Do LLMs Signal When They're Right? Evidence from Neuron Agreement

Large language models (LLMs) commonly boost reasoning via sample-evaluate-ensemble decoders, achieving label free gains without ground truth. However, prevailing strategies score candidates using only external outputs such as token probabilities, entropies, or self evaluations, and these signals can be poorly calibrated after post training. We instead analyze internal behavior based on neuron activations and uncover three findings: (1) external signals are low dimensional projections of richer internal dynamics; (2) correct responses activate substantially fewer unique neurons than incorrect ones throughout generation; and (3) activations from correct responses exhibit stronger cross sample agreement, whereas incorrect ones diverge. Motivated by these observations, we propose Neuron Agreement Decoding (NAD), an unsupervised best-of-N method that selects candidates using activation sparsity and cross sample neuron agreement, operating solely on internal signals and without requiring comparable textual outputs. NAD enables early correctness prediction within the first 32 generated tokens and supports aggressive early stopping. Across math and science benchmarks with verifiable answers, NAD matches majority voting; on open ended coding benchmarks where majority voting is inapplicable, NAD consistently outperforms Avg@64. By pruning unpromising trajectories early, NAD reduces token usage by 99% with minimal loss in generation quality, showing that internal signals provide reliable, scalable, and efficient guidance for label free ensemble decoding.

Revisiting LLM Evaluation through Mechanism Interpretability: a New Metric and Model Utility Law

Large Language Models (LLMs) have become indispensable across academia, industry, and daily applications, yet current evaluation methods struggle to keep pace with their rapid development. In this paper, we analyze the core limitations of traditional evaluation pipelines and propose a novel metric, the Model Utilization Index (MUI), which introduces mechanism interpretability techniques to complement traditional performance metrics. MUI quantifies the extent to which a model leverages its capabilities to complete tasks. The core idea is that to assess an LLM's overall ability, we must evaluate not only its task performance but also the effort expended to achieve the outcome. Our extensive experiments reveal an inverse relationship between MUI and performance, from which we deduce a common trend observed in popular LLMs, which we term the Utility Law. Based on this, we derive four corollaries that address key challenges, including training judgement, the issue of data contamination, fairness in model comparison, and data diversity. We hope that our survey, novel metric, and utility law will foster mutual advancement in both evaluation and mechanism interpretability. Our code can be found at https://github.com/ALEX-nlp/MUI-Eva.