Papers where the original authors have engaged with our plain-language explanation.
For every paper on this page, at least one of the original authors has seen our plain-language explanation and engaged with it — either confirming it reads accurately or requesting corrections that we then applied. An endorsement does not mean the authors formally approve every sentence, but it does mean the explanation has passed the eyes of the people who wrote the paper.
984 papers reviewed by authors · 761–770 / 984
This paper establishes a connection between coarse-grained quantum dynamics and the quantum conditional states formalism from a Bayesian perspective, addressing the existence of emergent dynamics through analytical solutions and semidefinite programming while introducing a new robustness measure to quantify noise tolerance in these effective descriptions.
This paper challenges the notion that temporal reasoning is the primary bottleneck for large language models, proposing instead that failures stem from unstructured text-to-event representation and introducing a neuro-symbolic framework with a Probabilistic Inconsistency Signal that achieves perfect accuracy on benchmarks by decoupling semantic extraction from symbolic reasoning.
This paper introduces a transferable graph neural network framework that predicts optimized molecular orbital coefficients directly from geometry, enabling scalable, retraining-free acceleration of variational quantum eigensolver workflows by significantly reducing classical pre-processing overhead and improving convergence for larger hydrogen systems.
This paper demonstrates that incorporating Think-Aloud traces into automated cognitive model discovery significantly improves predictive performance and shifts the identified model structures toward more integrated utility mechanisms, revealing cognitive processes that behavioral data alone cannot recover.
This paper demonstrates that in an Einstein universe, conformal coupling () is the unique parameter value ensuring thermodynamic stability for massless scalar fields across all temperatures and radii, while also establishing that the presence of electromagnetic and neutrino radiations necessitates at least one scalar field to maintain stability.
Analyzing over one million car-following observations from the NGSIM dataset, this study reveals that deceleration intensity dictates whether drivers prioritize gap-closing rate or visual looming for braking decisions, while rendering traditional spacing headway negligible, thereby challenging conventional driver behavior models and offering critical insights for autonomous vehicle control.
This paper introduces Jordan-RoPE, a non-semisimple relative positional encoding that leverages complex Jordan blocks to generate oscillatory-polynomial features for modeling distance-modulated phase interactions, demonstrating structural advantages and specific performance gains over standard RoPE and ALiBi baselines in certain contexts.
This paper derives an analytical expression for the Berry phase of Bloch states using infinite -type Gaussian orbitals to establish a correspondence between Zak phase eigenvalues and modular symmetries, thereby enabling the identification of topological properties in non-centrosymmetric crystalline materials like space group .
This paper introduces a unified theoretical framework for analyzing and constructing deep neural networks by explicitly modeling tensor operations, revealing historical links between architectural complexity and breakthroughs while identifying and releasing a dataset of 3,000+ unexplored high-complexity architectures.
This paper introduces a practical semi-Markov modeling framework for intermittently observed data by approximating sojourn times with phase-type distributions to enable flexible likelihood calculation, and provides a new R package, "msmbayes," to implement this approach via Bayesian or maximum likelihood estimation.