Tootfinder

Mehrheit für Social-Media-Verbot für unter 16-Jährige
In Australien gilt jetzt das weltweit erste Social-Media-Verbot für Kinder und Jugendliche unter 16 Jahren. Auch in Deutschland befürworten das viele.
https://w…

Secure and Scalable Rerouting in LEO Satellite Networks
Lyubomir Yanev, Pietro Ronchetti, Joshua Smailes, Martin Strohmeier
https://arxiv.org/abs/2509.10173 https://

Secure and Scalable Rerouting in LEO Satellite Networks
Resilient routing in large-scale Low Earth Orbit (LEO) satellite networks remains a key challenge due to frequent and unpredictable link and node failures, potentially in response to cybersecurity breaches. While prior work has explored rerouting strategies with various levels of network awareness, their relative tradeoffs under dynamic failure conditions remain underexplored. In this work, we extend the Deep Space Network Simulator (DSNS) to systematically compare three rerouting paradigms, ea…

CardRewriter: Leveraging Knowledge Cards for Long-Tail Query Rewriting on Short-Video Platforms
Peiyuan Gong, Feiran Zhu, Yaqi Yin, Chenglei Dai, Chao Zhang, Kai Zheng, Wentian Bao, Jiaxin Mao, Yi Zhang
https://arxiv.org/abs/2510.10095

CardRewriter: Leveraging Knowledge Cards for Long-Tail Query Rewriting on Short-Video Platforms
Short-video platforms have rapidly become a new generation of information retrieval systems, where users formulate queries to access desired videos. However, user queries, especially long-tail ones, often suffer from spelling errors, incomplete phrasing, and ambiguous intent, resulting in mismatches between user expectations and retrieved results. While large language models (LLMs) have shown success in long-tail query rewriting within e-commerce, they struggle on short-video platforms, where p…

SRUM: Fine-Grained Self-Rewarding for Unified Multimodal Models
Weiyang Jin, Yuwei Niu, Jiaqi Liao, Chengqi Duan, Aoxue Li, Shenghua Gao, Xihui Liu
https://arxiv.org/abs/2510.12784

SRUM: Fine-Grained Self-Rewarding for Unified Multimodal Models
Recently, remarkable progress has been made in Unified Multimodal Models (UMMs), which integrate vision-language generation and understanding capabilities within a single framework. However, a significant gap exists where a model's strong visual understanding often fails to transfer to its visual generation. A model might correctly understand an image based on user instructions, yet be unable to generate a faithful image from text prompts. This phenomenon directly raises a compelling question: …

recent-ish reading/rereading. #books

Verbieten klingt erst einmal gut, löst aber nicht das Problem: 53 % der Deutschen befürworten ein Social-Media-Verbot für Kinder. Klingt vernünftig – bis man merkt: Die Risiken der digitalen Welt verschwinden nicht mit dem Ausloggen. Statt komplexe Probleme zu verbieten, sollten wir sie verstärkt an der Wurzel anpacken: Plattformregeln statt Pausenknopf. #Statista

Compression for Coinductive Infinitary Rewriting: A Generic Approach, with Applications to Cut-Elimination for Non-Wellfounded Proofs
R\'emy Cerda, Alexis Saurin
https://arxiv.org/abs/2510.08420

Compression for Coinductive Infinitary Rewriting: A Generic Approach, with Applications to Cut-Elimination for Non-Wellfounded Proofs
Infinitary rewriting, i.e. rewriting featuring possibly infinite terms and sequences of reduction, is a convenient framework for describing the dynamics of non-terminating but productive rewriting systems. In its original definition based on metric convergence of ordinal-indexed sequences of rewriting steps, a highly desirable property of an infinitary rewriting system is Compression, i.e. the fact that rewriting sequences of arbitrary ordinal length can always be 'compressed' to equivalent seq…

RE: https://mastodon.social/@burger_jaap/115349815858331086
Adding a negative price component to a network tariff can be an interesting and non-discriminatory way of rewarding grid-friendly (in German: 'netzdienlich', in Dutch: 'netbewust&#…

"Conventionally, the output of an AI is graded in a binary way, rewarding it when it gives a correct response and penalizing it when it gives an incorrect one.
In simple terms, in other words, guessing is rewarded — because it might be right — over an AI admitting it doesn't know the answer, which will be graded as incorrect no matter what.
As a result, through "natural statistical pressures," LLMs are far more prone to hallucinate an answer instead of "ac…

TripScore: Benchmarking and rewarding real-world travel planning with fine-grained evaluation
Yincen Qu, Huan Xiao, Feng Li, Hui Zhou, Xiangying Dai
https://arxiv.org/abs/2510.09011

TripScore: Benchmarking and rewarding real-world travel planning with fine-grained evaluation
Travel planning is a valuable yet complex task that poses significant challenges even for advanced large language models (LLMs). While recent benchmarks have advanced in evaluating LLMs' planning capabilities, they often fall short in evaluating feasibility, reliability, and engagement of travel plans. We introduce a comprehensive benchmark for travel planning that unifies fine-grained criteria into a single reward, enabling direct comparison of plan quality and seamless integration with reinfo…