Tootfinder

Mixture of Raytraced Experts
Andrea Perin, Giacomo Lagomarsini, Claudio Gallicchio, Giuseppe Nuti
https://arxiv.org/abs/2507.12419 https://

Mixture of Raytraced Experts
We introduce a Mixture of Raytraced Experts, a stacked Mixture of Experts (MoE) architecture which can dynamically select sequences of experts, producing computational graphs of variable width and depth. Existing MoE architectures generally require a fixed amount of computation for a given sample. Our approach, in contrast, yields predictions with increasing accuracy as the computation cycles through the experts' sequence. We train our model by iteratively sampling from a set of candidate exper…

May 2025 recap | The latest edition of the GÉANT School of Software Engineering, hosted by PCSS, brought together developers and experts from across Europe to explore generative #AI tools, risks, secure coding practices.
Highlights:
🔹#GenAI Buzz-Free Programming with LLMs – training by Maciej Łabędzki…

Responsible AI in Practice: Shaping a Safer Future for AI-Powered Software Development | GÉANT CONNECT Online
Poznań, 20–22 May 2025 — The latest edition of the GÉANT School of Software Engineering, held at the Poznań Supercomputing and Networking Center (PSNC), brought together top-tier developers and experts from across Europe for a deep-dive into generative AI tools, risks, and secure coding practices. At the core of the event was the acclaimed training

Trump's 'severe tariffs' on Russia probably won't work, experts say: https://benborges.xyz/2025/07/15/trumps-severe-tariffs-on-russia.html

Beyond One-Size-Fits-All: A Study of Neural and Behavioural Variability Across Different Recommendation Categories
Georgios Koutroumpas, Sebastian Idesis, Mireia Masias Bruns, Carlos Segura, Joemon M. Jose, Sergi Abadal, Ioannis Arapakis
https://arxiv.org/abs/2506.13409

Beyond One-Size-Fits-All: A Study of Neural and Behavioural Variability Across Different Recommendation Categories
Traditionally, Recommender Systems (RS) have primarily measured performance based on the accuracy and relevance of their recommendations. However, this algorithmic-centric approach overlooks how different types of recommendations impact user engagement and shape the overall quality of experience. In this paper, we shift the focus to the user and address for the first time the challenge of decoding the neural and behavioural variability across distinct recommendation categories, considering more…

Learning to Tune Like an Expert: Interpretable and Scene-Aware Navigation via MLLM Reasoning and CVAE-Based Adaptation
Yanbo Wang, Zipeng Fang, Lei Zhao, Weidong Chen
https://arxiv.org/abs/2507.11001

Learning to Tune Like an Expert: Interpretable and Scene-Aware Navigation via MLLM Reasoning and CVAE-Based Adaptation
Service robots are increasingly deployed in diverse and dynamic environments, where both physical layouts and social contexts change over time and across locations. In these unstructured settings, conventional navigation systems that rely on fixed parameters often fail to generalize across scenarios, resulting in degraded performance and reduced social acceptance. Although recent approaches have leveraged reinforcement learning to enhance traditional planners, these methods often fail in real-w…

A Proposal for High-Level Architectural Model Capable of Expressing Various Data Collaboration Platform and Data Space Concepts
Masaru Dobashi (NTT DATA Corporation), Kohei Toshimitsu (NTT DATA Corporation), Hirotsugu Seike (Koshizuka Laboratory), Miki Kanno (NTT DATA Corporation), Genki Horie (Koshizuka Laboratory), Noboru Koshizuka (Koshizuka Laboratory)
http…

A Proposal for High-Level Architectural Model Capable of Expressing Various Data Collaboration Platform and Data Space Concepts
This paper proposes "Data Space High-Level Architecture Model" (DS-HLAM) for expressing diverse data collaboration platforms across regional implementations. The framework introduces mathematically rigorous definitions with success conditions formalized through finite state automata theory, enabling interoperability while preserving digital sovereignty requirements.

Knowledge-Guided Adaptive Mixture of Experts for Precipitation Prediction
Chen Jiang, Kofi Osei, Sai Deepthi Yeddula, Dongji Feng, Wei-Shinn Ku
https://arxiv.org/abs/2509.11459 …

Knowledge-Guided Adaptive Mixture of Experts for Precipitation Prediction
Accurate precipitation forecasting is indispensable in agriculture, disaster management, and sustainable strategies. However, predicting rainfall has been challenging due to the complexity of climate systems and the heterogeneous nature of multi-source observational data, including radar, satellite imagery, and surface-level measurements. The multi-source data vary in spatial and temporal resolution, and they carry domain-specific features, making it challenging for effective integration in con…

Sounds like you can now pay to have all your data sent through Israeli intelligence if that’s what does it for you.
#expressVPN #VPN #israel

EquiContact: A Hierarchical SE(3) Vision-to-Force Equivariant Policy for Spatially Generalizable Contact-rich Tasks
Joohwan Seo, Arvind Kruthiventy, Soomi Lee, Megan Teng, Xiang Zhang, Seoyeon Choi, Jongeun Choi, Roberto Horowitz
https://arxiv.org/abs/2507.10961

EquiContact: A Hierarchical SE(3) Vision-to-Force Equivariant Policy for Spatially Generalizable Contact-rich Tasks
This paper presents a framework for learning vision-based robotic policies for contact-rich manipulation tasks that generalize spatially across task configurations. We focus on achieving robust spatial generalization of the policy for the peg-in-hole (PiH) task trained from a small number of demonstrations. We propose EquiContact, a hierarchical policy composed of a high-level vision planner (Diffusion Equivariant Descriptor Field, Diff-EDF) and a novel low-level compliant visuomotor policy (Ge…

LLM Enhancement with Domain Expert Mental Model to Reduce LLM Hallucination with Causal Prompt Engineering
Boris Kovalerchuk, Brent D. Fegley
https://arxiv.org/abs/2509.10818 ht…

LLM Enhancement with Domain Expert Mental Model to Reduce LLM Hallucination with Causal Prompt Engineering
Difficult decision-making problems abound in various disciplines and domains. The proliferation of generative techniques, especially large language models (LLMs), has excited interest in using them for decision support. However, LLMs cannot yet resolve missingness in their training data, leading to hallucinations. Retrieval-Augmented Generation (RAG) enhances LLMs by incorporating external information retrieval, reducing hallucinations and improving accuracy. Yet, RAG and related methods are on…