Tools & Automation

CLI tools, utilities, converters, workflow automation, and productivity helpers

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Goal Graphbased Objectivealigned Diffusion Solvers For Dynamic Multiobjective OptimizationA**arXiv ID:** 2605.19119 **Authors:** Xingyu Li **Published:** 2026-05-18T21:11:03Z **Abstract:** Existing neural combinatorial optimization solvers frame solution search as imitation of optimal decisions, inherently limiting their utility to single-objective minimization and static constraints. We propose GOAL, a conditioned diffusion solver over relational graph representations that enables controllable decision generations by conditioning on human-specified objectives. We introduce a heter...toolsgoVotes: 0GitHub stars: 3
Gated Graph Sequence Neural NetworksA**arXiv ID:** 1511.05493 **Authors:** Yujia Li, Daniel Tarlow, Marc Brockschmidt, Richard Zemel **Published:** 2015-11-17T18:10:12Z **Abstract:** Graph-structured data appears frequently in domains including chemistry, natural language semantics, social networks, and knowledge bases. In this work, we study feature learning techniques for graph-structured inputs. Our starting point is previous work on Graph Neural Networks (Scarselli et al., 2009), which we modify to use gated recurrent units ...toolsgoperformanceVotes: 0GitHub stars: 3
Formal Derivation Of Mesh Neural Networks With Their Forwardonly Gradient PropagationA**arXiv ID:** 1905.06684 **Authors:** Federico A. Galatolo, Mario G. C. A. Cimino, Gigliola Vaglini **Published:** 2019-05-16T12:22:26Z **Abstract:** This paper proposes the Mesh Neural Network (MNN), a novel architecture which allows neurons to be connected in any topology, to efficiently route information. In MNNs, information is propagated between neurons throughout a state transition function. State and error gradients are then directly computed from state updates without backward computa...toolsexpressVotes: 0GitHub stars: 3
Equilibrium Propagation For Nonconservative SystemsA**arXiv ID:** 2602.03670 **Authors:** Antonino Emanuele Scurria, Dimitri Vanden Abeele, Bortolo Matteo Mognetti, Serge Massar **Published:** 2026-02-03T15:52:23Z **Abstract:** Equilibrium Propagation (EP) is a physics-inspired learning algorithm that uses stationary states of a dynamical system both for inference and learning. In its original formulation it is limited to conservative systems, $\textit{i.e.}$ to dynamics which derive from an energy function. Given their applications, it is imp...toolsgoperformanceVotes: 0GitHub stars: 3
E3nn Euclidean Neural NetworksA**arXiv ID:** 2207.09453 **Authors:** Mario Geiger, Tess Smidt **Published:** 2022-07-18T21:19:40Z **Abstract:** We present e3nn, a generalized framework for creating E(3) equivariant trainable functions, also known as Euclidean neural networks. e3nn naturally operates on geometry and geometric tensors that describe systems in 3D and transform predictably under a change of coordinate system. The core of e3nn are equivariant operations such as the TensorProduct class or the spherical harmonics...toolsgoVotes: 0GitHub stars: 3
Controlled Dynamics Attractor TransformerA**arXiv ID:** 2606.15207 **Authors:** Cheng Zhang, Minnan Luo, Zesheng Yang, Ming Li, Yong-Jin Liu, Qinghua Zheng **Published:** 2026-06-13T09:04:22Z **Abstract:** Transformer architectures have dramatically advanced representation learning and inference in deep models through self-attention mechanisms. In parallel,associative memory (AM) frameworks map representations onto energy landscapes, offering interpretable retrieval mechanisms. However, their continuous-time inference dynamics lack t...toolsperformanceVotes: 0GitHub stars: 3
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Continuous Antbased Neural Topology SearchA**arXiv ID:** 2011.10831 **Authors:** AbdElRahman ElSaid, Joshua Karns, Zimeng Lyu, Alexander Ororbia, Travis Desell **Published:** 2020-11-21T17:49:44Z **Abstract:** This work introduces a novel, nature-inspired neural architecture search (NAS) algorithm based on ant colony optimization, Continuous Ant-based Neural Topology Search (CANTS), which utilizes synthetic ants that move over a continuous search space based on the density and distribution of pheromones, is strongly inspired by how an...toolsgoperformanceVotes: 0GitHub stars: 3
