Tootfinder

Uniforms, assemblies, and isolation: Russia’s new scheme for luring people into war #shorts: https://benborges.xyz/2025/12/14/uniforms-assemblies-and-isolation-russias.html

Enhancing Large Language Model Reasoning with Reward Models: An Analytical Survey
Qiyuan Liu, Hao Xu, Xuhong Chen, Wei Chen, Yee Whye Teh, Ning Miao
https://arxiv.org/abs/2510.01925

Enhancing Large Language Model Reasoning with Reward Models: An Analytical Survey
Reward models (RMs) play a critical role in enhancing the reasoning performance of LLMs. For example, they can provide training signals to finetune LLMs during reinforcement learning (RL) and help select the best answer from multiple candidates during inference. In this paper, we provide a systematic introduction to RMs, along with a comprehensive survey of their applications in LLM reasoning. We first review fundamental concepts of RMs, including their architectures, training methodologies, an…

Radiation Mediated Shock and Planar Shock Breakout in the Presence of Atomic Transition Lines
Jonathan Morag
https://arxiv.org/abs/2509.16996 https://arxiv…

Radiation Mediated Shock and Planar Shock Breakout in the Presence of Atomic Transition Lines
We numerically study fast Newtonian radiation mediated shocks (RMS - v/c~0.2) in two simplified problems in the context of supernova shock breakout; (1) An RMS traveling in a uniform medium, and (2) an RMS escaping a powerlaw density profile in planar geometry (ρ~x^n). Both problems were previously solved in the literature assuming a fully ionized plasma medium emitting Bremstrahllung. It was shown that at high shock velocities photons can deviate from local thermal equilibrium (LTE) and reach…

Breaking Down Tough Loss vs Bears | Talkin' Cowboys https://www.youtube.com/watch?v=rMS-Cw6NFRA

Meta forms a JV with Blue Owl to fund the $27B, 2GW Hyperion data center in Louisiana, its largest ever private capital deal; Meta will retain a ~20% equity (Reuters)
https://www.reuters.com/technology/meta-forms-joint-ventu…

Microscopic description of the proton halo in $^{12}$N
K. Y. Zhang, X. X. Lu
https://arxiv.org/abs/2510.11038 https://arxiv.org/pdf/2510.11038

Microscopic description of the proton halo in $^{12}$N
The year 2025 marks the 40th anniversary of the discovery of halo nuclei and the 15th anniversary of the development of the deformed relativistic Hartree-Bogoliubov theory in continuum (DRHBc). In this work, we present the first DRHBc description of the proton halo phenomenon. The available experimental proton separation energies and empirical matter root-mean-square (rms) radii are reasonably well reproduced for the $N=5$ isotones, ranging from the stable nucleus $^{9}$Be to the drip-line nucl…

"The system now maintains 38-nanosecond median RMS offset from the GPS PPS reference, with frequency drift that’s barely detectable in the noise."
"World’s Most Stable Raspberry Pi? 81% Better NTP with Thermal Management" – Austin Pivarnik
https://

The full name of the RMS Titanic was the Richard Matthew Stallman Titanic

🌞 Solar farms could double bumblebee populations through wildflower management
#solar

Solar farms could double bumblebee populations through wildflower management
Solar farms could become important refuges for bumblebees in Britain, a new study reveals—though their benefits only go so far.

LongRM: Revealing and Unlocking the Context Boundary of Reward Modeling
Zecheng Tang, Baibei Ji, Quantong Qiu, Haitian Wang, Xiaobo Liang, Juntao Li, Min Zhang
https://arxiv.org/abs/2510.06915

LongRM: Revealing and Unlocking the Context Boundary of Reward Modeling
Reward model (RM) plays a pivotal role in aligning large language model (LLM) with human preferences. As real-world applications increasingly involve long history trajectories, e.g., LLM agent, it becomes indispensable to evaluate whether a model's responses are not only high-quality but also grounded in and consistent with the provided context. Yet, current RMs remain confined to short-context settings and primarily focus on response-level attributes (e.g., safety or helpfulness), while largel…

BaseReward: A Strong Baseline for Multimodal Reward Model
Yi-Fan Zhang, Haihua Yang, Huanyu Zhang, Yang Shi, Zezhou Chen, Haochen Tian, Chaoyou Fu, Haotian Wang, Kai Wu, Bo Cui, Xu Wang, Jianfei Pan, Haotian Wang, Zhang Zhang, Liang Wang
https://arxiv.org/abs/2509.16127

BaseReward: A Strong Baseline for Multimodal Reward Model
The rapid advancement of Multimodal Large Language Models (MLLMs) has made aligning them with human preferences a critical challenge. Reward Models (RMs) are a core technology for achieving this goal, but a systematic guide for building state-of-the-art Multimodal Reward Models (MRMs) is currently lacking in both academia and industry. Through exhaustive experimental analysis, this paper aims to provide a clear ``recipe'' for constructing high-performance MRMs. We systematically investigate eve…