AI RESEARCH
scicode-lint: Detecting Methodology Bugs in Scientific Python Code with LLM-Generated Patterns
arXiv CS.LG
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ArXi:2603.17893v1 Announce Type: cross Methodology bugs in scientific Python code produce plausible but incorrect results that traditional linters and static analysis tools cannot detect. Several research groups have built ML-specific linters, nstrating that detection is feasible. Yet these tools share a sustainability problem: dependency on specific pylint or Python versions, limited packaging, and reliance on manual engineering for every new pattern.