The monograph introduces Elimination Geometry (EG), a typed, native‑loss, audit‑oriented framework that investigates when locally optimal objects can be realized by a shared deployment rule. EG examines how elimination and compression can erase distinctions needed for prediction, inference, control, or representation, and it separates local solvability, global realizability, and finite‑sample certifiability. The work synthesizes tools from geometry, optimization, information theory, statistics, and machine learning to address regular, coordination, singular, compositional, and resource‑limited mechanisms, and demonstrates applications in sparse model selection, distribution‑free prediction, observational treatment policies, routed expert and retrieval systems, and learned score fields.
By Mian Huang, Xueqin Wang
arXiv:2608. 08618v1 Announce Type: cross Abstract: Industrial device commissioning requires engineers to manually extract hundreds of protocol-specific parameters from heterogeneous PDF manuals and transcribe them into supervisory control systems, a time-intensive, error-prone workflow.
By Aadil Gani Ganie, Saad Ezzini, Naveed Farooz Marazi
arXiv:2607. 05750v1 Announce Type: new Abstract: Computer-aided design (CAD) for industrial components requires long-horizon procedural modeling, robust feature dependencies, editable parametric geometry, and production-grade B-Rep execution.
By Yunhan Xu, Qifeng Wu, Xunjin Li, Yuanwei Bin, Qingsong Yao, Jianghang Gu, Guan Wang, Weihao Lv, Huiyu Yang, Wenfa Luo, Jiao Xiang, Yuntian Chen, Shiyi Chen
Computer-aided design (CAD) for industrial components requires long-horizon procedural modeling, robust feature dependencies, editable parametric geometry, and production-grade B-Rep execution. Existing text-to-CAD methods have made promising progress in generating CAD programs from natural-language descriptions, but they still struggle when user prompts are ambiguous, underspecified, or only describe high-level design intent.
arXiv:2609.14741v1 Announce Type: cross
Abstract: Early thermal-mechanical lattice requirements are knowledge-intensive and often jointly unsatisfiable: an engineer asks for a cell that is light, sti...
By Shaoliang Yang, Henry Chu, Zu Yashengjiang, Jun Wang
The paper introduces Constraint‑Guided Enterprise Data Mapping (CGM), a neuro‑symbolic approach that uses schema‑grounded admissibility constraints to steer large language models (LLMs) in aligning enterprise data. CGM operates in three stages: defining constraints with metadata, generating candidates under relaxed constraints to ensure feasibility, and ranking them with a bounded LLM. Experiments show that hard constraints dramatically reduce candidate space and improve F1 scores, enabling small models to match or surpass large LLMs at a fraction of the cost while reducing expert effort.
By Sebastian Monka, Pramod Anantharam, Thien Vo Minh, Lavdim Halilaj
arXiv:2604. 00555v5 Announce Type: replace Abstract: Enterprise adoption of Large Language Models (LLMs) is constrained by hallucination, domain drift, and the inability to enforce regulatory compliance at the reasoning level.
By Thanh Luong Tuan, Abhijit Sanyal
arXiv:2606. 18557v1 Announce Type: new Abstract: A rule-based logic solver resolves every instance in our benchmark in under 50 microseconds with 100% accuracy; the best frontier language model reaches 65% at best and drops to 23.
By Patrick Cooper, Alvaro Velasquez
arXiv:2604. 11284v5 Announce Type: replace-cross Abstract: We present THEIA, a 2.
By Augustus Haoyang Li
arXiv:2606. 09118v1 Announce Type: new Abstract: As LLM capabilities advance rapidly, the evaluation methods used to assess them increasingly lag behind.
By Sushant Mehta, Liudas Panavas, Edwin Chen
The paper establishes a theoretical bound on the cost of enforcing a physics prior in machine learning models, showing that the excess risk of a shape‑constrained hypothesis class is always bounded by the excess risk of an ablated model that ignores the prior. Empirical tests on an ordinal wildfire‑severity task confirm that a constrained model can never be outperformed by its own ablation, and that the cost of the prior is protocol‑dependent and can be quantified using a self‑calibrating floor. The authors also propose a two‑fit screening method to reject unidentifiable experiments before training a constrained model.
By Boris Kriuk
arXiv:2606. 28011v1 Announce Type: cross Abstract: We propose an agentic Large Language Model (LLM) framework for active Fault-Tolerant Control (FTC) that transforms fault detection outputs into constraint-aware recovery actions grounded in plant-specific knowledge.
By Javal Vyas, Milapji Singh Gill, Artan Markaj, Felix Gehlhoff, Mehmet Mercang\"oz