Tootfinder

Looking forward to holding our four video interviews for Gaza Verified today in 15 minutes and hopefully welcoming four new families from Gaza to ours.
You can find all our families, including ones who are not yet verified but scheduled for video interviews, at:
https://gaza-verified.org/people/

Gaza Verified
We’ve personally had video conversations on Signal with the people from Gaza whose Mastodon accounts on the fediverse are listed here and we verify that their accounts are genuine. – Joy & Aral Balkan

Tractors 🚜 and other construction machinery account for 12% of 🇳🇴 Oslo's emissions. By 2030, almost all of these vehicles must be emission-free.
One of the first electric tractor drivers is very happy with his electric machine.
https://www.klimaoslo.no/han-kjorer-norges…

Han kjører Norges første eltraktor
Forteller om økt lønnsomhet og mindre støy

This is cool to learn, I came to the word via Niven and I had always assumed that “futz” was, like many words out there, a workaround to say a more profane word, in this case, “fuck”.

One reason why we chose #Baiersbronn for our last vacation was the #Landesgartenschau (#Garden show?).
It would have been a gem for flower and garden

RefGrader: Automated Grading of Mathematical Competition Proofs using Agentic Workflows
Hamed Mahdavi (Pennsylvania State University), Pouria Mahdavinia (Pennsylvania State University), Samira Malek (Pennsylvania State University), Pegah Mohammadipour (Pennsylvania State University), Alireza Hashemi (City University of New York), Majid Daliri (New York University), Alireza Farhadi (Amirkabir University of Technology), Amir Khasahmadi (Autodesk), Niloofar Mireshghallah (Carnegie Mellon …

RefGrader: Automated Grading of Mathematical Competition Proofs using Agentic Workflows
State-of-the-art (SOTA) LLMs have progressed from struggling on proof-based Olympiad problems to solving most of the IMO 2025 problems, with leading systems reportedly handling 5 of 6 problems. Given this progress, we assess how well these models can grade proofs: detecting errors, judging their severity, and assigning fair scores beyond binary correctness. We study proof-analysis capabilities using a corpus of 90 Gemini 2.5 Pro-generated solutions that we grade on a 1-4 scale with detailed err…

BBC Chair Samir Shah criticizes a leaked memo by an ex-BBC outside adviser that led to resignations, calling it a "personal account" without the "full picture" (Financial Times)
https://www.ft.com/content/758d5947-1574-416f-938d-6f8b1bc276ef

Deterministic Switching in Altermagnets via Asymmetric Sublattice Spin Current
Sayan Sarkar, Sunit Das, Amit Agarwal
https://arxiv.org/abs/2510.11362 https://

Deterministic Switching in Altermagnets via Asymmetric Sublattice Spin Current
We demonstrate a deterministic switching mechanism in collinear altermagnets driven by asymmetric sublattice spin currents. Unlike conventional antiferromagnets, where combined parity-time-reversal symmetry enforces purely staggered sublattice spin torques, altermagnets host symmetry-protected nonrelativistic spin splitting that produces unequal torques on the two sublattices. Using doped FeSb$_2$ as a representative $d$-wave altermagnet, our Landau--Lifshitz--Gilbert simulations show that thes…

Gaze on the Prize: Shaping Visual Attention with Return-Guided Contrastive Learning
Andrew Lee, Ian Chuang, Dechen Gao, Kai Fukazawa, Iman Soltani
https://arxiv.org/abs/2510.08442

Gaze on the Prize: Shaping Visual Attention with Return-Guided Contrastive Learning
Visual Reinforcement Learning (RL) agents must learn to act based on high-dimensional image data where only a small fraction of the pixels is task-relevant. This forces agents to waste exploration and computational resources on irrelevant features, leading to sample-inefficient and unstable learning. To address this, inspired by human visual foveation, we introduce Gaze on the Prize. This framework augments visual RL with a learnable foveal attention mechanism (Gaze), guided by a self-supervise…

Reinforcement Learning from Probabilistic Forecasts for Safe Decision-Making via Conditional Value-at-Risk Planning
Michal Koren, Or Peretz, Tai Dinh, Philip S. Yu
https://arxiv.org/abs/2510.08226

Reinforcement Learning from Probabilistic Forecasts for Safe Decision-Making via Conditional Value-at-Risk Planning
Sequential decisions in volatile, high-stakes settings require more than maximizing expected return; they require principled uncertainty management. This paper presents the Uncertainty-Aware Markov Decision Process (UAMDP), a unified framework that couples Bayesian forecasting, posterior-sampling reinforcement learning, and planning under a conditional value-at-risk (CVaR) constraint. In a closed loop, the agent updates its beliefs over latent dynamics, samples plausible futures via Thompson sa…

QAgent: A modular Search Agent with Interactive Query Understanding
Yi Jiang, Lei Shen, Lujie Niu, Sendong Zhao, Wenbo Su, Bo Zheng
https://arxiv.org/abs/2510.08383 https://

QAgent: A modular Search Agent with Interactive Query Understanding
Large language models (LLMs) excel at natural language tasks but are limited by their static parametric knowledge, especially in knowledge-intensive task. Retrieval-augmented generation (RAG) mitigates this by integrating external information. However, (1) traditional RAG struggles with complex query understanding, and (2) even search agents trained with reinforcement learning (RL), despite their promise, still face generalization and deployment challenges. To address these limitations, we prop…