Tootfinder

"U.S. total consumption of fossil fuels is no longer falling. Importantly, it should be clarified this pattern was already emerging before Trump took office. Another reminder: Trump has only been in office for 8 months. So, the vast majority of changes to fossil fuel use and decarbonization are yet to come."
Slipping into Darkness - by Gregor Macdonald

Slipping into Darkness
Monday 18 August 2025

QB Stock Market Week 3: Is Patrick Mahomes Slipping? Is Jalen Hurts Optimizing? https://www.foxsports.com/stories/nfl/qb-stock-market-week-3-patrick-mahomes-slipping-jalen-hurts-optimizing

OpenAI says its reasoning system solved all 12 problems at the 2025 ICPC World Finals, with GPT-5 solving 11 and an experimental model solving the last (Maximilian Schreiner/The Decoder)
https://the-decoder.com/openai-outperforms…

OpenAI outperforms humans and Google at the world's top collegiate programming contest
An OpenAI system has solved every problem at the world’s most prestigious collegiate programming championship, outperforming not only human competitors but also Google's Deepmind model.

QB Stock Market Week 3: Is Patrick Mahomes Slipping? Is Jalen Hurts Optimizing? https://www.foxsports.com/stories/nfl/qb-stock-market-week-3-patrick-mahomes-slipping-jalen-hurts-optimizing

An Empirical Study on Failures in Automated Issue Solving
Simiao Liu, Fang Liu, Liehao Li, Xin Tan, Yinghao Zhu, Xiaoli Lian, Li Zhang
https://arxiv.org/abs/2509.13941 https://

An Empirical Study on Failures in Automated Issue Solving
Automated issue solving seeks to autonomously identify and repair defective code snippets across an entire codebase. SWE-Bench has emerged as the most widely adopted benchmark for evaluating progress in this area. While LLM-based agentic tools show great promise, they still fail on a substantial portion of tasks. Moreover, current evaluations primarily report aggregate issue-solving rates, which obscure the underlying causes of success and failure, making it challenging to diagnose model weakne…

🇺🇦 #NowPlaying on KEXP's #MiddayShow
The Shins:
🎵 Sleeping Lessons
#TheShins
https://joshpatrick.bandcamp.com/track/the-shins-sleeping-lessons-josh-patricks-dreamy-lesson
https://open.spotify.com/track/3Bz3lj6E9VFBk1Wj1hC8vw

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure
Micha{\l} Derezi\'nski, Aaron Sidford
https://arxiv.org/abs/2507.11724 …

Approaching Optimality for Solving Dense Linear Systems with Low-Rank Structure
We provide new high-accuracy randomized algorithms for solving linear systems and regression problems that are well-conditioned except for $k$ large singular values. For solving such $d \times d$ positive definite system our algorithms succeed whp. and run in time $\tilde O(d^2 + k^ω)$. For solving such regression problems in a matrix $\mathbf{A} \in \mathbb{R}^{n \times d}$ our methods succeed whp. and run in time $\tilde O(\mathrm{nnz}(\mathbf{A}) + d^2 + k^ω)$ where $ω$ is the matrix mult…

Does using machine learning solve our problem of p-hacking and HARKing or do we have the same problems as with statistical tests and models?
https://digiresacademy.kit.com/posts/is-machine-learning-and-ai-solving-the-problem-of-p-hacking

Beyond Solving Math Quiz: Evaluating the Ability of Large Reasoning Models to Ask for Information
Youcheng Huang, Bowen Qin, Chen Huang, Duanyu Feng, Xi Yang, Wenqiang Lei
https://arxiv.org/abs/2508.11252

Beyond Solving Math Quiz: Evaluating the Ability of Large Reasoning Models to Ask for Information
Large Reasoning Models (LRMs) have demonstrated remarkable problem-solving abilities in mathematics, as evaluated by existing benchmarks exclusively on well-defined problems. However, such evaluation setup constitutes a critical gap, since a genuine intelligent agent should not only solve problems (as a math quiz solver), but also be able~to ask for information when the problems lack sufficient information, enabling proactivity in responding users' requests. To bridge such gap, we proposes a ne…

Learn to Relax with Large Language Models: Solving Nonlinear Combinatorial Optimization Problems via Bidirectional Coevolution
Beidan Liu, Zhengqiu Zhu, Chen Gao, Yong Zhao, Wei Qi, Quanjun Yin
https://arxiv.org/abs/2509.12643

Learn to Relax with Large Language Models: Solving Nonlinear Combinatorial Optimization Problems via Bidirectional Coevolution
Nonlinear Combinatorial Optimization Problems (NCOPs) present a formidable computational hurdle in practice, as their nonconvex nature gives rise to multi-modal solution spaces that defy efficient optimization. Traditional constraint relaxation approaches rely heavily on expert-driven, iterative design processes that lack systematic automation and scalable adaptability. While recent Large Language Model (LLM)-based optimization methods show promise for autonomous problem-solving, they predomina…