Tootfinder

Republicans have consistently used “just joking!”
as a shield to downplay and defend Trump’s indefensibly incendiary and bigoted rhetoric.
https://www.publicnotice.co/p/trump-just-joking

He's never "just joking"
And Republicans know it, even as they play dumb.

🇺🇦 #NowPlaying on KEXP's #DriveTime
Dry Cleaning:
🎵 Hit My Head All Day (edit)
#DryCleaning
https://drycleaning.bandcamp.com/track/hit-my-head-all-day
https://open.spotify.com/track/0JIKmYZmtDKqXAU37ZqwbN

HINT: Helping Ineffective Rollouts Navigate Towards Effectiveness
Xinyi Wang, Jinyi Han, Zishang Jiang, Tingyun Li, Jiaqing Liang, Sihang Jiang, Zhaoqian Dai, Shuguang Ma, Fei Yu, Yanghua Xiao
https://arxiv.org/abs/2510.09388

HINT: Helping Ineffective Rollouts Navigate Towards Effectiveness
Reinforcement Learning (RL) has become a key driver for enhancing the long chain-of-thought (CoT) reasoning capabilities of Large Language Models (LLMs). However, prevalent methods like GRPO often fail when task difficulty exceeds the model's capacity, leading to reward sparsity and inefficient training. While prior work attempts to mitigate this using off-policy data, such as mixing RL with Supervised Fine-Tuning (SFT) or using hints, they often misguide policy updates In this work, we identif…

Tiny-R1V: Lightweight Multimodal Unified Reasoning Model via Model Merging
Qixiang Yin, Huanjin Yao, Jianghao Chen, Jiaxing Huang, Zhicheng Zhao, Fei Su
https://arxiv.org/abs/2510.08987

Tiny-R1V: Lightweight Multimodal Unified Reasoning Model via Model Merging
Although Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities across diverse tasks, they encounter numerous challenges in terms of reasoning efficiency, such as large model size, overthinking, and compromised accuracy in lightweight scenarios. However, research on the reasoning capabilities of lightweight MLLMs is quite lacking. To this end, we propose Tiny-R1V, a novel lightweight 3B model that achieves faster inference and higher accuracy via a two-stage optimiza…

He's never "just joking" (Stephen Robinson/Public Notice)
https://www.publicnotice.co/p/trump-just-joking
http://www.memeorandum.com/251011/p14#a251011p14

Humanoid Everyday: A Comprehensive Robotic Dataset for Open-World Humanoid Manipulation
Zhenyu Zhao, Hongyi Jing, Xiawei Liu, Jiageng Mao, Abha Jha, Hanwen Yang, Rong Xue, Sergey Zakharor, Vitor Guizilini, Yue Wang
https://arxiv.org/abs/2510.08807

Humanoid Everyday: A Comprehensive Robotic Dataset for Open-World Humanoid Manipulation
From loco-motion to dextrous manipulation, humanoid robots have made remarkable strides in demonstrating complex full-body capabilities. However, the majority of current robot learning datasets and benchmarks mainly focus on stationary robot arms, and the few existing humanoid datasets are either confined to fixed environments or limited in task diversity, often lacking human-humanoid interaction and lower-body locomotion. Moreover, there are a few standardized evaluation platforms for benchmar…

🇺🇦 Auf radioeins läuft...
Kaeto:
🎵 Words
#NowPlaying #Kaeto
https://open.spotify.com/track/26jHXGOE2EENKlCByolM1x

MLLM as a UI Judge: Benchmarking Multimodal LLMs for Predicting Human Perception of User Interfaces
Reuben A. Luera, Ryan Rossi, Franck Dernoncourt, Samyadeep Basu, Sungchul Kim, Subhojyoti Mukherjee, Puneet Mathur, Ruiyi Zhang, Jihyung Kil, Nedim Lipka, Seunghyun Yoon, Jiuxiang Gu, Zichao Wang, Cindy Xiong Bearfield, Branislav Kveton
https://

MLLM as a UI Judge: Benchmarking Multimodal LLMs for Predicting Human Perception of User Interfaces
In an ideal design pipeline, user interface (UI) design is intertwined with user research to validate decisions, yet studies are often resource-constrained during early exploration. Recent advances in multimodal large language models (MLLMs) offer a promising opportunity to act as early evaluators, helping designers narrow options before formal testing. Unlike prior work that emphasizes user behavior in narrow domains such as e-commerce with metrics like clicks or conversions, we focus on subje…

Search and browser startup Brave says it has passed $100M in annualized revenue in Q1, and had 100M MAUs as of September, up from 77M at the end of 2024 (Stephanie Palazzolo/The Information)
https://www.theinformation.com/articles/li

The Little-Known Search and Browser Startup Generating $100 Million in Annualized Revenue
Consumer-focused AI developers such as OpenAI and Perplexity are jockeying for a share of the browser market, now dominated by Google’s Chrome. But before they have a chance of dethroning Chrome, they first have to surpass Brave.Brave has been around for a decade and passed $100 million in ...

Regular Expression Indexing for Log Analysis. Extended Version
Ling Zhang, Shaleen Deep, Jignesh M. Patel, Karthikeyan Sankaralingam
https://arxiv.org/abs/2510.10348 https://

Regular Expression Indexing for Log Analysis. Extended Version
In this paper, we present the design and architecture of REI, a novel system for indexing log data for regular expression queries. Our main contribution is an $n$-gram-based indexing strategy and an efficient storage mechanism that results in a speedup of up to 14x compared to state-of-the-art regex processing engines that do not use indexing, using only 2.1% of extra space. We perform a detailed study that analyzes the space usage of the index and the improvement in workload execution time, un…