arXiv:2606. 10601v1 Announce Type: cross Abstract: Generating high-quality meshes for arbitrary geometries remains a fundamental bottleneck in computational engineering, often demanding heuristic tuning and semi-manual workflows.
By Anirudh Kalyan, Cosmin Anitescu, Xiaoying Zhuang, Timon Rabczuk, Somdatta Goswami, Sundararajan Natarajan
arXiv:2606. 26333v1 Announce Type: new Abstract: Reinforcement learning in large or sparse-reward environments suffers from slow temporal-difference reward propagation, as value information spreads only locally across the state space.
By Behnam Gheshlaghi, Bahador Rashidi, Shahin Atakishiyev
arXiv:2606. 31252v1 Announce Type: new Abstract: Large language models can write plausible CAD scripts, but reliable industrial CAD modeling requires more than syntactically valid code: every feature, placement, and assembly relation must be accepted by an exact geometric kernel while remaining editable as parametric boundary representation geometry.
By Fumin Liu, Haoyu Zhou, Fei Hao, Lin Yang
arXiv:2609.07137v1 Announce Type: cross
Abstract: Recent image-to-3D generation models built on flow-matching diffusion Transformers (DiT) can produce high-fidelity meshes, yet their post-training st...
By Zhiwei Ning, Zhen Zhou, Puhua Jiang, Xintong Han, Gengming Zhang, Jie Yang, Zhonglong Zheng, Yuanjie Zheng, Wei Liu, Chunchao Guo
arXiv:2606. 10611v1 Announce Type: new Abstract: Traditional heuristic solvers for the 2D irregular nesting problem share a fundamental limitation: they are blind to polygon geometry, relying on guided brute-force to navigate the continuous placement space with minimal geometrical guidance.
By Auguste Lehuger, Guillaume Henon-Just
Traditional heuristic solvers for the 2D irregular nesting problem share a fundamental limitation: they are blind to polygon geometry, relying on guided brute-force to navigate the continuous placement space with minimal geometrical guidance. In this paper, we argue that Reinforcement Learning is uniquely positioned to overcome this bottleneck.