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PaperMech-IBM-BOB
PaperMech: Autonomous Research Verification Engine The Pitch Transforming multi-week statistical methodology verification into a 3-second automated pipeline. Long Description The scientific and enterprise communities are currently facing a massive reproducibility crisis. In fields ranging from psychology to medical ML, a significant percentage of published claims cannot be replicated using the provided data. For an independent researcher, enterprise data scientist, or journal editor, manually verifying the statistical methodology of a single paper—extracting the exact test used, hunting down the group sizes, means, and standard deviations, and re-running the math—can take days of intense labor. The bottleneck is not skepticism; it is tooling. PaperMech is an autonomous research replication engine designed to instantly verify statistical claims. By simply inputting an ArXiv ID, PaperMech initiates a complex agentic workflow: Intelligent Ingestion: It bypasses messy PDF OCR by scraping the raw LaTeX/HTML source from ar5iv, extracting the clean methodology and results sections. AI Parameter Extraction: It utilizes a Large Language Model to parse the dense academic prose, cleanly extracting the exact statistical test used (e.g., Independent T-Test, ANOVA) along with all critical variables (n-sizes, means, standard deviations, and reported p-values). Local Execution Sandbox: It routes these parameters into an isolated, local Python execution environment powered by SciPy. It recalculates the math from scratch. The Verdict: The system compares the mathematically computed p-value against the paper's reported p-value. It then outputs a definitive, color-coded verdict: REPLICATES (if the math holds up) or DISCREPANCY (if the numbers do not align). PaperMech takes the rigorous, manual process of peer-review verification and compresses it into an instant, objective, and automated B2B workflow.
17 May 2026
