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We investigate this through \\textbf{BadScientist}, a framework that evaluates whether fabrication-oriented paper generation agents can deceive multi-model LLM review systems. Our generator employs presentation-manipulation strategies requiring no real experiments. We develop a rigorous evaluation framework with formal error guarantees (concentration bounds and calibration analysis), calibrated on real data. Our results reveal systematic vulnerabilities: fabricated papers achieve acceptance rates up to . Critically, we identify \\textit{concern-acceptance conflict} -- reviewers frequently flag integrity issues yet assign acceptance-level scores. 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Our results reveal systematic vulnerabilities: fabricated papers achieve acceptance rates up to . Critically, we identify \\textit{concern-acceptance conflict} -- reviewers frequently flag integrity issues yet assign acceptance-level scores. 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