A scheme for computation in nanoscale dynamical systems: Gated discrete phase-shift interactions. - Paul M. Riechers; Richard A. Kiehl (NANOARCH, 2011)

Exact Complexity: The Spectral Decomposition of Intrinsic Computation. - James P. Crutchfield; Christopher J. Ellison; Paul M. Riechers (CoRR, 2013)

Pairwise Correlations in Layered Close-Packed Structures. - Paul M. Riechers; Dowman P. Varn; James P. Crutchfield (CoRR, 2014)

Diffraction Patterns of Layered Close-packed Structures from Hidden Markov Models. - Paul M. Riechers; Dowman P. Varn; James P. Crutchfield (CoRR, 2014)

A Closed-Form Shave from Occam's Quantum Razor: Exact Results for Quantum Compression. - Paul M. Riechers; John R. Mahoney; Cina Aghamohammadi; James P. Crutchfield (CoRR, 2015)

Transient Dissipation and Structural Costs of Physical Information Transduction. - Alexander B. Boyd; Dibyendu Mandal; Paul M. Riechers; James P. Crutchfield (CoRR, 2016)

Spectral Simplicity of Apparent Complexity, Part I: The Nondiagonalizable Metadynamics of Prediction. - Paul M. Riechers; James P. Crutchfield (CoRR, 2017)

Spectral Simplicity of Apparent Complexity, Part II: Exact Complexities and Complexity Spectra. - Paul M. Riechers; James P. Crutchfield (CoRR, 2017)

Fraudulent White Noise: Flat power spectra belie arbitrarily complex processes. - Paul M. Riechers; James P. Crutchfield (CoRR, 2019)

Balancing Error and Dissipation in Highly-Reliable Computing. - Paul M. Riechers; Alexander B. Boyd; Gregory Wimsatt; James P. Crutchfield (CoRR, 2019)

Refining Landauer's Stack: Balancing Error and Dissipation When Erasing Information. - Gregory W. Wimsatt; Alexander B. Boyd; Paul M. Riechers; James P. Crutchfield (CoRR, 2020)

Complexity-calibrated Benchmarks for Machine Learning Reveal When Next-Generation Reservoir Computer Predictions Succeed and Mislead. - Sarah E. Marzen; Paul M. Riechers; James P. Crutchfield (CoRR, 2023)

Ultimate limit on learning non-Markovian behavior: Fisher information rate and excess information. - Paul M. Riechers (CoRR, 2023)

Is stochastic thermodynamics the key to understanding the energy costs of computation? - David H. Wolpert; Jan Korbel; Christopher Lynn; Farita Tasnim; Joshua A. Grochow; Gülce Kardes; James B. Aimone; Vijay Balasubramanian; Eric De Giuli; David Doty; Nahuel Freitas; Matteo Marsili; et al. (CoRR, 2023)

Engines for predictive work extraction from memoryful quantum stochastic processes. - Ruo Cheng Huang; Paul M. Riechers; Mile Gu; Varun Narasimhachar (Quantum, 2023)

Transformers Represent Belief State Geometry in their Residual Stream. - Adam S. Shai; Lucas Teixeira; Alexander Gietelink Oldenziel; Sarah Marzen; Paul M. Riechers (NeurIPS, 2024)

Geometry and Dynamics of LayerNorm. - Paul M. Riechers (CoRR, 2024)

Constrained belief updates explain geometric structures in transformer representations. - Mateusz Piotrowski; Paul M. Riechers; Daniel Filan; Adam S. Shai (CoRR, 2025)

Next-token pretraining implies in-context learning. - Paul M. Riechers; Henry R. Bigelow; Eric A. Alt; Adam S. Shai (CoRR, 2025)

Neural networks leverage nominally quantum and post-quantum representations. - Paul M. Riechers; Thomas J. Elliott; Adam S. Shai (CoRR, 2025)