News about foundation models, LLMs, multimodal models, benchmarks, and model releases from OpenAI, Anthropic, Google, Meta, Mistral, and others.
719 articles
A detailed report examines an AI investment strategy backed by Wang Huiwen's family office, covering the full industry chain from foundational technology research to AI agents. Source: eu.36kr.com
Pilot projects with the governments of Uruguay and Brazil tested Model Context Protocol (MCP) as a structured way for AI systems to access current, reliable open data. Source: govinsider.asia
In October, Advanced Research Computing launched a new set of LLM services now open to all students, faculty, and staff. The expansion aims to give the wider university community easier access to AI tools for research and coursework. Source: news.vt.edu
Training large language models increasingly hits GPU memory limits before compute capacity is fully used, due to the size of model weights, gradients, and optimizer states. A new approach uses host offloading in JAX to reduce these high-bandwidth memory bottlenecks. Source: developer.nvidia.com
Researchers unveiled ARTEM (Agentic Retrieval with Temporal-Episodic Memory), a hybrid agent architecture that combines LLMs with a self-organizing neural memory system. The design aims to give AI agents better spatial and temporal episodic memory for more context-aware reasoning. Source: ojs.aaai.org
As large language models continue to spread worldwide, experts are reassessing which benchmarks actually capture meaningful performance. The piece takes stock of current LLM evaluation methods and their limitations. Source: cacm.acm.org
Researchers introduced CancerLLM, a 7-billion-parameter language model designed specifically for the cancer domain. It aims to boost LLM performance on oncology-related tasks and assist medical professionals in their work. Source: nature.com
A new overview examines intelligent agents, a longstanding goal in AI research, in light of recent progress in large language models. It covers key definitions, methods, and future directions for LLM-based agents. Source: wires.onlinelibrary.wiley.com
A curated list highlights five books covering how to build, fine-tune, and deploy large language models. They're aimed at readers who want a deeper technical grasp of LLM development. Source: kdnuggets.com
NASA has deployed Google's Gemma LLM in space, testing the concept amid ongoing debate over whether orbital data centers can realistically host the largest and most capable AI models. Source: aol.com
For three years, vision-language-action (VLA) research has assumed that top-tier robot AI models need a large language model at their core. TurboVLA challenges that assumption by achieving comparable results without one. Source: techtimes.com
Researchers found a fundamental weakness that makes it simple to manipulate LLMs into producing harmful outputs, including instructions for sabotaging an aircraft's navigation system. Source: technologyreview.com
PolyAI Ltd. announced Dialog-RSN-1, a new voice dialog AI model designed to make conversations with AI-driven phone agents sound more natural and human. Source: siliconangle.com
The new governance layer gives enterprises a single controlled path for every tool call, API request, agent interaction, and LLM prompt, as companies increasingly seek to manage the tools and APIs used by AI agents. Source: securityboulevard.com
Debian developers last week opened discussion on a general resolution addressing how AI large language models should be used within the project, with five different proposals now on the table. Source: phoronix.com
The open-sourcing of Kimi K3 suggests that AI developers' strategy of releasing highly optimized, high-performance models as open source is gaining real momentum. Source: eu.36kr.com
JetBrains released an experimental IntelliJ IDEA plugin that lets Kotlin/JVM projects hand off runtime logic to an LLM directly from Kotlin code. Source: infoworld.com
Backed by the GSMA, the African Trust & Safety LLM Challenge has released a benchmark of 4,216 verified, reproducible tests designed to probe AI safety across diverse African languages and cultural contexts. The project aims to fill gaps in safety evaluation for regions underrepresented in existing LLM benchmarks. Source: thefastmode.com
Researchers introduced a Dual-Population Co-Evolutionary (DPEC) framework that lets an LLM-driven population of algorithms learn from expert-designed strategies to solve complex scheduling problems. The approach was applied to agile earth observation satellite scheduling, combining adaptive large neighborhood search with LLM assistance. Source: eurekalert.org
A study published in JCO Clinical Cancer Informatics shows that the Truveta Language Model can accurately pull critical cancer staging details from vast collections of unstructured clinical records. The research highlights a scalable way to turn raw medical notes into research-ready data. Source: globenewswire.com