Tootfinder

OpenAI details why "emergent misalignment", where training on wrong answers in one area can lead to misalignment in others, happens and how it can be mitigated (Maxwell Zeff/TechCrunch)
https://techcrunch.com/2025/06/18/open

OpenAI found features in AI models that correspond to different 'personas' | TechCrunch
OpenAI researchers say they've discovered hidden features inside AI models that correspond to misaligned "personas," according to new research published

Monilajinen oikeudenmukaisuus: mahdollisuudet ja mahdolliset kompastuskivet https://www.elaimiksi.fi/2025/07/14/monilajinen-oikeudenmukaisuus-mahdollisuudet-ja-mahdolliset-kompastuskivet/

Monilajinen oikeudenmukaisuus: mahdollisuudet ja mahdolliset kompastuskivet -
Johdanto Tutkin tässä esseessä, miten monilajisen oikeudenmukaisuuden tai monilajisen oikeuden (multispecies justice) käsitettä on teoretisoitu

Dolphins WR Tyreek Hill, Olympic gold medalist Noah Lyles cancel race due to personal reasons https://www.nfl.com/news/dolphins-wr-tyreek-hill-olympic-gold-medalist-noah-lyles-cancel-race-due-to-personal-reasons

Can We Predict Alignment Before Models Finish Thinking? Towards Monitoring Misaligned Reasoning Models
Yik Siu Chan, Zheng-Xin Yong, Stephen H. Bach
https://arxiv.org/abs/2507.12428

Can We Predict Alignment Before Models Finish Thinking? Towards Monitoring Misaligned Reasoning Models
Open-weights reasoning language models generate long chains-of-thought (CoTs) before producing a final response, which improves performance but introduces additional alignment risks, with harmful content often appearing in both the CoTs and the final outputs. In this work, we investigate if we can use CoTs to predict final response misalignment. We evaluate a range of monitoring approaches, including humans, highly-capable large language models, and text classifiers, using either CoT text or ac…

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making
Yucen Wang, Rui Yu, Shenghua Wan, Le Gan, De-Chuan Zhan
https://arxiv.org/abs/2507.12496

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making
Foundation Models (FMs) and World Models (WMs) offer complementary strengths in task generalization at different levels. In this work, we propose FOUNDER, a framework that integrates the generalizable knowledge embedded in FMs with the dynamic modeling capabilities of WMs to enable open-ended task solving in embodied environments in a reward-free manner. We learn a mapping function that grounds FM representations in the WM state space, effectively inferring the agent's physical states in the wo…

Retrieving biparameter persistence modules from monoparameter ones: a characterization of hook-decomposable persistence modules
Isabella Mastroianni, Marco Guerra, Ulderico Fugacci, Emanuela De Negri
https://arxiv.org/abs/2506.14678

Retrieving biparameter persistence modules from monoparameter ones: a characterization of hook-decomposable persistence modules
Motivated by the need to relate the biparameter persistence module induced by a pair of scalar functions with the monoparameter persistence modules induced by each function separately, we introduce a construction that defines a kind of product between two monoparameter persistence modules. While originally conceived to serve this comparative purpose, our construction unexpectedly reveals a deeper structural property: it also characterizes a class of biparameter modules known as hook-decomposabl…

Querying Large Automotive Software Models: Agentic vs. Direct LLM Approaches
Lukasz Mazur, Nenad Petrovic, James Pontes Miranda, Ansgar Radermacher, Robert Rasche, Alois Knoll
https://arxiv.org/abs/2506.13171

Querying Large Automotive Software Models: Agentic vs. Direct LLM Approaches
Large language models (LLMs) offer new opportunities for interacting with complex software artifacts, such as software models, through natural language. They present especially promising benefits for large software models that are difficult to grasp in their entirety, making traditional interaction and analysis approaches challenging. This paper investigates two approaches for leveraging LLMs to answer questions over software models: direct prompting, where the whole software model is provided …

Zero-Shot Scene Understanding with Multimodal Large Language Models for Automated Vehicles
Mohammed Elhenawy, Shadi Jaradat, Taqwa I. Alhadidi, Huthaifa I. Ashqar, Ahmed Jaber, Andry Rakotonirainy, Mohammad Abu Tami
https://arxiv.org/abs/2506.12232

Zero-Shot Scene Understanding with Multimodal Large Language Models for Automated Vehicles
Scene understanding is critical for various downstream tasks in autonomous driving, including facilitating driver-agent communication and enhancing human-centered explainability of autonomous vehicle (AV) decisions. This paper evaluates the capability of four multimodal large language models (MLLMs), including relatively small models, to understand scenes in a zero-shot, in-context learning setting. Additionally, we explore whether combining these models using an ensemble approach with majority…

NetRoller: Interfacing General and Specialized Models for End-to-End Autonomous Driving
Ren Xin, Hongji Liu, Xiaodong Mei, Wenru Liu, Maosheng Ye, Zhili Chen, Jun Ma
https://arxiv.org/abs/2506.14589

NetRoller: Interfacing General and Specialized Models for End-to-End Autonomous Driving
Integrating General Models (GMs) such as Large Language Models (LLMs), with Specialized Models (SMs) in autonomous driving tasks presents a promising approach to mitigating challenges in data diversity and model capacity of existing specialized driving models. However, this integration leads to problems of asynchronous systems, which arise from the distinct characteristics inherent in GMs and SMs. To tackle this challenge, we propose NetRoller, an adapter that incorporates a set of novel mechan…

Olympic gold medalist Noah Lyles calls off 100-meter race against Dolphins' Tyreek Hill for 'personal reasons'

https://www.cbsspor…