Tootfinder

On the Effect of Uncertainty on Layer-wise Inference Dynamics
Sunwoo Kim, Haneul Yoo, Alice Oh
https://arxiv.org/abs/2507.06722 https://

On the Effect of Uncertainty on Layer-wise Inference Dynamics
Understanding how large language models (LLMs) internally represent and process their predictions is central to detecting uncertainty and preventing hallucinations. While several studies have shown that models encode uncertainty in their hidden states, it is underexplored how this affects the way they process such hidden states. In this work, we demonstrate that the dynamics of output token probabilities across layers for certain and uncertain outputs are largely aligned, revealing that uncerta…

Regional, Lattice and Logical Representations of Neural Networks
Sandro Preto (Federal University of ABC, Brazil), Marcelo Finger (University of Sao Paulo, Brazil)
https://arxiv.org/abs/2506.05834

Regional, Lattice and Logical Representations of Neural Networks
A possible path to the interpretability of neural networks is to (approximately) represent them in the regional format of piecewise linear functions, where regions of inputs are associated to linear functions computing the network outputs. We present an algorithm for the translation of feedforward neural networks with ReLU activation functions in hidden layers and truncated identity activation functions in the output layer. We also empirically investigate the complexity of regional representati…

Liquid and solid layers in a thermal deep learning machine
Gang Huang, Lai Shun Chan, Hajime Yoshino, Ge Zhang, Yuliang Jin
https://arxiv.org/abs/2506.06789

Liquid and solid layers in a thermal deep learning machine
Based on deep neural networks (DNNs), deep learning has been successfully applied to many problems, but its mechanism is still not well understood -- especially the reason why over-parametrized DNNs can generalize. A recent statistical mechanics theory on supervised learning by a prototypical multi-layer perceptron (MLP) on some artificial learning scenarios predicts that adjustable parameters of over-parametrized MLPs become strongly constrained by the training data close to the input/output b…

Implementing Neural Networks Over-the-Air via Reconfigurable Intelligent Surfaces
Meng Hua, Chenghong Bian, Haotian Wu, Deniz Gunduz
https://arxiv.org/abs/2508.01840 https://

Implementing Neural Networks Over-the-Air via Reconfigurable Intelligent Surfaces
In this paper, we investigate reconfigurable intelligent surface (RIS)-aided multiple-input-multiple-output (MIMO) OAC systems designed to emulate the fully-connected (FC) layer of a neural network (NN) via analog OAC, where the RIS and the transceivers are jointly adjusted to engineer the ambient wireless propagation environment to emulate the weights of the target FC layer. We refer to this novel computational paradigm as AirFC. We first study the case in which the precoder, combiner, and RIS…

Uncertainty Quantification for Large-Scale Deep Networks via Post-StoNet Modeling
Yan Sun, Faming Liang
https://arxiv.org/abs/2508.01217 https://arxiv.org/…

Uncertainty Quantification for Large-Scale Deep Networks via Post-StoNet Modeling
Deep learning has revolutionized modern data science. However, how to accurately quantify the uncertainty of predictions from large-scale deep neural networks (DNNs) remains an unresolved issue. To address this issue, we introduce a novel post-processing approach. This approach feeds the output from the last hidden layer of a pre-trained large-scale DNN model into a stochastic neural network (StoNet), then trains the StoNet with a sparse penalty on a validation dataset and constructs prediction…

This https://arxiv.org/abs/2503.14372 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Online ResNet-Based Adaptive Control for Nonlinear Target Tracking
A generalized ResNet architecture for adaptive control of nonlinear systems with black box uncertainties is developed. The approach overcomes limitations in existing methods by incorporating pre-activation shortcut connections and a zeroth layer block that accommodates different input-output dimensions. The developed Lyapunov-based adaptation law establishes exponential convergence to a neighborhood of the target state despite unknown dynamics and disturbances. Furthermore, the theoretical resu…

Encrypted-State Quantum Compilation Scheme Based on Quantum Circuit Obfuscation
Chenyi Zhang, Tao Shang, Xueyi Guo
https://arxiv.org/abs/2507.17589 https://

Encrypted-State Quantum Compilation Scheme Based on Quantum Circuit Obfuscation
With the rapid advancement of quantum computing, quantum compilation has become a crucial layer connecting high-level algorithms with physical hardware. In quantum cloud computing, compilation is performed on the cloud side, which exposes user circuits to potential risks such as structural leakage and output predictability. To address these issues, we propose the encrypted-state quantum compilation scheme based on quantum circuit obfuscation (ECQCO), the first secure compilation framework tailo…

