Perspectives Without Independence: Multi-Agent and Multi-Persona Reasoning Under Compute-Normalised Comparison, Why the Gains That Survive Are Not the Ones Diversity Predicts, and the Controls That Would Tell Them Apart

Abstract

Multi-agent debate, multi-persona prompting and related schemes are usually justified by diversity of viewpoint: several perspectives err differently, so their combination is more reliable than any one of them. That justification is a claim about independent errors, and a scheme that samples all of its perspectives from one model has to earn the independence it needs. This paper contributes an audit of the published results that hold inference compute constant, an analysis of the six cost units those results use, and a procedure for attributing a matched-cost gain to one mechanism. Where debate or persona schemes are compared with self-consistency or majority voting at a matched number of calls, tokens or generations, most of the reported gain disappears, and voting over the agents' independent first answers accounts for most of what remains; in one replication, self-consistency with nine samples scored 88.2 percent on GSM8K against 83.0 for debate at the same count. Against that, a 2026 study reports mixture-of-agents and debate ahead of self-consistency at equal compute, and a controlled agentic study reports gains of up to 80.8 percent on decomposable tasks. The paper argues that these positive results do not rest on diversity of viewpoint: the strongest one uses a single model in every role, and the agentic gains track whether the task decomposes. It separates four mechanisms that "multi-agent" names (voting, synthesis by an aggregator, decomposition, and viewpoint diversity) and shows that the compute-normalised comparisons with self-consistency isolate the fourth almost nowhere for single-model personas, where the few matched tests give small and mixed results. Model heterogeneity is different: at equal numbers of calls, mixed-model debate beats single-model debate, and a clone-controlled study on estimation and forecasting tasks found that deliberation among different models improved on their own pooled answers where deliberation among copies of one model did not. Two controls are still missing: a heterogeneous vote at matched cost on a reasoning benchmark, and a measurement of conditional error correlation among persona agents, including how often they agree on the same wrong answer.

The literature search, drafting and citation verification for this paper were carried out with AI assistance under the author's direction. Every arXiv citation was machine-verified against its live arXiv Atom API record, and every other citation against its Crossref or DOI record, during drafting (title and author list checked against the record returned). Every quantitative claim is taken from the abstract, full text or a table of the source credited with it; full-text numbers were read from the sources' own HTML or PDF renderings rather than from summaries. No experiment was run and no number in this paper was measured by its author. Table 1 re-presents numbers published by the cited papers, each named on its row; Figure 1 plots published values from three cited papers with no transformation other than rescaling proportions to percent. The four-mechanism decomposition, Algorithm 1 and the reporting protocol in Section 11 are original conceptual synthesis by the author, not empirical results, and are presented as such. Four classical references (Ladha 1992, Hong and Page 2004, Kuncheva and Whitaker 2003, Thompson 2014) were verified as records but their full texts were not read; they are cited only for their titles and headline positions, and no argument in the paper depends on their detailed results.

About the author

Pranay Mahendrakar is an ai specialist and large language model engineer based in Bengaluru, India. He builds production artificial intelligence systems and publishes open-access research on how those systems fail. See all 66 papers by Pranay Mahendrakar, or his ORCID record.