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Ces gros contribuables qui négocient leurs redressements fiscaux avec l’Etat
https://www.lemonde.fr/politique/article/2025/06/18/quand-les-gros-contribuables-nego…

Quand les gros contribuables négocient leurs redressements fiscaux
En 2024, l’Etat a conclu des accords amiables avec 92 particuliers et 223 entreprises en délicatesse avec le fisc. Les montants demandés ont été réduits de 71,5 %. Bercy a ainsi renoncé à 1,9 milliard d’euros, un record. Une pratique contestée par un rapport parlementaire.

Time to write a dice roller...gonna use `lark` to parse the string so I can just type out the roll and add tags for the damage types and let it do the math. Is it overkill? Yes. Does it work? No, not yet...but that's the fun part.
#python #lark

KI-gestützte Cyberangriffe: Experten beobachten zunehmenden LLM-Einsatz
Erstmals wurde russische Spionagesoftware entdeckt, die mit Large Language Models entwickelt wurde. Experten sehen einen Wendepunkt im Cyberwettrüsten.

Inclusion Arena: An Open Platform for Evaluating Large Foundation Models with Real-World Apps
Kangyu Wang, Hongliang He, Lin Liu, Ruiqi Liang, Zhenzhong Lan, Jianguo Li
https://arxiv.org/abs/2508.11452

Inclusion Arena: An Open Platform for Evaluating Large Foundation Models with Real-World Apps
Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) have ushered in a new era of AI capabilities, demonstrating near-human-level performance across diverse scenarios. While numerous benchmarks (e.g., MMLU) and leaderboards (e.g., Chatbot Arena) have been proposed to help evolve the development of LLMs and MLLMs, most rely on static datasets or crowdsourced general-domain prompts, often falling short of reflecting performance in real-world applications. To bridge this criti…

Evaluating the Ability of Large Language Models to Reason about Cardinal Directions, Revisited
Anthony G Cohn, Robert E Blackwell
https://arxiv.org/abs/2507.12059

Evaluating the Ability of Large Language Models to Reason about Cardinal Directions, Revisited
We investigate the abilities of 28 Large language Models (LLMs) to reason about cardinal directions (CDs) using a benchmark generated from a set of templates, extensively testing an LLM's ability to determine the correct CD given a particular scenario. The templates allow for a number of degrees of variation such as means of locomotion of the agent involved, and whether set in the first, second or third person. Even the newer Large Reasoning Models are unable to reliably determine the correct C…

KI-Systeme bevorzugen eigene Texte: Studie warnt vor "Anti-Human-Bias"
Wissenschaftler haben nachgewiesen, dass Large Language Models Inhalte anderer KI-Systeme bevorzugen. Was Menschen daraus lernen können.

Inspire or Predict? Exploring New Paradigms in Assisting Classical Planners with Large Language Models
Wenkai Yu, Jianhang Tang, Yang Zhang, Shanjiang Tang, Kebing Jin, Hankz Hankui Zhuo
https://arxiv.org/abs/2508.11524

Inspire or Predict? Exploring New Paradigms in Assisting Classical Planners with Large Language Models
Addressing large-scale planning problems has become one of the central challenges in the planning community, deriving from the state-space explosion caused by growing objects and actions. Recently, researchers have explored the effectiveness of leveraging Large Language Models (LLMs) to generate helpful actions and states to prune the search space. However, prior works have largely overlooked integrating LLMs with domain-specific knowledge to ensure valid plans. In this paper, we propose a nove…

From Black Boxes to Transparent Minds: Evaluating and Enhancing the Theory of Mind in Multimodal Large Language Models
Xinyang Li, Siqi Liu, Bochao Zou, Jiansheng Chen, Huimin Ma
https://arxiv.org/abs/2506.14224

From Black Boxes to Transparent Minds: Evaluating and Enhancing the Theory of Mind in Multimodal Large Language Models
As large language models evolve, there is growing anticipation that they will emulate human-like Theory of Mind (ToM) to assist with routine tasks. However, existing methods for evaluating machine ToM focus primarily on unimodal models and largely treat these models as black boxes, lacking an interpretative exploration of their internal mechanisms. In response, this study adopts an approach based on internal mechanisms to provide an interpretability-driven assessment of ToM in multimodal large …