Tootfinder

#today attended TTK Energy Group meeting with some interesting run-on discussion about a community energy project. Now I am on my #research model stuff for the rest of the day...

MAEBE: Multi-Agent Emergent Behavior Framework
Sinem Erisken (Independent Researcher), Timothy Gothard (Independent Researcher), Martin Leitgab (Independent Researcher), Ram Potham (Independent Researcher)
https://arxiv.org/abs/2506.03053

MAEBE: Multi-Agent Emergent Behavior Framework
Traditional AI safety evaluations on isolated LLMs are insufficient as multi-agent AI ensembles become prevalent, introducing novel emergent risks. This paper introduces the Multi-Agent Emergent Behavior Evaluation (MAEBE) framework to systematically assess such risks. Using MAEBE with the Greatest Good Benchmark (and a novel double-inversion question technique), we demonstrate that: (1) LLM moral preferences, particularly for Instrumental Harm, are surprisingly brittle and shift significantly …

Towards Secure MLOps: Surveying Attacks, Mitigation Strategies, and Research Challenges
Raj Patel, Himanshu Tripathi, Jasper Stone, Noorbakhsh Amiri Golilarz, Sudip Mittal, Shahram Rahimi, Vini Chaudhary
https://arxiv.org/abs/2506.02032

Towards Secure MLOps: Surveying Attacks, Mitigation Strategies, and Research Challenges
The rapid adoption of machine learning (ML) technologies has driven organizations across diverse sectors to seek efficient and reliable methods to accelerate model development-to-deployment. Machine Learning Operations (MLOps) has emerged as an integrative approach addressing these requirements by unifying relevant roles and streamlining ML workflows. As the MLOps market continues to grow, securing these pipelines has become increasingly critical. However, the unified nature of MLOps ecosystem …

Bra och underhållande text kring begreppen statistisk signifikans och konfidensintervall.
How a Cup of Tea Laid the Foundations for Modern Statistical Analysis https://www.wired.com/story/how-a-cup-of-t…

How a Cup of Tea Laid the Foundations for Modern Statistical Analysis
Scientific experiments run today are based on research practices that evolved out of a British tea-tasting experiment in the 1920s.

Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices
Lu\'is Cruz, Jo\~ao Paulo Fernandes, Maja H. Kirkeby, Silverio Mart\'inez-Fern\'andez, June Sallou, Hina Anwar, Enrique Barba Roque, Justus Bogner, Joel Casta\~no, Fernando Castor, Aadil Chasmawala, Sim\~ao Cunha, Daniel Feitosa, Alexandra Gonz\'alez, Andreas Jedlitschka, Patricia Lago, Ana Oprescu, Pooja Rani, Jo\~ao Saraiva, Federica Sarro, Raghavendra…

Greening AI-enabled Systems with Software Engineering: A Research Agenda for Environmentally Sustainable AI Practices
The environmental impact of Artificial Intelligence (AI)-enabled systems is increasing rapidly, and software engineering plays a critical role in developing sustainable solutions. The "Greening AI with Software Engineering" CECAM-Lorentz workshop (no. 1358, 2025) funded by the Centre Européen de Calcul Atomique et Moléculaire and the Lorentz Center, provided an interdisciplinary forum for 29 participants, from practitioners to academics, to share knowledge, ideas, practices, and current resul…

Federated Deep Reinforcement Learning-Driven O-RAN for Automatic Multirobot Reconfiguration
Faisal Ahmed, Myungjin Lee, Shao-Yu Lien, Suresh Subramaniam, Motoharu Matsuura, Hiroshi Hasegawa, Shih-Chun Lin
https://arxiv.org/abs/2506.00822

Federated Deep Reinforcement Learning-Driven O-RAN for Automatic Multirobot Reconfiguration
The rapid evolution of Industry 4.0 has led to the emergence of smart factories, where multirobot system autonomously operates to enhance productivity, reduce operational costs, and improve system adaptability. However, maintaining reliable and efficient network operations in these dynamic and complex environments requires advanced automation mechanisms. This study presents a zero-touch network platform that integrates a hierarchical Open Radio Access Network (O-RAN) architecture, enabling the …

DeepShop: A Benchmark for Deep Research Shopping Agents
Yougang Lyu, Xiaoyu Zhang, Lingyong Yan, Maarten de Rijke, Zhaochun Ren, Xiuying Chen
https://arxiv.org/abs/2506.02839

DeepShop: A Benchmark for Deep Research Shopping Agents
Web agents for online shopping have shown great promise in automating user interactions across e-commerce platforms. Benchmarks for assessing such agents do not reflect the complexity of real-world shopping scenarios, as they often consist of overly simple queries with deterministic paths, such as "Find iPhone 15." Real shopping scenarios are inherently more layered, involving multi-dimensional product attributes, search filters, and user-specific sorting preferences. To address this gap, we in…

MERIT: Multilingual Semantic Retrieval with Interleaved Multi-Condition Query
Wei Chow, Yuan Gao, Linfeng Li, Xian Wang, Qi Xu, Hang Song, Lingdong Kong, Ran Zhou, Yi Zeng, Yidong Cai, Botian Jiang, Shilin Xu, Jiajun Zhang, Minghui Qiu, Xiangtai Li, Tianshu Yang, Siliang Tang, Juncheng Li
https://arxiv.org/abs/2506.03144

MERIT: Multilingual Semantic Retrieval with Interleaved Multi-Condition Query
Semantic retrieval is crucial for modern applications yet remains underexplored in current research. Existing datasets are limited to single languages, single images, or singular retrieval conditions, often failing to fully exploit the expressive capacity of visual information as evidenced by maintained performance when images are replaced with captions. However, practical retrieval scenarios frequently involve interleaved multi-condition queries with multiple images. Hence, this paper introduc…

At the Semantic Digital Humanities 2025 Workshop, Jose Maldonado-Rodríguez is presenting "Natural Language Querying for Humanities #KnowledgeGraphs A case study on the GOLEM KG". Main contribution is a bilingual dataset (English-Spanish) specifically designed to evaluate automatic text-to-SPARQL translation systems for GOLEM, a specialized humanities KG.
paper:

Cyrus : A DRL-based Puncturing Solution to URLLC/eMBB Multiplexing in O-RAN
Ehsan Ghoreishi, Bahman Abolhassani, Yan Huang, Shiva Acharya, Wenjing Lou, Y. Thomas Hou
https://arxiv.org/abs/2506.00167

Cyrus+: A DRL-based Puncturing Solution to URLLC/eMBB Multiplexing in O-RAN
Puncturing is a promising technique in 3GPP to multiplex Enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communications (URLLC) traffic on the same 5G New Radio (NR) air interface. The essence of puncturing is to transmit URLLC packets on demand upon their arrival, by preempting the radio resources (or subcarriers) that are already allocated to eMBB traffic. Although it is considered most bandwidth efficient, puncturing URLLC data on eMBB can lead to degradation of eMBB's perfor…