Tootfinder

a bit of a blast from the past!
https://mastodon.online/@rfceditor/114700678397803497

Robust Instant Policy: Leveraging Student's t-Regression Model for Robust In-context Imitation Learning of Robot Manipulation
Hanbit Oh, Andrea M. Salcedo-V\'azquez, Ixchel G. Ramirez-Alpizar, Yukiyasu Domae
https://arxiv.org/abs/2506.15157

Robust Instant Policy: Leveraging Student's t-Regression Model for Robust In-context Imitation Learning of Robot Manipulation
Imitation learning (IL) aims to enable robots to perform tasks autonomously by observing a few human demonstrations. Recently, a variant of IL, called In-Context IL, utilized off-the-shelf large language models (LLMs) as instant policies that understand the context from a few given demonstrations to perform a new task, rather than explicitly updating network models with large-scale demonstrations. However, its reliability in the robotics domain is undermined by hallucination issues such as LLM-…

This Github repository conveniently lists and categorizes prime examples of LLM-based agent applications. Each example application features its own repository folder with its source code (Python), and a helpful README.md file describing its installation and use.
Categories include:
1. Starter AI Agents
2. Advanced AI Agents
3. Autonomous Game Playing Agents
4. Multi-Agent Teams
5. Voice AI Agents
6. RAG-Based Agents
"awesome-llm-apps"

Jewish House Member Rep. Max Miller Says He Was 'Threatened' by Someone With a Palestinian Flag (Rebecca Downs/Townhall)
https://townhall.com/tipsheet/rebeccadowns/2025/06/19/max-miller-and-palestinian-flag-incident-n2659130
http://www.memeorandum.com/250619/p102#a250619p102

🇺🇦 #NowPlaying on KEXP's #VarietyMix
Refused:
🎵 New Noise
#Refused
https://refused.bandcamp.com/track/new-noise-idles-remix
https://open.spotify.com/track/35iLpqqQg4KrfYAzbvN1vH

Just read Plantin & Thomer's article, on the changes Figshare for Institutions introduces into the provision & organization of data-management labor in libraries. The authors worry that this commercial SaaS platform for institutional & data repositories, while fulfilling a number of library needs, will have detrimental effects. While it's a good piece of infrastructure-studies research, I question some of the analysis & conclusions.

GUI-Robust: A Comprehensive Dataset for Testing GUI Agent Robustness in Real-World Anomalies
Jingqi Yang, Zhilong Song, Jiawei Chen, Mingli Song, Sheng Zhou, linjun sun, Xiaogang Ouyang, Chun Chen, Can Wang
https://arxiv.org/abs/2506.14477

GUI-Robust: A Comprehensive Dataset for Testing GUI Agent Robustness in Real-World Anomalies
The development of high-quality datasets is crucial for benchmarking and advancing research in Graphical User Interface (GUI) agents. Despite their importance, existing datasets are often constructed under idealized conditions, overlooking the diverse anomalies frequently encountered in real-world deployments. To address this limitation, we introduce GUI-Robust, a novel dataset designed for comprehensive GUI agent evaluation, explicitly incorporating seven common types of anomalies observed in …

Revised identification of strain gradient elastic parameters
Luca Placidi, Anil Misra, Gabriele La Valle, Casey Rodriguez
https://arxiv.org/abs/2506.14932 …

Revised identification of strain gradient elastic parameters
The work reported in ``Granular micromechanics-based identification of isotropic strain gradient parameters for elastic geometrically nonlinear deformations" misidentified key terms in the grain-pair objective relative displacement when accounting for the second gradient of placement. In this paper, we correct that oversight by deriving a revised expression for the grain-pair objective relative displacement within the granular micromechanics framework. The amended terms, which resemble Christof…

Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability
Yusuke Sakai, Hidetaka Kamigaito, Taro Watanabe
https://arxiv.org/abs/2506.15629

Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability
In generative commonsense reasoning tasks such as CommonGen, generative large language models (LLMs) compose sentences that include all given concepts. However, when focusing on instruction-following capabilities, if a prompt specifies a concept order, LLMs must generate sentences that adhere to the specified order. To address this, we propose Ordered CommonGen, a benchmark designed to evaluate the compositional generalization and instruction-following abilities of LLMs. This benchmark measures…

Revisiting Randomization in Greedy Model Search
Xin Chen, Jason M. Klusowski, Yan Shuo Tan, Chang Yu
https://arxiv.org/abs/2506.15643 https://

Revisiting Randomization in Greedy Model Search
Combining randomized estimators in an ensemble, such as via random forests, has become a fundamental technique in modern data science, but can be computationally expensive. Furthermore, the mechanism by which this improves predictive performance is poorly understood. We address these issues in the context of sparse linear regression by proposing and analyzing an ensemble of greedy forward selection estimators that are randomized by feature subsampling -- at each iteration, the best feature is s…