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State-of-the-art VLMs struggle to execute scientific research workflows, revealing a critical gap in their ability to interact with specialized software and produce verifiable results.
Standard time-series forecasting losses systematically fail on pathological demand regimes, creating a bias irreducible by model capacity alone.
Recurring frontier API calls can be replaced by one-minute compile-time distillation, turning plain-text prompts into local, versionable neural functions that achieve 83.6% accuracy on benchmarks where instantaneous compilers fail entirely.