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Musks Gehirnchip-Firma holt sich 650 Millionen Dollar mehr
Über Implantate von Neuralink können Menschen mit Querschnittslähmung verschiedene Geräte bedienen. Mit einer neuen Geldspritze hat Musk noch größere Pläne.

Multi-Task Reward Learning from Human Ratings
Mingkang Wu, Devin White, Evelyn Rose, Vernon Lawhern, Nicholas R Waytowich, Yongcan Cao
https://arxiv.org/abs/2506.09183

Multi-Task Reward Learning from Human Ratings
Reinforcement learning from human feeback (RLHF) has become a key factor in aligning model behavior with users' goals. However, while humans integrate multiple strategies when making decisions, current RLHF approaches often simplify this process by modeling human reasoning through isolated tasks such as classification or regression. In this paper, we propose a novel reinforcement learning (RL) method that mimics human decision-making by jointly considering multiple tasks. Specifically, we lever…

Les colibris mâles peuvent être des gros lourds:
"Les colibris femelles imitent les mâles pour éviter le harcèlement."
https://www.futurity.org/female-hummingbirds-colors-harassment-3275032/

Towards Next-Generation Intelligent Maintenance: Collaborative Fusion of Large and Small Models
Xiaoyi Yuan, Qiming Huang, Mingqing Guo, Huiming Ma, Ming Xu, Zeyi Liu, Xiao He
https://arxiv.org/abs/2506.05854

Towards Next-Generation Intelligent Maintenance: Collaborative Fusion of Large and Small Models
With the rapid advancement of intelligent technologies, collaborative frameworks integrating large and small models have emerged as a promising approach for enhancing industrial maintenance. However, several challenges persist, including limited domain adaptability, insufficient real-time performance and reliability, high integration complexity, and difficulties in knowledge representation and fusion. To address these issues, an intelligent maintenance framework for industrial scenarios is prop…

Towards Human-like Preference Profiling in Sequential Recommendation
Zhongyu Ouyang, Qianlong Wen, Chunhui Zhang, Yanfang Ye, Soroush Vosoughi
https://arxiv.org/abs/2506.02261

Towards Human-like Preference Profiling in Sequential Recommendation
Sequential recommendation systems aspire to profile users by interpreting their interaction histories, echoing how humans make decisions by weighing experience, relative preference strength, and situational relevance. Yet, existing large language model (LLM)-based recommenders often fall short of mimicking the flexible, context-aware decision strategies humans exhibit, neglecting the structured, dynamic, and context-aware mechanisms fundamental to human behaviors. To bridge this gap, we propose…

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