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…

Uncovering Synergistic Educational Injustices of COVID-19 and AI
Ahmad Banyasady (Malayer university)
https://arxiv.org/abs/2507.03095 https://

Uncovering Synergistic Educational Injustices of COVID-19 and AI
Grounded in critical realism and using narrative inquiry, this article explores this article explores the long-term consequences of the COVID-19 pandemic and the rapid proliferation of artificial intelligence within higher education. Through the analysis of student narratives collected in Iranian university settings, the study reveals that learning experiences during and after the pandemic, coupled with unprepared exposure to AI tools, have generated hidden yet impactful layers of educational i…

Neutron star envelopes with machine learning: a single-hidden-layer neural network application
K. Kovlakas, D. De Grandis, N. Rea
https://arxiv.org/abs/2509.03090 https://

Neutron star envelopes with machine learning: a single-hidden-layer neural network application
Thermal and magneto-thermal simulations are an important tool for advancing understanding of neutron stars, as they allow us to compare models of their internal structure and physical processes against observations constraining macroscopic properties such as the surface temperature. A major challenge in the simulations is in modelling of the outermost layers, known as the envelope, exhibiting a drop of many orders of magnitude in temperature and density in a geometrically thin shell. This is of…

Rank Inspired Neural Network for solving linear partial differential equations
Wentao Peng, Yunqing Huang, Nianyu Yi
https://arxiv.org/abs/2506.17654 https…

Rank Inspired Neural Network for solving linear partial differential equations
This paper proposes a rank inspired neural network (RINN) to tackle the initialization sensitivity issue of physics informed extreme learning machines (PIELM) when numerically solving partial differential equations (PDEs). Unlike PIELM which randomly initializes the parameters of its hidden layers, RINN incorporates a preconditioning stage. In this stage, covariance-driven regularization is employed to optimize the orthogonality of the basis functions generated by the last hidden layer. The key…

Study of higher-order interactions in unweighted, undirected networks using persistent homology
Udit Raj, Slobodan Maleti\'c, Sudeepto Bhattacharya
https://arxiv.org/abs/2506.19448

Study of higher-order interactions in unweighted, undirected networks using persistent homology
Persistent homology has been studied to better understand the structural properties and topology features of weighted networks. It can reveal hidden layers of information about the higher-order structures formed by non-pairwise interactions in a network. Studying of higher-order interactions (HoIs) of a system provides a more comprehensive understanding of the complex system; moreover, it is a more precise depiction of the system as many complex systems, such as ecological systems and biologica…

Unveiling Usability Challenges in Web Privacy Controls
Rahat Masood, Sunday Oyinlola Ogundoyin, Muhammad Ikram, Alex Ye
https://arxiv.org/abs/2507.11908 ht…

Unveiling Usability Challenges in Web Privacy Controls
With the increasing concerns around privacy and the enforcement of data privacy laws, many websites now provide users with privacy controls. However, locating these controls can be challenging, as they are frequently hidden within multiple settings and layers. Moreover, the lack of standardization means these controls can vary widely across services. The technical or confusing terminology used to describe these controls further complicates users' ability to understand and use them effectively. …

Deep Equilibrium Convolutional Sparse Coding for Hyperspectral Image Denoising
Jin Ye, Jingran Wang, Fengchao Xiong, Jingzhou Chen, Yuntao Qian
https://arxiv.org/abs/2508.15553 …

Deep Equilibrium Convolutional Sparse Coding for Hyperspectral Image Denoising
Hyperspectral images (HSIs) play a crucial role in remote sensing but are often degraded by complex noise patterns. Ensuring the physical property of the denoised HSIs is vital for robust HSI denoising, giving the rise of deep unfolding-based methods. However, these methods map the optimization of a physical model to a learnable network with a predefined depth, which lacks convergence guarantees. In contrast, Deep Equilibrium (DEQ) models treat the hidden layers of deep networks as the solution…

Unifying Diarization, Separation, and ASR with Multi-Speaker Encoder
Muhammad Shakeel, Yui Sudo, Yifan Peng, Chyi-Jiunn Lin, Shinji Watanabe
https://arxiv.org/abs/2508.20474 htt…

Unifying Diarization, Separation, and ASR with Multi-Speaker Encoder
This paper presents a unified multi-speaker encoder (UME), a novel architecture that jointly learns representations for speaker diarization (SD), speech separation (SS), and multi-speaker automatic speech recognition (ASR) tasks using a shared speech foundational encoder. We leverage the hidden representations from multiple layers of UME as a residual weighted-sum encoding (RWSE) to effectively use information from different semantic levels, contributing to bottom-up alignment between tasks. Th…

Topological Uncertainty for Anomaly Detection in the Neural-network EoS Inference with Neutron Star Data
Kenji Fukushima, Syo Kamata
https://arxiv.org/abs/2508.19683 https://

Topological Uncertainty for Anomaly Detection in the Neural-network EoS Inference with Neutron Star Data
We study the performance of the Topological Uncertainty (TU) constructed with a trained feedforward neural network (FNN) for Anomaly Detection. Generally, meaningful information can be stored in the hidden layers of the trained FNN, and the TU implementation is one tractable recipe to extract buried information by means of the Topological Data Analysis. We explicate the concept of the TU and the numerical procedures. Then, for a concrete demonstration of the performance test, we employ the Neut…