Imagine being Discord…
…and seeing that a user has joined a new Discord “server”
…and immediately sends the same message to all channels they can access
…and not feeling the responsibility to automatically ban these spammers
…and instead relying on your moderators/users to do these kinds of platform-heuristic tasks… to act as human firewalls.
I have a fundamental professional disconnect with treating this as anything other than a high priority to fix this for the he…
We get so easily fooled because the typical answer is often in fact the correct one. That’s a heuristic we use in our interactions with other humans — “That’s what people say!” — and an LLM using human social forms triggers that psychological process. That’s how this magic trick works.
2/
When you don't know the gender of a person because he/she/they doesn't talk about it, what is the best/preferred approach in English?
1) Gender the person based on their appearance, based on the heuristic that most people are cisgender
I don't like this heuristic because it gives strength to stereotypes and the notion of "normality"
2) Use a neutral pronoun like the singular they
It feels more natural, but maybe some people will consider this som…
Riccati-ZORO: An efficient algorithm for heuristic online optimization of internal feedback laws in robust and stochastic model predictive control
Florian Messerer, Yunfan Gao, Jonathan Frey, Moritz Diehl
https://arxiv.org/abs/2511.10473 https://arxiv.org/pdf/2511.10473 https://arxiv.org/html/2511.10473
arXiv:2511.10473v1 Announce Type: new
Abstract: We present Riccati-ZORO, an algorithm for tube-based optimal control problems (OCP). Tube OCPs predict a tube of trajectories in order to capture predictive uncertainty. The tube induces a constraint tightening via additional backoff terms. This backoff can significantly affect the performance, and thus implicitly defines a cost of uncertainty. Optimizing the feedback law used to predict the tube can significantly reduce the backoffs, but its online computation is challenging.
Riccati-ZORO jointly optimizes the nominal trajectory and uncertainty tube based on a heuristic uncertainty cost design. The algorithm alternates between two subproblems: (i) a nominal OCP with fixed backoffs, (ii) an unconstrained tube OCP, which optimizes the feedback gains for a fixed nominal trajectory. For the tube optimization, we propose a cost function informed by the proximity of the nominal trajectory to constraints, prioritizing reduction of the corresponding backoffs. These ideas are developed in detail for ellipsoidal tubes under linear state feedback. In this case, the decomposition into the two subproblems yields a substantial reduction of the computational complexity with respect to the state dimension from $\mathcal{O}(n_x^6)$ to $\mathcal{O}(n_x^3)$, i.e., the complexity of a nominal OCP.
We investigate the algorithm in numerical experiments, and provide two open-source implementations: a prototyping version in CasADi and a high-performance implementation integrated into the acados OCP solver.
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