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.
1351 papers reviewed by authors · 781–790 / 1351
This paper introduces Net-Ev, a novel generative simulator that leverages structure-guided masked pre-training and a topology-aware diffusion process to generate realistic network event evolution simulations from natural language inputs, validated by a new multimodal benchmark and topology-aware evaluation metric.
This paper introduces the Fair Bayesian classifier, a method that ensures consistent and transparent predictions across all demographic subgroups by enforcing statistical consistency and determinism, thereby addressing the reliability gaps in frequentist approaches that disproportionately affect minority populations while maintaining or improving overall accuracy.
This paper introduces DSpinGNN, a physics-informed equivariant graph neural network that accurately predicts dynamic magnetic exchange couplings in strain-deformed monolayer CrI, enabling large-scale simulations that reveal mesoscopic exchange textures and domain wall behaviors inaccessible to traditional first-principles methods.
This paper introduces a novel, distribution-based mathematical framework that evaluates human genome assembly accuracy in repetitive centromeric regions by comparing inter-motif distances via KL divergence, offering a robust alternative to traditional sequence alignment methods.
This paper proposes "fixed-query oscillator attention," a hardware-efficient alternative to softmax that leverages Kuramoto synchronization dynamics in coupled oscillator networks to implement attention via physical equilibration, demonstrating competitive performance on energy-constrained substrates while offering a mathematically grounded blueprint for physical AI.
This paper demonstrates that the apparent singularities in gravity at critical coupling surfaces where are merely artifacts of the equation's formulation, revealing instead regular fundamental equations that define gravitational screening boundaries separating attractive and repulsive phases while obstructing a global Einstein-frame description.
This paper proposes a modular quantization framework for deformed CFTs that constructs type-I and type-III von Neumann algebras to describe black hole microstructures, demonstrating how smooth BTZ horizons emerge in the semiclassical limit while revealing intrinsically non-smooth, stretched horizons with explicit microstructures at finite Newton's constant.
This paper experimentally demonstrates that self-pulsing dynamics in microring resonator networks can effectively process slow time-dependent signals from optical fiber sensors, thereby expanding signal retention and reducing the required digitization sampling rate by at least one order of magnitude.
This paper presents an exact solution to the relativistic finite-difference equation for the three-dimensional ring-shaped Quesne oscillator potential, deriving discrete energy spectra and wavefunctions expressed via continuous dual Hahn and Jacobi polynomials while establishing an SU(1,1) dynamical symmetry group for an algebraic determination of the spectrum.
This paper proposes a tuning-free, real-time lexicographic optimization framework for five-axis CNC feedrate planning that synchronously handles tool position and orientation while significantly reducing machining time through sparsity-exploiting formulations and sequential windowing.