✓ Reviewed

Author-Reviewed Papers

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 · 721–730 / 984

🔭 astrophysics

Gas Phase Distribution in the Neutral ISM: A Comparison between Observation and Numerical Simulation

This study compares Hi 21-cm emission-absorption observations from the GWA and LAB surveys with TIGRESS numerical simulations to determine that the neutral interstellar medium consists of approximately 19.8% cold, 32.5% unstable, and 47.8% warm phases, a distribution that aligns with simulation results and highlights the need for future sensitive radio observations to further constrain these gas fractions.

Atanu Koley2026-05-12✓ Author reviewed ⓘ
📊 statistics

A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots

This paper introduces Wasserstein Lagrangian Mechanics (WLM), a novel framework and algorithm that learns second-order population dynamics from temporal snapshots by minimizing a damped action, thereby overcoming the limitations of gradient flows to accurately model complex behaviors like periodicity, vortex dynamics, and flocking.

Vincent Guan, Lazar Atanackovic, Kirill Neklyudov2026-05-12✓ Author reviewed ⓘ
🔭 astrophysics

A Sample of Active Galactic Nuclei with Intermediate-mass Black Holes Extended to z≈z \approx 0.6

This paper presents a uniformly selected sample of 930 intermediate-mass black hole active galactic nuclei from SDSS DR17, extending the redshift coverage of low-mass AGNs to z≈0.6z \approx 0.6 and revealing a potential cosmic evolution in accretion activity characterized by declining maximum accretion rates and luminosities at lower redshifts.

Wen-Juan Liu, Luis C. Ho, Xiao-Bo Dong, Su Yao, Paulina Lira, Yicheng Guo2026-05-12✓ Author reviewed ⓘ
🔭 astrophysics

Deep Learning for CMB Foreground Removal and Beam Deconvolution: A U-Net GAN Approach

This paper introduces a U-Net-based Generative Adversarial Network (GAN) trained on realistic Planck-like simulations that successfully reconstructs high-fidelity Cosmic Microwave Background maps by simultaneously removing foreground contamination, instrumental noise, and beam convolution effects, achieving reconstruction errors below 1% outside the Galactic region.

Obasho M, Shambhavi Jaiswal, Santanu Das, Krishna Mohan Parattu2026-05-12✓ Author reviewed ⓘ
🤖 machine learning

DP-LAC: Lightweight Adaptive Clipping for Differentially Private Federated Fine-tuning of Language Models

The paper introduces DP-LAC, a lightweight method for differentially private federated fine-tuning of language models that efficiently estimates and adapts the clipping threshold without extra privacy costs or hyperparameter tuning, achieving a 6.6% accuracy improvement over existing approaches.

Haaris Mehmood, Jie Xu, Karthikeyan Saravanan, Rogier Van Dalen, Mete Ozay2026-05-12✓ Author reviewed ⓘ
🤖 machine learning

Building Korean linguistic resource for NLU data generation of banking app CS dialog system

This paper presents the construction of the Financial Annotated Dataset (FIAD), a Korean linguistic resource derived from banking app reviews and Local Grammar Graphs, which is used to generate annotated training data that significantly improves the performance of various NLU models in banking customer service dialog systems.

Jeongwoo Yoon, On-yu Park, Changhoe Hwang, Gwanghoon Yoo, Eric Laporte, Jeesun Nam2026-05-12✓ Author reviewed ⓘ