Tootfinder

Unramified Brauer groups of homogeneous spaces with finite stabilisers and the Grunwald Problem
Lucas Lagarde
https://arxiv.org/abs/2506.02600 https://

Unramified Brauer groups of homogeneous spaces with finite stabilisers and the Grunwald Problem
We provide an algorithm for calculating the unramified Brauer group of a homogeneous space $X$ of a semi-simple simply connected group $H$ with finite geometric stabiliser over any field of characteristic 0. When $k$ is a number field, we use the obtained description of the unramified Brauer group in order to study the Brauer-Manin obstruction to weak approximation on $X$. In particular, we provide an algorithm to compute the Brauer-Manin obstruction on $X$, which guarantees effectivity of the …

On the (in)equivalence of Brouwer's fixed point theorem and Sperner's lemma
Junichi Minagawa
https://arxiv.org/abs/2507.02366 https://

On the (in)equivalence of Brouwer's fixed point theorem and Sperner's lemma
We consider Brouwer's fixed point theorem and Sperner's lemma in one dimension. We present a proof of the Brouwer theorem using the Sperner lemma, and vice versa. However, we also show that they are not equivalent, because the Sperner lemma holds in the ordered field of rational numbers, whereas proving the Brouwer theorem requires the property of completeness.

Breaking the HBM Bit Cost Barrier: Domain-Specific ECC for AI Inference Infrastructure
Rui Xie, Asad Ul Haq, Yunhua Fang, Linsen Ma, Sanchari Sen, Swagath Venkataramani, Liu Liu, Tong Zhang
https://arxiv.org/abs/2507.02654

Breaking the HBM Bit Cost Barrier: Domain-Specific ECC for AI Inference Infrastructure
High-Bandwidth Memory (HBM) delivers exceptional bandwidth and energy efficiency for AI workloads, but its high cost per bit, driven in part by stringent on-die reliability requirements, poses a growing barrier to scalable deployment. This work explores a system-level approach to cost reduction by eliminating on-die ECC and shifting all fault management to the memory controller. We introduce a domain-specific ECC framework combining large-codeword Reed--Solomon~(RS) correction with lightweight …

Numerical analysis of the convex relaxation of the barrier parameter functional of self-concordant barriers
Vitali Pirau, Roland Hildebrand
https://arxiv.org/abs/2507.01812

Numerical analysis of the convex relaxation of the barrier parameter functional of self-concordant barriers
Self-concordant barriers are essential for interior-point algorithms in conic programming. To speed up the convergence it is of interest to find a barrier with the lowest possible parameter for a given cone. The barrier parameter is a non-convex function on the set of self-concordant barriers on a given cone, and finding an optimal barrier amounts to solving a non-convex infinite-dimensional optimization problem. In this work we study the degradation of the optimal value of the problem when the…

Zwischen Tradition und Technik: Besuch in einer Brauerei
KI trifft auf Hefe: So braut die Brauerei Riegele Bier – mit jahrhundertealtem Wissen und moderner Technik.
https://www.

I’m not a Coffee Guy™ (I don’t even drink the stuff), but this video about the V60 drip coffee brewer is fascinating.
https://www.youtube.com/watch?v=BfNoNTjcRbE

This is how we celebrate the 4th of July here in Aotearoa... With an Ōtautahi-brewed America Pale Ale, and gratitude that we took the precaution of drifting to the other end of the earth over the last few hundred million years.

MGC: A Compiler Framework Exploiting Compositional Blindness in Aligned LLMs for Malware Generation
Lu Yan, Zhuo Zhang, Xiangzhe Xu, Shengwei An, Guangyu Shen, Zhou Xuan, Xuan Chen, Xiangyu Zhang
https://arxiv.org/abs/2507.02057

MGC: A Compiler Framework Exploiting Compositional Blindness in Aligned LLMs for Malware Generation
Large language models (LLMs) have democratized software development, reducing the expertise barrier for programming complex applications. This accessibility extends to malicious software development, raising significant security concerns. While LLM providers have implemented alignment mechanisms to prevent direct generation of overtly malicious code, these safeguards predominantly evaluate individual prompts in isolation, overlooking a critical vulnerability: malicious operations can be systema…

MISC: Minimal Intervention Shared Control with Guaranteed Safety under Non-Convex Constraints
Shivam Chaubey, Francesco Verdoja, Shankar Deka, Ville Kyrki
https://arxiv.org/abs/2507.02438

MISC: Minimal Intervention Shared Control with Guaranteed Safety under Non-Convex Constraints
Shared control combines human intention with autonomous decision-making, from low-level safety overrides to high-level task guidance, enabling systems that adapt to users while ensuring safety and performance. This enhances task effectiveness and user experience across domains such as assistive robotics, teleoperation, and autonomous driving. However, existing shared control methods, based on e.g. Model Predictive Control, Control Barrier Functions, or learning-based control, struggle with feas…

PandasBench: A Benchmark for the Pandas API
Alex Broihier, Stefanos Baziotis, Daniel Kang, Charith Mendis
https://arxiv.org/abs/2506.02345 https://

PandasBench: A Benchmark for the Pandas API
The Pandas API has been central to the success of pandas and its alternatives. Despite its importance, there is no benchmark for it, and we argue that we cannot repurpose existing benchmarks (from other domains) for the Pandas API. In this paper, we introduce requirements that are necessary for a Pandas API enchmark, and present the first benchmark that fulfills them: PandasBench. We argue that it should evaluate the real-world coverage of a technique. Yet, real-world coverage is not sufficie…