aiqbench analysis
This research introduces Chain of Operators (CHOP), a framework that enables frozen scientific foundation models to handle out-of-distribution physical tasks without parameter fine-tuning. By decomposing complex problems into interpretable sequences of mathematical operations, the method significantly reduces inference errors in benchmarks ranging from partial differential equations to air-quality forecasting. The work establishes a modular paradigm for agentic scientific computing where adaptability is achieved through programmable inference rather than weight updates.
Chain of Operators: An Inference-Time Harness for In-Context Operator Learning
arXiv:2606.12318v2 Announce Type: replace-cross Abstract: While scientific foundation models show immense promise in accelerating physical simulations and numerical forecasting, they remain notoriously brittle when encountering out-of-distribution (OOD) scenarios. Adapting these generalist models to complex OOD tasks typically requires expensive parameter fine-tuning. In linguistic AI, this bottleneck is bypassed using "harnesses", which serve as external scaffolding such as reasoning chains and tool use to adapt frozen weights to complex tasks, yet designing an equivalent harness for physical domains remains an open frontier. To bridge this gap, we introduce Chain of Operators (CHOP), a framework that guides a frozen foundation model through complex OOD tasks without updating a single weight. By exploiting the in-context learning capability of In-Context Operator Networks (ICON), CHOP systematically decomposes unfamiliar problems into a sequence of explicit, closed-form mathematical operations and multiple model calls, translating OOD queries back into the model's learned operating regime. Across diverse benchmarks, including canonical PDE problems and real-world air-quality forecasting, CHOP consistently and substantially reduces inference errors compared to direct model evaluation. Crucially, these modular operator chains remain fully interpretable and can generalize across entirely distinct families of physical equations. Ultimately, this work demonstrates how frozen scientific models can adapt through programmable inference, establishing a modular, potential paradigm for agentic scientific computing.
Abstract:While scientific foundation models show immense promise in accelerating physical simulations and numerical forecasting, they remain notoriously brittle when encountering out-of-distribution (OOD) scenarios. Adapting these generalist models to complex OOD tasks typically requires expensive parameter fine-tuning. In linguistic AI, this bottleneck is bypassed using "harnesses", which serve as external scaffolding such as reasoning chains and tool use to adapt frozen weights to complex tasks, yet designing an equivalent harness for physical domains remains an open frontier. To bridge this gap, we introduce Chain of Operators (CHOP), a framework that guides a frozen foundation model through complex OOD tasks without updating a single weight. By exploiting the in-context learning capability of In-Context Operator Networks (ICON), CHOP systematically decomposes unfamiliar problems into a sequence of explicit, closed-form mathematical operations and multiple model calls, translating OOD queries back into the model's learned operating regime. Across diverse benchmarks, including canonical PDE problems and real-world air-quality forecasting, CHOP consistently and substantially reduces inference errors compared to direct model evaluation. Crucially, these modular operator chains remain fully interpretable and can generalize across entirely distinct families of physical equations. Ultimately, this work demonstrates how frozen scientific models can adapt through programmable inference, establishing a modular, potential paradigm for agentic scientific computing.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2606.12318 [cs.LG]
(or arXiv:2606.12318v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.12318 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 299 words
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