Tootfinder

Got a Surface Laptop Studio 2? Or just need a 100W USB-Power Delivery charger?
My new favorite charger is the "Anker Prime Charger, 160W 3-Port (140W on a single port). Perfect for a power-hungry laptop smartphone.
https://www.amazon.com/Anker-Charger-Compa

2025 has only intensified the struggles of past years. The challenge for us is not "how do we pull through?" but "how do we make a 'new normal' work for us?"
The answer, of course, is not to simply try harder. To prevent 2026 from going like 2025, we need to deliberately re-evaluate our own goals, and our relationships with our jobs.
Learn more in the final issue of the Product Picnic for the year — and if you like it, please subscribe!

Bouncing back from burnout
The causes of burnout are systemic. You can't get out of it just by trying harder.

🇺🇦 #NowPlaying on BBCRadio3's #ClassicalLive
Benjamin Britten, Ruby Hughes & Errollyn Wallen:
🎵 Folksong Arrangements - Volume 4 ; How Sweet The Answer (The Wren)
#BenjaminBritten #RubyHughes #ErrollynWallen

ReTraceQA: Evaluating Reasoning Traces of Small Language Models in Commonsense Question Answering
Francesco Maria Molfese, Luca Moroni, Ciro Porcaro, Simone Conia, Roberto Navigli
https://arxiv.org/abs/2510.09351

ReTraceQA: Evaluating Reasoning Traces of Small Language Models in Commonsense Question Answering
While Small Language Models (SLMs) have demonstrated promising performance on an increasingly wide array of commonsense reasoning benchmarks, current evaluation practices rely almost exclusively on the accuracy of their final answers, neglecting the validity of the reasoning processes that lead to those answers. To address this issue, we introduce ReTraceQA, a novel benchmark that introduces process-level evaluation for commonsense reasoning tasks. Our expert-annotated dataset reveals that in a…

Browns rookie Shedeur Sanders pantomimes answers to Cleveland media following Joe Flacco's benching https://www.nfl.com/news/browns-rookie-shedeur-sanders-pantomimes-answers-to-cleveland-media-following-joe-flacco-s-b…

VQArt-Bench: A semantically rich VQA Benchmark for Art and Cultural Heritage
A. Alfarano (University of Zurich, Max Planck Society), L. Venturoli (University of Zurich, Max Planck Society), D. Negueruela del Castillo (University of Zurich, Max Planck Society)
https://arxiv.org/abs/2510.12750

VQArt-Bench: A semantically rich VQA Benchmark for Art and Cultural Heritage
Multimodal Large Language Models (MLLMs) have demonstrated significant capabilities in joint visual and linguistic tasks. However, existing Visual Question Answering (VQA) benchmarks often fail to evaluate deep semantic understanding, particularly in complex domains like visual art analysis. Confined to simple syntactic structures and surface-level attributes, these questions fail to capture the diversity and depth of human visual inquiry. This limitation incentivizes models to exploit statisti…

📢 Netanjahu ordnet neue Angriffe im Gazastreifen an
Der israelische Ministerpräsident Benjamin Netanjahu hat die Armee angewiesen, sofort "intensive Angriffe" im Gazastreifen auszuführen. Dies teilte das Büro Netanjahus mit. Seit rund zwei Wochen gilt im Gazastreifen eine Waffenruhe.
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Netanjahu ordnet neue Angriffe im Gazastreifen an
Der israelische Ministerpräsident Benjamin Netanjahu hat die Armee angewiesen, sofort "intensive Angriffe" im Gazastreifen auszuführen. Dies teilte das Büro Netanjahus mit. Seit rund zwei Wochen gilt im Gazastreifen eine Waffenruhe.

The illusion of readiness: Stress testing large frontier models on multimodal medical benchmarks #AI

The Illusion of Readiness: Stress Testing Large Frontier Models on Multimodal Medical Benchmarks
Large frontier models like GPT-5 now achieve top scores on medical benchmarks. But our stress tests tell a different story. Leading systems often guess correctly even when key inputs like images are removed, flip answers under trivial prompt changes, and fabricate convincing yet flawed reasoning. These aren't glitches; they expose how today's benchmarks reward test-taking tricks over medical understanding. We evaluate six flagship models across six widely used benchmarks and find that high lead…

MatheMagic: Generating Dynamic Mathematics Benchmarks Robust to Memorization
Dayy\'an O'Brien, Barry Haddow, Emily Allaway, Pinzhen Chen
https://arxiv.org/abs/2510.05962

MatheMagic: Generating Dynamic Mathematics Benchmarks Robust to Memorization
Conducting contamination-free evaluation of mathematical capabilities can be difficult for two reasons: models may memorize a test set once it is made public, and current mathematical benchmarks are prone to overfitting due to having limited diversity of symbols and rules, coupled with closed-ended answers. This paper proposes a method to leverage these shortcomings as useful features to a construct dynamic, counterfactual benchmark, which can be used to both reveal overfitting and measure true…

fev-bench: A Realistic Benchmark for Time Series Forecasting
Oleksandr Shchur, Abdul Fatir Ansari, Caner Turkmen, Lorenzo Stella, Nick Erickson, Pablo Guerron, Michael Bohlke-Schneider, Yuyang Wang
https://arxiv.org/abs/2509.26468

fev-bench: A Realistic Benchmark for Time Series Forecasting
Benchmark quality is critical for meaningful evaluation and sustained progress in time series forecasting, particularly given the recent rise of pretrained models. Existing benchmarks often have narrow domain coverage or overlook important real-world settings, such as tasks with covariates. Additionally, their aggregation procedures often lack statistical rigor, making it unclear whether observed performance differences reflect true improvements or random variation. Many benchmarks also fail to…