arXiv:2606. 12318v1 Announce Type: cross Abstract: Neural operators approximate mappings between function spaces, but often generalize poorly to other operators and usually require fine-tuning or retraining.
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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.
The paper proposes that large language models (LLMs) encode high‑level concepts as linear directions within their activation space and that they can use subspaces and vector algebra to perform tasks. By analyzing functional modules and residual streams during in‑context learning (ICL), the authors find that LLMs can create evidence‑accumulating subspaces and solve ICL tasks through simple algebraic operations within those subspaces.
arXiv:2609.22143v1 Announce Type: cross Abstract: Machine learning learns functions: prompt to response, image to caption. What these functions are mathematically remains hard to say. We present a me...
arXiv:2610.01054v1 Announce Type: cross Abstract: In-context learning (ICL) enables language models to perform new tasks from demonstrations without weight updates. However, every ICL inference requi...
The paper investigates how large language models learn new tasks in-context, comparing rule-based instruction following to example-based few-shot prompting across five diverse tasks. Results show that models generally learn more reliably from rule descriptions than from examples alone, and adding more examples does not consistently improve performance. Instruction tuning further enhances rule-based learning while preserving example-based capabilities, with rule advantages being strongest for algebraic tasks and weaker for tasks requiring distributional sensitivity or parametric knowledge.
arXiv:2505. 11766v4 Announce Type: replace Abstract: Neural Operators (NOs) are powerful architectures for learning mappings between function spaces.