Ollama has partnered with NVIDIA to optimize performance on the new NVIDIA DGX Spark, powered by the GB10 Grace Blackwell Superchip, enabling fast prototyping and running of local language models.
Preliminary, alphabetically ordered list of papers accepted for inclusion in the DX’25 proceedings as full papers.
The papers cover a range of topics within the field of diagnosis and prognosis, including:
* **LLM Applications:** Exploring the potential of Large Language Models for diagnostic concepts and fault detection.
* **Fault Diagnosis & Isolation:** Techniques for identifying and locating faults in systems like HVAC, control systems, and radiotherapy equipment, utilizing methods like dynamic slicing, spectrum-based fault localization, and data-driven approaches.
* **Prognosis & Remaining Useful Life:** Predicting future system behavior and estimating remaining useful life, including approaches using particle filters and spectral fault receptive fields.
* **Hybrid Systems & Machine Learning:** Utilizing one-shot learning and deep learning clustering for system identification and predictive maintenance.
* **Competition & Benchmarks:** Details about the DX 2025 competition and its associated benchmarks.
* **Model-Based Diagnosis:** Using qualitative simulation models and fusing temporal logic with probabilistic diagnosis.
The list includes 15 accepted papers with authors and titles provided. The conference will be held from September 22-24, 2025, in Nashville, Tennessee.
The papers cover a range of topics within the field of diagnosis and prognosis, including:
* **LLM Applications:** Exploring the potential of Large Language Models for diagnostic concepts and fault detection.
* **Fault Diagnosis & Isolation:** Techniques for identifying and locating faults in systems like HVAC, control systems, and radiotherapy equipment, utilizing methods like dynamic slicing, spectrum-based fault localization, and data-driven approaches.
* **Prognosis & Remaining Useful Life:** Predicting future system behavior and estimating remaining useful life, including approaches using particle filters and spectral fault receptive fields.
* **Hybrid Systems & Machine Learning:** Utilizing one-shot learning and deep learning clustering for system identification and predictive maintenance.
* **Competition & Benchmarks:** Details about the DX 2025 competition and its associated benchmarks.
* **Model-Based Diagnosis:** Using qualitative simulation models and fusing temporal logic with probabilistic diagnosis.
The list includes 15 accepted papers with authors and titles provided. The conference will be held from September 22-24, 2025, in Nashville, Tennessee.
Google DeepMind research reveals a fundamental architectural limitation in Retrieval-Augmented Generation (RAG) systems related to fixed-size embeddings. The research demonstrates that retrieval performance degrades as database size increases, with theoretical limits based on embedding dimensionality. They introduce the LIMIT benchmark to empirically test these limitations and suggest alternatives like cross-encoders, multi-vector models, and sparse models.
An Apple study shows that large language models (LLMs) can improve performance by using a checklist-based reinforcement learning scheme, similar to a simple productivity trick of checking one's work.
A new study by MIT CSAIL researchers maps the challenges of AI in software development, identifying bottlenecks and highlighting research directions to move the field forward, aiming to allow humans to focus on high-level design while automating routine tasks.
DeepMind introduces Ithaca, a deep neural network that can restore damaged ancient Greek inscriptions, identify their original location, and help establish their creation date, collaborating with historians to advance understanding of ancient history.
Introducing Aeneas, the first AI model for contextualizing ancient inscriptions, designed to help historians better interpret, attribute, and restore fragmentary texts. It reasons across thousands of Latin inscriptions, retrieving textual and contextual parallels to aid in historical research.
A detailed comparison of the architectures of recent large language models (LLMs) including DeepSeek-V3, OLMo 2, Gemma 3, Mistral Small 3.1, Llama 4, Qwen3, SmolLM3, and Kimi 2, focusing on key design choices and their impact on performance and efficiency.
This book covers foundational topics within computer vision, with an image processing and machine learning perspective. It aims to build the reader’s intuition through visualizations and is intended for undergraduate and graduate students, as well as experienced practitioners.
AI Nexus is a platform for collaboration, knowledge exchange, and groundbreaking discourse in AI. It features upcoming AI events, speaker series, and faculty contributions to the global AI community. The site also provides information on MBZUAI programs and opportunities for collaboration.
