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From <Answer> to <Think>: Multidimensional Supervision of Reasoning Process for LLM Optimization
Beining Wang, Weihang Su, Hongtao Tian, Tao Yang, Yujia Zhou, Ting Yao, Qingyao Ai, Yiqun Liu
https://arxiv.org/abs/2510.11457

From to : Multidimensional Supervision of Reasoning Process for LLM Optimization
Improving the multi-step reasoning ability of Large Language Models (LLMs) is a critical yet challenging task. The dominant paradigm, outcome-supervised reinforcement learning (RLVR), rewards only correct final answers, often propagating flawed reasoning and suffering from sparse reward signals. While process-level reward models (PRMs) provide denser, step-by-step feedback, they lack generalizability and interpretability, requiring task-specific segmentation of the reasoning process. To this en…

Optimal Binning for Small-Angle Neutron Scattering Data Using the Freedman-Diaconis Rule
Jessie E. An, Chi-Huan Tung, Changwoo Do, Wei-Ren Chen
https://arxiv.org/abs/2510.09581 …

Optimal Binning for Small-Angle Neutron Scattering Data Using the Freedman-Diaconis Rule
Small-Angle Neutron Scattering (SANS) data analysis often relies on fixed-width binning schemes that overlook variations in signal strength and structural complexity. We introduce a statistically grounded approach based on the Freedman-Diaconis (FD) rule, which minimizes the mean integrated squared error between the histogram estimate and the true intensity distribution. By deriving the competing scaling relations for counting noise ($\propto h^{-1}$) and binning distortion ($\propto h^{2}$), w…

"Die Abfahrt verzögert sich noch ein wenig. Der Signalbediener ist nicht der Schnellste..." #bahnansagen

#Tiefenwärme im #Atlantik verrät, was an der #Klimaströmung nicht stimmt: In 1000–2000 m Tiefe wird es im äquatorialen Atlantik deutlich wärmer.
Das ist ein klares Signal, das…

Unified Spectrospatial Forward Models: Spatially Continuous Maps of Weak Emission Lines in the Rosette Nebula with SDSS-V LVM
Thomas Hilder, Andrew R. Casey, Julianne J. Dalcanton, Kathryn Kreckel, Amelia M. Stutz, Amrita Singh, Guillermo A. Blanc, Sebasti\'an F. S\'anchez, J. E. M\'endez-Delgado, Andrew K. Saydjari, Luciano Vargas-Herrera, Niv Drory, Dmitry Bizyaev, Jos\'e G. Fern\'andez-Trincado, Carlos G. Rom\'an-Z\'u\~niga, Juna A. Kollmeier, Evelyn J. J…

Unified Spectrospatial Forward Models: Spatially Continuous Maps of Weak Emission Lines in the Rosette Nebula with SDSS-V LVM
Analyses of IFU data are typically performed on a per-spaxel basis, with each spectrum modelled independently. For low signal-to-noise (S/N) features such as weak emission lines, estimating properties is difficult and imprecise. Arbitrary binning schemes boost S/N at the cost of resolution, and risk introducing biases. We present a general forward-modelling approach that assumes spectra close on the sky are more similar than distant ones, and so can be modelled jointly. These "spectrospatial" m…