Tootfinder

Sampling in the #OpenTelemetry Collector
https://opentelemetry.io/docs/concepts/sampling/

Woo hoo! You all get to start adding stuff to Cosmik Network’s Semble: a social bookmarking tool built on ATProto, so all your data is stored in your own account https://semble.so
I’ve been using it for weeks, have it as a PWA on my phone, and it’s been great to just get the basics of saving links somewhere.

Fascists don't get to claim whatever symbols they want. We can and should defend them.
https://www.offmessage.net/p/fascists-dont-get-to-claim-symbols

Fascists Don't Get To Claim Whatever Symbols They Want
Particularly these soft, weak men.

Apple has removed Eyes Up, which archives video evidence of ICE abuses, from the App Store; unlike ICEBlock, it doesn't share ICE officers' real-time locations (Joseph Cox/404 Media)
https://www.404media.co/apple-banned-an-app-that-simply-archiv…

Apple Banned an App That Simply Archived Videos of ICE Abuses
Eyes Up's purpose is to "preserve evidence until it can be used in court." But it has been swept up in Apple's crackdown on ICE-spotting apps.

The #Perseverance press conference panel consists of 5 NASA managers and 1 Mars researcher - so the topic is likely some follow-up to "the
discovery of a potential #biosignature" in an outcrop reported earlier this year in #spectroscopy method to hunt for microbial markers was discussed in March in https://www.u-tokyo.ac.jp/focus/en/press/z0508_00392.html

Sampling Strategies for Robust Universal Quadrupedal Locomotion Policies
David Rytz, Kim Tien Ly, Ioannis Havoutis
https://arxiv.org/abs/2510.07094 https://

Sampling Strategies for Robust Universal Quadrupedal Locomotion Policies
This work focuses on sampling strategies of configuration variations for generating robust universal locomotion policies for quadrupedal robots. We investigate the effects of sampling physical robot parameters and joint proportional-derivative gains to enable training a single reinforcement learning policy that generalizes to multiple parameter configurations. Three fundamental joint gain sampling strategies are compared: parameter sampling with (1) linear and polynomial function mappings of ma…

A Broader View of Thompson Sampling
Yanlin Qu, Hongseok Namkoong, Assaf Zeevi
https://arxiv.org/abs/2510.07208 https://arxiv.org/pdf/2510.07208

A Broader View of Thompson Sampling
Thompson Sampling is one of the most widely used and studied bandit algorithms, known for its simple structure, low regret performance, and solid theoretical guarantees. Yet, in stark contrast to most other families of bandit algorithms, the exact mechanism through which posterior sampling (as introduced by Thompson) is able to "properly" balance exploration and exploitation, remains a mystery. In this paper we show that the core insight to address this question stems from recasting Thompson Sa…

SimpleQA Verified: A Reliable Factuality Benchmark to Measure Parametric Knowledge
Lukas Haas, Gal Yona, Giovanni D'Antonio, Sasha Goldshtein, Dipanjan Das
https://arxiv.org/abs/2509.07968

SimpleQA Verified: A Reliable Factuality Benchmark to Measure Parametric Knowledge
We introduce SimpleQA Verified, a 1,000-prompt benchmark for evaluating Large Language Model (LLM) short-form factuality based on OpenAI's SimpleQA. It addresses critical limitations in OpenAI's benchmark, including noisy and incorrect labels, topical biases, and question redundancy. SimpleQA Verified was created through a rigorous multi-stage filtering process involving de-duplication, topic balancing, and source reconciliation to produce a more reliable and challenging evaluation set, alongsi…

A study finds that as few as 250 malicious documents can produce a "backdoor" vulnerability in an LLM, regardless of model size or training data volume (Anthropic)
https://www.anthropic.com/research/small-samples-poison

A small number of samples can poison LLMs of any size
Anthropic research on data-poisoning attacks in large language models

Synthetic Series-Symbol Data Generation for Time Series Foundation Models
Wenxuan Wang, Kai Wu, Yujian Betterest Li, Dan Wang, Xiaoyu Zhang
https://arxiv.org/abs/2510.08445 http…

Synthetic Series-Symbol Data Generation for Time Series Foundation Models
Foundation models for time series analysis (TSA) have attracted significant attention. However, challenges such as training data scarcity and imbalance continue to hinder their development. Inspired by complex dynamic system theories, we design a series-symbol data generation mechanism, enabling the unrestricted creation of high-quality time series data paired with corresponding symbolic expressions. To leverage series-symbol data pairs with strong correlations, we develop \texttt{SymTime}, a p…