LLMs as a Jury: Cross-Model Consensus Beats Reward Models for Reasoning

In the race to improve the reasoning capabilities of language models (LLMs), one of the most persistent bottlenecks is how to select the correct chain of thought from a set of candidates. Traditionally, the dominant methods have been self-consistency and trained reward models. However, both carry significant costs: the former replicates the errors of the generating model, while the latter requires labeled data and degrades when encountering distributions different from training. An alternative is emerging: consensus among independently trained models, acting as an LLM jury. This approach, which requires no additional resources during inference, is based on the structure of agreement between models, not on one model scoring another. Across seven benchmarks, this method outperforms self-consistency and approaches the performance of an ideal (oracle) selector on competitive math problems, while self-scoring barely closes the gap. The secret lies in error decorrelation: when multiple independent models make mistakes, they do so in different ways, scattering their wrong answers, while the correct answer accumulates agreement. This phenomenon is described by a parameter-free law, derived in closed form, which predicts consensus accuracy from three observable panel statistics with a mean absolute error of 0.03. The law also reveals a ceiling: a shared-error floor when all models share the same misconception, which is near zero in math but non-trivial in science. When comparing the jury with four trained verifiers (discriminative, outcome, and generative), cross-model consensus matches the best within its math training domain and surpasses it outside. This turns cross-model consensus into a verifier we can characterize in advance: a law that says when to trust it, and a floor that marks where it cannot. For a company like Q2BSTUDIO, dedicated to software development and technology, this innovation has direct implications. In our artificial intelligence projects, the reliability of AI agents is critical. Integrating a consensus mechanism between models allows us to build custom applications that make more robust decisions, especially in scenarios where labeled data is scarce or domains change rapidly. For example, a cybersecurity system analyzing attack patterns can benefit from a panel of LLMs agreeing on the nature of a threat, reducing false positives. Similarly, in cloud AWS/Azure solutions, orchestrating agents that verify their answers collectively improves scalability without sacrificing accuracy. Our Business Intelligence with Power BI offering is also enhanced: AI-generated reports can be validated by consensus, ensuring insights reflect the data reality. Additionally, this approach aligns perfectly with process automation, where unsupervised verification is key. The law of cross-model consensus is not just an academic advance: it is a practical tool we are incorporating into our developments to deliver smarter and more secure software. The shared-error floor, identified by the law, allows us to design diverse model panels, minimizing common biases. At Q2BSTUDIO, we believe that collaboration between models, far more than individual judgment, is the path to reliable AI. That is why we continue researching how to apply this principle to our custom software development, integrating cloud, cybersecurity, and BI into a coherent ecosystem. Cross-model consensus does not replace reward models; it complements them, offering a free and transparent alternative. Ultimately, the future of verification in AI lies in diverse juries, not single judges. And at Q2BSTUDIO, we are ready to build that future.