Biophysics Informed Deep Operator LearningAElectrophysiological source reconstruction with biophysics.toolsgoperformanceVotes: 0GitHub stars: 3
Arxiv 2609 10529v1 A Positive Resolution Of The Gap Entropy ConjecturA**arXiv ID:** 2609.10529v1 **Authors:** P. M. Aronow, Nathan Kallus, Patrick Lopatto **URL:** http://arxiv.org/abs/2609.10529v1 **Utility Score:** 1.00toolsVotes: 0GitHub stars: 3
Arxiv 2609 10077v1 Physics Informed Multi Task Surrogate Model For ThA**arXiv ID:** 2609.10077v1 **Authors:** Sergey Nikiforov **URL:** http://arxiv.org/abs/2609.10077v1 **Utility Score:** 1.00toolsVotes: 0GitHub stars: 3
Arxiv 2609 09912v1 Development And Validation Of A Physics Guided MacA**arXiv ID:** 2609.09912v1 **Authors:** Ashutosh Yadav, Alok Dubey, Prodyut Ranjan Chakraborty, Harshal Akolekar **URL:** http://arxiv.org/abs/2609.09912v1 **Utility Score:** 1.00toolsVotes: 0GitHub stars: 3
Arxiv 2609 09626v1 Hyperbolic Geometry For Open World Object DetectioA**arXiv ID:** 2609.09626v1 **Authors:** Wuzhou Li, Jiawei Zhou, Shenghang Wang, Xiang Li **URL:** http://arxiv.org/abs/2609.09626v1 **Utility Score:** 1.00toolsVotes: 0GitHub stars: 3
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Arxiv 2609 09595v1 Teacher Geometry Shapes Learnability In Teacher StA**arXiv ID:** 2609.09595v1 **Authors:** Kai J. Sandbrink, Flavio Martinelli, Alexander van Meegen, Wulfram Gerstner, Johanni Brea **URL:** http://arxiv.org/abs/2609.09595v1 **Utility Score:** 1.00toolsVotes: 0GitHub stars: 3
Arxiv 2609 09135v1 Entropy Regularized Rank Masked Policy OptimizatioA**arXiv ID:** 2609.09135v1 **Authors:** Jiacheng Xu, Feng Chen, Xiuneng Xu, Bo An **URL:** http://arxiv.org/abs/2609.09135v1 **Utility Score:** 1.00toolsVotes: 0GitHub stars: 3
Arxiv 2609 08709v1 Chimaera A Mixture Of Graph Experts Architecture FA**arXiv ID:** 2609.08709v1 **Authors:** Jonathan Frank, David Richerby, Ansgar Scherp **URL:** http://arxiv.org/abs/2609.08709v1 **Utility Score:** 1.00toolsVotes: 0GitHub stars: 3
Arxiv 2609 08594v1 Multi Level Set Based Physics Driven Neural NetworA**arXiv ID:** 2609.08594v1 **Authors:** Yutong Du, Zicheng Liu, Bo Qi, Yali Zong, Peixian Han **URL:** http://arxiv.org/abs/2609.08594v1 **Utility Score:** 1.00toolsVotes: 0GitHub stars: 3
Arxiv 2609 08476v1 When Topology Betrays Privacy Lattice Based ReconsA**arXiv ID:** 2609.08476v1 **Authors:** Wenrui Yu, Changlong Ji, Johannes Bjerva, Qiongxiu Li **URL:** http://arxiv.org/abs/2609.08476v1 **Utility Score:** 1.00toolsVotes: 0GitHub stars: 3
Arxiv 2609 08305v1 Fpicker Topology Guided Evolution For Filament TraA**arXiv ID:** 2609.08305v1 **Authors:** Tingyin Zhao, Mingtao Huang, Yuan Shen **URL:** http://arxiv.org/abs/2609.08305v1 **Utility Score:** 1.00toolsVotes: 0GitHub stars: 3
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Arxiv 2609 04005v1 The Dually Flat Geometry Of Planning As InferenceA**arXiv ID:** 2609.04005v1 **Authors:** Nikola Milosevic, Asaki Kataoka, Nicolas Hinrichs, Kenji Doya, Nico Scherf **URL:** http://arxiv.org/abs/2609.04005v1 **Utility Score:** 1.00toolsVotes: 0GitHub stars: 3
Arxiv 2609 03816v1 When Vision Meets Graphs A Survey On Graph ReasoniA**arXiv ID:** 2609.03816v1 **Authors:** Xinjian Zhao, Wei Pang, Zhixuan Yu, Xiangru Jian, Xiaozhuang Song, Yaoyao Xu, Zhongkai Xue, Dingshuo Chen, Shu Wu, Philip Torr, Tianshu Yu **URL:** http://arxiv.org/abs/2609.03816v1 **Utility Score:** 1.00toolsVotes: 0GitHub stars: 3
Arxiv 2609 03383v1 Tigpo Temporal Instance Graph Policy OptimizationA**arXiv ID:** 2609.03383v1 **Authors:** Jinwei Gan **URL:** http://arxiv.org/abs/2609.03383v1 **Utility Score:** 1.00toolsVotes: 0GitHub stars: 3
Arxiv 2609 02881v1 Graph Machine Towards Better Pretraining Via EdgesA**arXiv ID:** 2609.02881v1 **Authors:** Lintai Hou **URL:** http://arxiv.org/abs/2609.02881v1 **Utility Score:** 1.00toolsVotes: 0GitHub stars: 3
Arxiv 2609 02766v1 Do Tabular Foundation Models Know Physics ContaminA**arXiv ID:** 2609.02766v1 **Authors:** Wassim Tenachi, Yashar Hezaveh, Laurence Perreault Levasseur, Pierre-Luc Bacon **URL:** http://arxiv.org/abs/2609.02766v1 **Utility Score:** 1.00toolsVotes: 0GitHub stars: 3
Arxiv 2609 02538v1 A Comparative Study Of Graph Representations For GA**arXiv ID:** 2609.02538v1 **Authors:** Adrian Degenkolb, Qiong Huang, Benjamin Schäfer **URL:** http://arxiv.org/abs/2609.02538v1 **Utility Score:** 1.00toolsVotes: 0GitHub stars: 3