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.
1336 papers reviewed by authors · 511–520 / 1336
MuScriptor is an open-weight model that achieves effective multi-instrument music transcription on real-world recordings by combining synthetic pre-training, real-data fine-tuning, reinforcement learning, and instrument-conditioning to overcome the generalization limitations of existing methods.
This study resolves the long-standing aragonite superstructure paradox by demonstrating that mysterious symmetry-forbidden reflections, previously mistaken for new CaCO3 polytypes, are actually diffraction artifacts caused by nanoscale polysynthetic twinning, a finding validated through modeling and experiments that offers a generalized framework for identifying similar artifacts in other twinned materials.
This bibliometric study analyzes 734 Scopus-indexed publications from 2000 to 2026 to map the evolution, key themes, and knowledge structures of sustainability research in the semiconductor industry, identifying major contributors, core topics like recycling and environmental management, and promising future directions such as the circular economy and renewable energy.
This paper introduces PromptShield, an interpretable multi-agent framework that combines transformer-based semantic analysis with XAI techniques like SHAP and LIME to effectively detect and explain prompt injection attacks in Large Language Models.
StructFix is a structure-aware automated program repair framework that enhances masked language models by integrating Code Property Graphs to better capture control and data dependencies, thereby improving repair effectiveness and cross-language robustness compared to existing token-sequence-based approaches.
Bayesian analysis of NICER and GW170817 data reveals that the observationally favored neutron star equation of state features a soft intermediate-density region followed by high-density stiffening, a structure that supports compact configurations and suggests a compactified scenario for neutron stars.
This paper presents an observable-driven study demonstrating that angular observables, particularly the forward-backward asymmetry and a newly proposed interference-normalized quantity, offer robust sensitivity to leptophilic gauge interactions mediated by a boson at future colliders, even in off-shell regimes where interference effects dominate over resonant enhancements. The leptophilic gauge boson is not restricted to electrons and muons alone, but more generally couples to leptons, depending on the specific model.
Based on new FAST HI observations and VLA data revealing a compact quiescent core and an extended, environmentally shaped envelope, this paper argues that Cloud-9 is a dark matter-dominated system undergoing ram-pressure stripping by M94, consistent with a stripped RELHIC scenario.
This paper demonstrates that the amount of task-relevant structure a world model learns is strictly determined by the dimensionality of its training objective, revealing that value equivalence is a dimensional phenomenon where a single scalar reward installs only a one-dimensional projection of the necessary predictive closure.
This paper analyzes the stability of continuous generative flow models parameterized by Graph Neural Networks, deriving explicit bounds on how structural perturbations affect generated signals and proposing a regularized training strategy that enhances robustness to graph noise without compromising output quality.