overhead directly addresses key efficiency constraints for widespread AI deployment and reduces operating costs.
The proposed MTI framework suggests a new paradigm for enhancing LLM reliability and performance without the need for extensive additional training or inference cost.
- · LLM developers
- · Cloud providers
- · AI-dependent industries
- · Companies relying on brute-force scaling for LLM performance
Increased accessibility and affordability of advanced LLM capabilities due to improved efficiency.
Faster innovation cycles in AI applications as development and deployment costs decrease.
Potentially democratizing advanced AI by lowering the barriers to entry for model deployment and usage.
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