Tootfinder

@… maybe a server response time metric that isn’t dependent on the client so we *could* draw at least some conclusions about hosting performance or backend performance

Great second half for England. You’d be surprised to learn that they were still inferior in most stats, but by a much closer margin. What matters is that they were able to get more possession and they’ve two scintillating subs in Kelly and Agyemang.
But Spain still look deadly & have more space now to run into. The question is whether England will push for a winner or bunker for penalties. I think they have to go for it, at least try to keep Spain temporarily away.

The Surprising Effectiveness of Linear Models for Whole-Body Model-Predictive Control
Arun L. Bishop, Juan Alvarez-Padilla, Sam Schoedel, Ibrahima Sory Sow, Juee Chandrachud, Sheitej Sharma, Will Kraus, Beomyeong Park, Robert J. Griffin, John M. Dolan, Zachary Manchester
https://arxiv.org/abs/2509.17884

The Surprising Effectiveness of Linear Models for Whole-Body Model-Predictive Control
When do locomotion controllers require reasoning about nonlinearities? In this work, we show that a whole-body model-predictive controller using a simple linear time-invariant approximation of the whole-body dynamics is able to execute basic locomotion tasks on complex legged robots. The formulation requires no online nonlinear dynamics evaluations or matrix inversions. We demonstrate walking, disturbance rejection, and even navigation to a goal position without a separate footstep planner on a…

Ask Me Again Differently: GRAS for Measuring Bias in Vision Language Models on Gender, Race, Age, and Skin Tone
Shaivi Malik, Hasnat Md Abdullah, Sriparna Saha, Amit Sheth
https://arxiv.org/abs/2508.18989

Ask Me Again Differently: GRAS for Measuring Bias in Vision Language Models on Gender, Race, Age, and Skin Tone
As Vision Language Models (VLMs) become integral to real-world applications, understanding their demographic biases is critical. We introduce GRAS, a benchmark for uncovering demographic biases in VLMs across gender, race, age, and skin tone, offering the most diverse coverage to date. We further propose the GRAS Bias Score, an interpretable metric for quantifying bias. We benchmark five state-of-the-art VLMs and reveal concerning bias levels, with the least biased model attaining a GRAS Bias S…