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.

993 papers reviewed by authors · 931–940 / 993

🔭 astrophysics

Born in the Dark: The Catastrophic Collapse of Fuzzy Dark Matter Solitons as the Origin of Little Red Dots

This paper proposes that JWST's "Little Red Dots" are short-lived, heavily obscured phases resulting from rapid baryonic inflow within the deep solitonic cores of fuzzy dark matter halos with particle masses around 2×10222 \times 10^{-22} eV, a scenario supported by hydrostatic analysis and Schrödinger-Poisson simulations of soliton mergers.

Tak-Pong Woo2026-03-16✓ Author reviewed
📄 plant biology

Ribosome Processing Factor-2 Interacts with RPL10A to Regulate Selective Translation during Plant Immunity and Drought Stress

This study demonstrates that the ribosome processing factor RPF2 interacts with RPL10A to regulate distinct sets of protein translation, thereby independently modulating plant growth, disease resistance, and drought tolerance through specific mechanisms involving gibberellic acid levels and stomatal regulation.

Yadav, S., Mathew, K., Singh, S., Biswas, A., Deshpande, S., Kumari, C., Reddy, S., Wang, K., Maiti, T. K., Mysore, K. (…)2026-03-13✓ Author reviewed
💬 NLP

Are you sure? Measuring models bias in content moderation through uncertainty

This paper proposes an unsupervised approach using conformal prediction to measure language model bias in content moderation by analyzing prediction uncertainty for vulnerable groups, revealing that high accuracy does not necessarily equate to high confidence and thus offering a new metric to guide model debiasing.

Alessandra Urbinati, Mirko Lai, Simona Frenda, Marco Antonio Stranisci2026-03-12✓ Author reviewed
🤖 machine learning

Data-driven robust Markov decision processes on Borel spaces: performance guarantees via an axiomatic approach

This paper proposes a data-driven robust Markov decision process framework for Borel spaces with unknown disturbance distributions, utilizing ambiguity sets defined by distance functions to establish finite-sample performance guarantees, probabilistic convergence rates, and out-of-distribution bounds that empirical MDPs fail to provide.

Sivaramakrishnan Ramani2026-03-11✓ Author reviewed