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Small-Scale Shear Layers in Isotropic Turbulence of Viscoelastic Fluids

Published notice on a work cited in the Pith corpus. Exact quotes below. No model judges whether any citation was load-bearing.

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Correction Crossref 4 open · 4 total · 0 disputed
DOI
10.1017/jfm.2024.1233
Notice DOI
10.1017/jfm.2025.341
Event date
2025-07-04
Machine twin
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01One-hop citing occurrences

Correction Open
AutoOR: Scalably Post-training LLMs to Autoformalize Operations Research Problems

ref [11] · 2604.16804 · notice #7272 · dispute

Raw extraction · citation context

isting synthetic datasets suffer from low accuracy or are distilled from stronger models, making them expensive and difficult to scale [9, 10]. At the same time, the organizations that most need automated formalization, such as manufacturers or logistics operators, often have domain-specific problem distributions that are private and limited in size [11]. A practical system must therefore be able to start from a small set of representative problems, scale up verified training signal, and generalize without relying on human annotation or distillation. We present AutoOR, a scalable synthetic data generation and reinforcement learning pipeline that trains LLMs to autoformalize optimization problems across linear, mixed-integer, and non-
Correction Open
OR-Space: A Full-Lifecycle Workspace Benchmark for Industrial Optimization Agents

ref [14] · 2605.28158 · notice #7273 · dispute

Raw extraction · bibliography line

Chenyu Huang, Zhengyang Tang, Shixi Hu, Ruoqing Jiang, Xin Zheng, Dongdong Ge, Benyou Wang, and Zizhuo Wang. ORLM: A Customizable Framework in Training Large Models for Automated Optimization Modeling.Operations Research, 73(6):2986–3009, November 2025. ISSN 0030-364X. doi: 10.1287/ opre.2024.1233. URLhttps://pubsonline.informs.org/doi/10.1287/opre.2024.1233
Correction Open
Solver-Verified Formulation Generation and Selection for Multi-Warehouse Inventory Allocation Using Large Language Models

ref [9] · 2606.29366 · notice #7274 · dispute

Raw extraction · bibliography line

Chenyu Huang, Zhengyang Tang, Shixi Hu, Ruoqing Jiang, Xin Zheng, Dongdong Ge, Benyou Wang, and Zizhuo Wang. ORLM : A customizable framework in training large models for automated optimization modeling. Operations Research, 73 0 (6): 0 2986--3009, 2025. doi:https://doi.org/10.1287/opre.2024.1233

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