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Evaluating Stochasticity in Deep Research Agents

Deep Research Agents are advanced artificial intelligence systems designed to autonomously gather and synthesize information to answer complex queries, but their real-world reliability is currently compromised by stochasticity, meaning they often produce vastly different findings and conclusions when given the exact same prompt multiple times. To address this fundamental flaw, researchers conceptualized the execution of these agents as a Markov Decision Process, systematically tracing how uncertainty is introduced and compounded through three main operational phases: formulating search queries, compressing retrieved data, and logically reasoning over the gathered evidence. Through controlled experiments manipulating the randomness at each phase, the researchers discovered that variability introduced during the early stages of data acquisition heavily dictates the consistency of the final output, although the internal reasoning module generates the highest amount of intrinsic variance. Furthermore, they demonstrated that increased stochasticity does not correlate with improved accuracy, prompting the development of mitigation strategies such as enforcing structured reasoning formats and requiring multiple system runs to agree on search queries before proceeding. Implementing these targeted algorithmic constraints successfully reduced output variance by twenty-two percent while simultaneously preserving or enhancing the overall accuracy of the final research reports, proving that deep research agents can be engineered for greater consistency without sacrificing analytical quality. https://arxiv.org/pdf/2602.23271