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This study introduces BudgetDoc, the first multimodal benchmark for evaluating model-budget-performance trade-offs in document tasks, and develops DRB, a lightweight estimator that predicts reasoning performance to optimize compute allocation and reduce costs in LLMs.
A new Stanford paper shows that under equal reasoning token budgets, single LLMs typically outperform multi-agent systems on multi-hop reasoning tasks, with gains from multi-agent setups often stemming from additional compute rather than architectural superiority. The paper uses the Data Processing Inequality to explain why information loss in handoffs harms multi-agent performance, and identifies context quality as the key factor where multi-agent systems can provide benefits.