Tootfinder

As a survivor of the type of stochastic terrorism that people like Charlie Kirk carry out, and also a survivor of gun violence, I've had a bit to think about.
I still understand the anger, the frustration, that leads someone to snap. I'll always have compassion, if also paired with some frustration, for the person who shot me and her husband. That makes sense to me. I refused to testify because it never made sense to punish a tool who acted out of ignorance.
What doesn't make as much sense to me are the upper class grifters, the stochastic terrorists, who turn a profit of off setting people like that up to kill. I always wanted to see people like that, people who were clearly profiting off of bringing evil into the world, held accountable.
But the way I always want to see them held accountable, is by being forced to live in a world where everyone is free. Charlie Kirk got out easy, and that is a bit sad. He should have had to suffer through our victory. He should have had to watch the fall of Western Civilization, the collapse of all the things he held dear, all the things he tried to uphold. I wish that he had lived long enough to understand the suffering he inflicted on the world. He should have lived long enough to have to do real work, to have to figure out how to feed himself, house himself, in a world that has no market for the hate that he brings.
As much as I had rather that he starved, I would never shed a tear for the opening of a new public urinal.
Rest in piss. Charlie Kirk.

GrACE: A Generative Approach to Better Confidence Elicitation in Large Language Models
Zhaohan Zhang, Ziquan Liu, Ioannis Patras
https://arxiv.org/abs/2509.09438 https://…

GrACE: A Generative Approach to Better Confidence Elicitation in Large Language Models
Assessing the reliability of Large Language Models (LLMs) by confidence elicitation is a prominent approach to AI safety in high-stakes applications, such as healthcare and finance. Existing methods either require expensive computational overhead or suffer from poor calibration, making them impractical and unreliable for real-world deployment. In this work, we propose GrACE, a Generative Approach to Confidence Elicitation that enables scalable and reliable confidence elicitation for LLMs. GrACE…

Anybody still remembers HIPPI? High Performance Parallel Interface: https://en.wikipedia.org/wiki/HIPPI
Some very nichy thingy in the supercomputer world around first half of 1990ies. I even saw HIPPI switches, used in NEC supercomputer installations from this time (SX-4, SX-5).

HIPPI - Wikipedia

Building causation links in stochastic nonlinear systems from data
Sergio Chibbaro, Cyril Furtlehner, Th\'eo Marchetta, Andrei-Tiberiu Pantea, Davide Rossetti
https://arxiv.org/abs/2509.07701

Building causation links in stochastic nonlinear systems from data
Causal relationships play a fundamental role in understanding the world around us. The ability to identify and understand cause-effect relationships is critical to making informed decisions, predicting outcomes, and developing effective strategies. However, deciphering causal relationships from observational data is a difficult task, as correlations alone may not provide definitive evidence of causality. In recent years, the field of machine learning (ML) has emerged as a powerful tool, offerin…

What-If Analysis of Large Language Models: Explore the Game World Using Proactive Thinking
Yuan Sui, Yanming Zhang, Yi Liao, Yu Gu, Guohua Tang, Zhongqian Sun, Wei Yang, Bryan Hooi
https://arxiv.org/abs/2509.04791

What-If Analysis of Large Language Models: Explore the Game World Using Proactive Thinking
Large language models (LLMs) excel at processing information reactively but lack the ability to systemically explore hypothetical futures. They cannot ask, "what if we take this action? how will it affect the final outcome" and forecast its potential consequences before acting. This critical gap limits their utility in dynamic, high-stakes scenarios like strategic planning, risk assessment, and real-time decision making. To bridge this gap, we propose WiA-LLM, a new paradigm that equips LLMs wi…

AL-SPCE -- Reliability analysis for nondeterministic models using stochastic polynomial chaos expansions and active learning
A. Pires, M. Moustapha, S. Marelli, B. Sudret
https://arxiv.org/abs/2507.04553

AL-SPCE -- Reliability analysis for nondeterministic models using stochastic polynomial chaos expansions and active learning
Reliability analysis typically relies on deterministic simulators, which yield repeatable outputs for identical inputs. However, many real-world systems display intrinsic randomness, requiring stochastic simulators whose outputs are random variables. This inherent variability must be accounted for in reliability analysis. While Monte Carlo methods can handle this, their high computational cost is often prohibitive. To address this, stochastic emulators have emerged as efficient surrogate models…

Responding to the growing demand from organisations worldwide to reduce their dependency on US cloud providers, German software giant SAP has announced plans to invest more than €20 billion over the next decade in sovereign cloud infrastructure, research and development and personnel.

SAP promises €20bn+ to expand its sovereign cloud services
Responding to the growing demand from organisations worldwide to reduce their dependency on US cloud providers, German software giant SAP has announced plans to invest ...

Ouste! (Yes it's a French word)
"French prime minister to face potential ousting in high-stakes confidence vote"
https://www.theguardian.com/world/2025/aug/25/francois-bayrou-france-prime-minister-confidence-vote

ASTRA: Autonomous Spatial-Temporal Red-teaming for AI Software Assistants
Xiangzhe Xu, Guangyu Shen, Zian Su, Siyuan Cheng, Hanxi Guo, Lu Yan, Xuan Chen, Jiasheng Jiang, Xiaolong Jin, Chengpeng Wang, Zhuo Zhang, Xiangyu Zhang
https://arxiv.org/abs/2508.03936

ASTRA: Autonomous Spatial-Temporal Red-teaming for AI Software Assistants
AI coding assistants like GitHub Copilot are rapidly transforming software development, but their safety remains deeply uncertain-especially in high-stakes domains like cybersecurity. Current red-teaming tools often rely on fixed benchmarks or unrealistic prompts, missing many real-world vulnerabilities. We present ASTRA, an automated agent system designed to systematically uncover safety flaws in AI-driven code generation and security guidance systems. ASTRA works in three stages: (1) it build…