The Cognitive Exoskeleton: A Structured Pipeline for Augmented Intelligence in Scientific Formulation and Evidence Synthesis

Abstract Background: The exponential growth of scientific literature and the integration of Large Language Models (LLMs) into research workflows represent a fundamental epistemological shift. The scientific community is rapidly moving from search-based literature reviews to synthesis-based analysis, accompanied by an institutional push toward autonomous "Research Agents." While AI offers immense potential to solve the modern evidence bottleneck and alleviate researcher fatigue, its unsupervised use introduces profound methodological risks, including the algorithmic smoothing of science, linguistic homogenization, inquiry bias, and the hallucination of authority. Objective: This paper argues against the deployment of fully autonomous agents in high-stakes scientific domains. Instead, it proposes a rigorous, multi-stage "computer-in-the-loop" pipeline to establish a safe, auditable human-machine interface. Proposed Framework: By enforcing strict methodological formalisms, spanning from the initial user-driven ideation of a research question to the quantitative harvesting of data, it is possible to demonstrate how deterministic AI settings, dynamic cognitive tuning, and continuous human validation can transform LLMs into a safe, auditable Cognitive Exoskeleton. By decommissioning autonomous workflows and adopting an Evidence-Based Agent Stack, this framework separates syntactic processing from semantic judgment. Impact and Significance: This architecture is designed not only to mitigate AI misuse but to actively improve research quality, aligning directly with regulatory principles such as the TREAT (Trustworthiness, Reproducibility, Explainability, Applicability, Transparency) framework and e-validation protocols. Ultimately, embracing this structured approach paves the way for a new era of highly reproducible research, significantly reducing publication timelines and enabling a more rapid, transparent peer-review process.