Chain of Operators: An Inference-Time Harness for In-Context Operator Learning

Chain of Operators (CHOP) is a framework that enables frozen scientific foundation models to tackle out‑of‑distribution (OOD) tasks without any parameter updates. By leveraging In‑Context Operator Networks (ICON), CHOP breaks down unfamiliar problems into sequences of explicit mathematical operations and model calls, effectively translating OOD queries into the model’s learned regime. Experiments on PDE benchmarks and air‑quality forecasting show that CHOP consistently reduces inference errors while maintaining full interpretability and cross‑equation generalization.

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• llms
• agents
• fine-tuning
• benchmarks