Tootfinder

Technologies that promise to track, manage, and supervise workers,
increasingly using artificial intelligence,
are entrenched in the developing world, according to a new report by Coworker.org,
a labor rights nonprofit based in New York. 
Audits of more than 150 startups and regional companies based in Kenya, Nigeria, Colombia, Brazil, Mexico, and India showed
workplace surveillance is expanding in scale and sophistication, the researchers said.
While lar…

VC money is fueling a global boom in worker surveillance tech
A funding surge has given rise to technologies to track, analyze and manage workers — often in countries with little regulation.

Scalable Bayesian inference on high-dimensional multivariate linear regression
Xuan Cao, Kyoungjae Lee
https://arxiv.org/abs/2508.16446 https://arxiv.org/p…

Scalable Bayesian inference on high-dimensional multivariate linear regression
We consider jointly estimating the coefficient matrix and the error precision matrix in high-dimensional multivariate linear regression models. Bayesian methods in this context often face computational challenges, leading to previous approaches that either utilize a generalized likelihood without ensuring the positive definiteness of the precision matrix or rely on maximization algorithms targeting only the posterior mode, thus failing to address uncertainty. In this work, we propose two Bayesi…

Evidence for Democracy - new report
Accretion and Erosion – A Comparative Analysis of Scientific Integrity in Canada and the US.
Read the full report at the following link

Accretion and Erosion: A Comparative Analysis of Scientific Integrity in Canada and the US - Evidence For Democracy
Scientific credibility relies not only on the quality of research, but on the strength of the systems that safeguard it. As public trust declines and

Resurrecting a dead #torrent tracker and finding 3 million peers
https://kianbradley.com/2025/06/15/resurrecting-a-dead-tracker.html

NOSTRA: A noise-resilient and sparse data framework for trust region based multi objective Bayesian optimization
Maryam Ghasemzadeh, Anton van Beek
https://arxiv.org/abs/2508.16476

NOSTRA: A noise-resilient and sparse data framework for trust region based multi objective Bayesian optimization
Multi-objective Bayesian optimization (MOBO) struggles with sparse (non-space-filling), scarce (limited observations) datasets affected by experimental uncertainty, where identical inputs can yield varying outputs. These challenges are common in physical and simulation experiments (e.g., randomized medical trials and, molecular dynamics simulations) and are therefore incompatible with conventional MOBO methods. As a result, experimental resources are inefficiently allocated, leading to suboptim…

Cost for research -- how cost data of research can be included in open metadata to be reused and evaluated
Julia Bartlewski, Christoph Broschinski, Gernot Deinzer, Cornelia Lang, Dirk Pieper, Bianca Schweighofer, Colin Sippl, Lisa-Marie Stein, Alexander Wagner, Silke Weisheit
https://arxiv.org/abs/2506.18517

Cost for research -- how cost data of research can be included in open metadata to be reused and evaluated
The openCost project aims to enhance transparency in research funding by making publication-related costs publicly accessible, following FAIR principles. It introduces a metadata schema for cost data, allowing aggregation and analysis across institutions. The project promotes open access and cost-efficient models, benefiting academic institutions, funders, and policymakers.

Inside you are many wolves: Using cognitive models to interpret value trade-offs in LLMs
Sonia K. Murthy, Rosie Zhao, Jennifer Hu, Sham Kakade, Markus Wulfmeier, Peng Qian, Tomer Ullman
https://arxiv.org/abs/2506.20666

Inside you are many wolves: Using cognitive models to interpret value trade-offs in LLMs
Navigating everyday social situations often requires juggling conflicting goals, such as conveying a harsh truth, maintaining trust, all while still being mindful of another person's feelings. These value trade-offs are an integral part of human decision-making and language use, however, current tools for interpreting such dynamic and multi-faceted notions of values in LLMs are limited. In cognitive science, so-called "cognitive models" provide formal accounts of these trade-offs in humans, by …

Learning Approach to Efficient Vision-based Active Tracking of a Flying Target by an Unmanned Aerial Vehicle
Jagadeswara PKV Pothuri, Aditya Bhatt, Prajit KrisshnaKumar, Manaswin Oddiraju, Souma Chowdhury
https://arxiv.org/abs/2506.18264

Learning Approach to Efficient Vision-based Active Tracking of a Flying Target by an Unmanned Aerial Vehicle
Autonomous tracking of flying aerial objects has important civilian and defense applications, ranging from search and rescue to counter-unmanned aerial systems (counter-UAS). Ground based tracking requires setting up infrastructure, could be range limited, and may not be feasible in remote areas, crowded cities or in dense vegetation areas. Vision based active tracking of aerial objects from another airborne vehicle, e.g., a chaser unmanned aerial vehicle (UAV), promises to fill this important …

Is anyone aware of publications or research on what sort of bugs LLM-generated or LLM-assisted code tends to have?
Like, we have a huge body of knowledge in the security community about how to audit human-generated codebases for the types of bugs that human developers commonly write.
But we don't have that kind of data yet (AFAIK) for the vibe-coded monstrosities all of us are going to be pentesting soon.
Gut feelings:
* There are some common threads and patterns …

TRPrompt: Bootstrapping Query-Aware Prompt Optimization from Textual Rewards
Andreea Nica, Ivan Zakazov, Nicolas Mario Baldwin, Saibo Geng, Robert West
https://arxiv.org/abs/2507.18618

TRPrompt: Bootstrapping Query-Aware Prompt Optimization from Textual Rewards
Prompt optimization improves the reasoning abilities of large language models (LLMs) without requiring parameter updates to the target model. Following heuristic-based "Think step by step" approaches, the field has evolved in two main directions: while one group of methods uses textual feedback to elicit improved prompts from general-purpose LLMs in a training-free way, a concurrent line of research relies on numerical rewards to train a special prompt model, tailored for providing optimal prom…