AI for Science: Verification in the Age of AI Scientists

AI Scientists are rapidly changing the pace and structure of scientific discovery. Emerging systems can generate hypotheses, design experiments, write papers, and propose scientific decisions at a scale that increasingly exceeds human capacity for manual review. Yet across domains, the ability to generate scientific outputs is advancing faster than the ability to verify them. This workshop addresses the resulting verification bottleneck: how should the scientific community trust, judge, and act on AI-generated scientific claims when ground truth is expensive, delayed, incomplete, or unavailable? We will bring together researchers from scientific domains spanning mathematics, physics, chemistry, biology, medicine, climate science, and energy systems, together with experts in machine learning, formal methods, simulation, experimental validation, uncertainty quantification, and safety-critical deployment, to examine verification across three settings (1) open-ended hypothesis generation, (2) imperfect simulators and surrogate verifiers, and (3) real-world constraints involving cost, uncertainty, and safety. The workshop will feature invited talks, contributed research, verifier systems, poster sessions, and a cross-scientific-domain panel on what counts as sufficient evidence for action. By centering verification as a key challenge for AI for Science, this workshop aims to build shared vocabulary, methods, and infrastructure for determining when AI-generated science is correct, trustworthy, and actionable.