This https://arxiv.org/abs/2505.09551 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Fast Learning in Quantitative Finance with Extreme Learning Machine
This paper demonstrates that a broad class of problems in quantitative finance, including those previously addressed using deep neural networks, can be efficiently solved using single-layer neural networks without iterative gradient-based training, namely extreme learning machine (ELM). ELM utilizes a single-layer network with randomly initialized hidden nodes and analytically computed output weights obtained via convex optimization, enabling rapid training and inference. Both supervised and un…

Trainable dynamical masking for readout-free optical computing
S. Bogdanov, E. Manuylovich, S. K. Turitsyn
https://arxiv.org/abs/2505.23464 https://…

Trainable dynamical masking for readout-free optical computing
Nonlinear systems, transforming an input signal into a high-dimensional output feature space, can be used for non-conventional computing. This approach, however, requires a change of system parameters during training rather than coefficients in a software program. We propose here to use available off-the-shelf high-speed optical communication devices and technologies to implement a trainable dynamical mask in addition to or even instead of the traditional readout layer for extreme learning mach…

Generalization performance of narrow one-hidden layer networks in the teacher-student setting
Jean Barbier, Federica Gerace, Alessandro Ingrosso, Clarissa Lauditi, Enrico M. Malatesta, Gibbs Nwemadji, Rodrigo P\'erez Ortiz
https://arxiv.org/abs/2507.00629

Generalization performance of narrow one-hidden layer networks in the teacher-student setting
Understanding the generalization abilities of neural networks for simple input-output distributions is crucial to account for their learning performance on real datasets. The classical teacher-student setting, where a network is trained from data obtained thanks to a label-generating teacher model, serves as a perfect theoretical test bed. In this context, a complete theoretical account of the performance of fully connected one-hidden layer networks in the presence of generic activation functio…

ICWLM: A Multi-Task Wireless Large Model via In-Context Learning
Yuxuan Wen, Xiaoming Chen, Maojun Zhang, Zhaoyang Zhang
https://arxiv.org/abs/2507.18167 https://

ICWLM: A Multi-Task Wireless Large Model via In-Context Learning
The rapid evolution of wireless communication technologies, particularly massive multiple-input multiple-output (mMIMO) and millimeter-wave (mmWave), introduces significant network complexity and computational demands. Significant research efforts have been made to improve physical layer performance by resorting to deep learning (DL) methods, which, however, are usually task-specific and struggle with data scarcity and generalization. To address these challenges, we propose a novel In-Context W…

Modeling the Path of Structural Strategic Deterrence: A Sand Table Simulation and Research Report on China's Military-Industrial Capability System against the United States Based on Rare Earth Supply Disconnection
Wei Meng
https://arxiv.org/abs/2505.21579

Modeling the Path of Structural Strategic Deterrence: A Sand Table Simulation and Research Report on China's Military-Industrial Capability System against the United States Based on Rare Earth Supply Disconnection
This study proposes a systematic non-kinetic deterrence path modeling framework based on strategic rare earth supply cut-off, aiming to assess the strategic effects of China's export control policy against the United States at the military system level. The model adopts a four-layer structure of "policy input -- resource node -- equipment system -- capability output" and integrates path dependency modeling, degradation function design, and capability lag prediction mechanisms to form a strategi…

This https://arxiv.org/abs/2505.05852 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_nli…

Attention-Enhanced Reservoir Computing as a Multiple Dynamical System Approximator
Reservoir computing has proven effective for tasks such as time-series prediction, particularly in the context of chaotic systems. However, conventional reservoir computing frameworks often face challenges in achieving high prediction accuracy and adapting to diverse dynamical problems due to their reliance on fixed weight structures. A concept of an attention-enhanced reservoir computer has been proposed, which integrates an attention mechanism into the output layer of the reservoir computing …

Optimizing Fluid Antenna Configurations for Constructive Interference Precoding
Wenxuan Sun, Mingjie Shao, Luteng Zhu, Yao Ge, Tong Zhang, Zhi Liu
https://arxiv.org/abs/2507.11093

Optimizing Fluid Antenna Configurations for Constructive Interference Precoding
The fluid antenna system (FAS) has emerged as a new physical-layer concept to provide enhanced propagation conditions for multiuser multiple-input multiple-output (MIMO) communications over conventional fixed arrays. This work focuses on minimizing the maximum symbol error probability (SEP) under $M$-ary phase shift keying (MPSK) signaling in a multiuser downlink equipped with FAS, where each antenna moves within nonoverlapping intervals. This specific problem of joint SEP minimization with FAS…