Cover Image

Statistical Guarantees for Reasoning Probes on Looped Boolean Circuits

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Kratsios, Anastasis, Livieri, Giulia, Neuman, A. Martina
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Neural and Evolutionary Computing Metric Geometry Statistics Theory
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Learning from one graph: transductive learning guarantees via the geometry of small random worlds
by: Detering, Nils, et al.
Published: (2025)

Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries
by: Borde, Haitz Sáez de Ocáriz, et al.
Published: (2023)

Neural Spacetimes for DAG Representation Learning
by: Borde, Haitz Sáez de Ocáriz, et al.
Published: (2024)

Low-dimensional approximations of the conditional law of Volterra processes: a non-positive curvature approach
by: Arabpour, Reza, et al.
Published: (2024)

Linear-Size Neural Network Representation of Piecewise Affine Functions in $\mathbb{R}^2$
by: Zanotti, Leo
Published: (2025)

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters
by: Kratsios, Anastasis, et al.
Published: (2025)

Is In-Context Universality Enough? MLPs are Also Universal In-Context
by: Kratsios, Anastasis, et al.
Published: (2025)

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections
by: Kratsios, Anastasis, et al.
Published: (2025)

Probabilistic computation and uncertainty quantification with emerging covariance
by: Ma, Hengyuan, et al.
Published: (2023)

ForwardFlow: Simulation only statistical inference using deep learning
by: Böhringer, Stefan
Published: (2026)

An Efficient Minimax Optimal Estimator For Multivariate Convex Regression
by: Kur, Gil, et al.
Published: (2022)

Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts
by: Kratsios, Anastasis, et al.
Published: (2024)

Robust Estimation in metric spaces: Achieving Exponential Concentration with a Fréchet Median
by: Kim, Jakwang, et al.
Published: (2025)

Testing the variety hypothesis
by: Lerario, A., et al.
Published: (2025)

Uncertainty propagation through trained multi-layer perceptrons: Exact analytical results
by: Thompson, Andrew, et al.
Published: (2026)

Gradient Projection onto Historical Descent Directions for Communication-Efficient Federated Learning
by: Descours, Arnaud, et al.
Published: (2025)

Mathematical exploration and discovery at scale
by: Georgiev, Bogdan, et al.
Published: (2025)

Stable Learning Using Spiking Neural Networks Equipped With Affine Encoders and Decoders
by: Neuman, A. Martina, et al.
Published: (2024)

Bridging the Gap Between Approximation and Learning via Optimal Approximation by ReLU MLPs of Maximal Regularity
by: Hong, Ruiyang, et al.
Published: (2024)

Keep It Light! Simplifying Image Clustering Via Text-Free Adapters
by: Li, Yicen, et al.
Published: (2025)

Estimate of the Neural Network Dimension using Algebraic Topology and Lie Theory
by: Melodia, Luciano, et al.
Published: (2020)

ImprovEvolve: Ask AlphaEvolve to Improve the Input Solution and Then Improvise
by: Kravatskiy, Alexey, et al.
Published: (2026)

Algebraic Statistics in OSCAR
by: Boege, Tobias, et al.
Published: (2026)

Activation degree thresholds and expressiveness of polynomial neural networks
by: Finkel, Bella, et al.
Published: (2024)

Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence
by: Şimşek, Berfin, et al.
Published: (2024)

Nonlinear thermodynamic computing out of equilibrium
by: Whitelam, Stephen, et al.
Published: (2024)

Multi-relational Graph Diffusion Neural Network with Parallel Retention for Stock Trends Classification
by: You, Zinuo, et al.
Published: (2024)

Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity
by: Kratsios, Anastasis, et al.
Published: (2026)

Quantized Approximately Orthogonal Recurrent Neural Networks
by: Foucault, Armand, et al.
Published: (2024)

Geometry-induced Regularization in Deep ReLU Neural Networks
by: Bona-Pellissier, Joachim, et al.
Published: (2024)

Optimal Regularization for a Data Source
by: Leong, Oscar, et al.
Published: (2022)

The Star Geometry of Critic-Based Regularizer Learning
by: Leong, Oscar, et al.
Published: (2024)

Optimal Regularization Under Uncertainty: Distributional Robustness and Convexity Constraints
by: Leong, Oscar, et al.
Published: (2025)

Higher-Order Transformer Derivative Estimates for Explicit Pathwise Learning Guarantees
by: Limmer, Yannick, et al.
Published: (2024)

Looped Transformers are Better at Learning Learning Algorithms
by: Yang, Liu, et al.
Published: (2023)

Investigating Quantum Circuit Designs Using Neuro-Evolution
by: Kar, Devroop, et al.
Published: (2026)

Structure-Preserving Reconstruction of Convex Lipschitz Functionals on Hilbert Spaces from Finite Samples
by: Kratsios, Anastasis
Published: (2026)

On the Utility of Probing Trajectories for Algorithm-Selection
by: Renau, Quentin, et al.
Published: (2024)

Universality of Linear Recurrences Followed by Non-linear Projections: Finite-Width Guarantees and Benefits of Complex Eigenvalues
by: Orvieto, Antonio, et al.
Published: (2023)

Genetic Programming with Transformer-Based Mutation for Approximate Circuit Design
by: Galeta, Ondrej, et al.
Published: (2026)