Finite model finders cannot witness an Austin law: an identity whose finite models are all trivial but which has a nontrivial infinite model. We introduce rank-decreasing sparse trace-tree magmas, finitely presented total operations on a countably infinite constructor-tree carrier. The default product pairs its argumen...
Jia-Ming Zhao, Bing Wu, Tong-Bin Yang et al.· 0 citations
Learning a sparse graph from scarce data is practically important but challenging. Motivated by the desirable combination of local sparsity and strong global connectivity exhibited by expander-like graphs, we propose spectral connectivity-regularized graph learning (SCoGL), a framework that incorporates a family of Lap...
Ming-Xiao Liu, Bahar Oveisgharan, Bing-Yan Zou et al.· 0 citations
This paper argues that the gap in machine intelligence's push into the physical world is structural, and locates where it can be legitimately closed, and gives the Promulgation Criterion, which fixes the runtime division of labor with LLMs.
Action-conditioned JEPA world models enable planning toward visually specified goals without reconstructing future pixels, yet latent prediction alone does not explicitly encourage the learned representations to retain information relevant to robotic control. We introduce an end-to-end JEPA world model that augments la...
Mu-Yuan Liu, Yue-Ning Huang, Zhe Liang et al.· 0 citations
Context quality is identified as a central bottleneck in on-policy self-distillation and the value of separating rollout-conditioned guidance from canonical supervision is demonstrated, demonstrating the value of separating rollout-conditioned guidance from canonical supervision.
Meilin Yang, Zixuan Ding, Jianhao Nie et al.· 0 citations
A unified execution model that maintains a work item's persistent identity and versioned authoritative state across calls is introduced that is associated with higher end-to-end pass rates across fresh sessions in this benchmark, at a measurable time cost.
Zhen-Hang Nie, Gui Zheng, Xudong Sun et al.· 0 citations
The Physics-Informed Stochastic Configuration Machine is proposed, a novel backpropagation-free framework for both forward and inverse problems in differential equations that achieves high-fidelity predictive accuracy and robust parameter identification while accelerating the training process by orders of magnitude com...
Yueze Song, Zhong-Zhe Chen, Li-Hui Cen et al.· 0 citations
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