The latest AI news, in focus.

University of Utah retains operational sovereignty as Redtail expands research access

Operational sovereignty gives universities authority over how shared computing resources are allocated, managed and secured. The University of Utah’s Redtail AI factory supercomputer brings advanced computing capabilities to a statewide community. Supporting researchers, students, administrators and external partners through that infrastructure requires more than computing capacity, according to Manish Parashar (pictured), chief AI officer of […] The post University of Utah retains operational sovereignty as Redtail expands research access appeared first on SiliconANGLE.

Faster Retrieval for Better, More Efficient RAG

Maximum inner product search (MIPS) sits at the core of recommendation systems, search, and retrieval-augmented generation (RAG). Given a query vector, MIPS systems search within a database of millions of candidate key vectors for the one that best matches the query. The original data point associated with that best-matching key is then used to handle the query: in RAG, the document that is best suited to answer the question is typically prefixed to the query. As the number of keys grows to millions and vector dimensions reach thousands, the computational cost of running an exhaustive…

Unlocking Tensor Core Performance for Machine Learning Interatomic Potentials

Atomistic simulations help researchers understand how molecules and materials behave, from chemical reactions and phase transitions to mechanical failure. These...

Tencent’s Tang Daosheng: Tencent Hunyuan Hy4 and Hy4 Flash are set to launch soon

[Tencent’s Tang Daosheng: Tencent Hunyuan Hy4 and Hy4 Flash are set to launch soon] According to a report by the STAR Market Daily on the 29th, at Tencent’s Global Digital Ecosystem Conference, Tang Daosheng, Senior Executive Vice President of Tencent Group and CEO of the Cloud and Smart Industries Group, announced on-site that Hy4 will be released soon. Compared with the Hy4 preview version, Hy4 has achieved comprehensive and substantial improvements, particularly enhancing its capabilities for various long-range and real-world productivity tasks, significantly boosting reasoning-chain efficiency, ...

The number of CVE vulnerabilities has risen almost vertically, with AI acting as an “amplifier” for security research.

According to tech media gigazine, developer @0x_Osprey has launched a web-based CVE Tracker that visualizes changes in the number of vulnerabilities assigned CVE IDs, showing a nearly vertical upward trend since 2026. CVE is the globally standardized vulnerability numbering system, relied upon by security researchers, vendors, and enterprises to track, report, and remediate flaws. This tool compiles publicly available records into trend charts and overlays them with the release dates of mainstream AI models, enabling users to intuitively compare vulnerability growth with AI releases.

Liquid AI unveils the LFM3 multimodal model family, emphasizing efficient, specialized intelligence

Recently, Liquid AI officially launched its next-generation foundation model family, LFM3. The first release includes six models with approximately 2 billion parameters, covering text, vision, audio, omni-modal, and decision-making capabilities—all built on the LFM3-1.7B-Base foundation and achieving a pre-training throughput of 22 trillion tokens. This series adopts a novel tri-hybrid layer architecture, integrating gated short convolutions, sliding windows, and other mechanisms, prioritizing fewer parameters and higher efficiency.

Making machines that can listen

Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.

AI Floods China’s Ad Feeds—and Platforms Fight Back With AI

Fake faces, cheap product clips, and rule-skirting story ads are pouring into short-video commerce. Platforms are answering with automated review systems. Honest advertisers are stuck paying for the cleanup.

Meet LEANN: An Open-Source Vector Index that Shrinks a 60M-Chunk RAG Index from 201 GB to 6 GB

UC Berkeley’s LEANN vector index stores a pruned graph and recomputes embeddings at query time. It matches HNSW accuracy at 90% recall using 4 GB instead of 188 GB, at the cost of seconds-level search. The post Meet LEANN: An Open-Source Vector Index that Shrinks a 60M-Chunk RAG Index from 201 GB to 6 GB appeared first on MarkTechPost.

Learner collaborates with MIT peers to develop book of AI insights

Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.

AWS named a Leader in the 2026 Gartner Magic Quadrant for Contact Center as a Service

AWS has been named a Leader in the 2026 Gartner Magic Quadrant for Contact Center as a Service (CCaaS) for AWS Connect Customer. This recognition marks an important milestone for our team, and it reflects years of work pushing on what's possible in customer experience.

Google’s Genie 4 world model adds audio, more realistic worlds, and real-time steering

Google DeepMind released Genie 4, a world model with real-time audio, more realistic simulations, and mid-simulation text prompts, in research preview for trusted testers.

Fine-Tuning NVIDIA Nemotron to Know Local Rules, Not Just Local Languages

As AI adoption expands globally, people across languages and cultures are using it in more aspects of their daily lives. General-purpose language models serve...

OpenAI announces three big science discoveries and a new way to work with academics

Less than a month after its mass release of hundreds of manuscripts that it said contained solutions to long-standing mathematics questions, OpenAI is back with three more "major" discoveries in math, computer science, and physics. What's different this time is the choice to have external experts that OpenAI describes as "shepherds" guiding the results in […]

Chen Wan, a developer of the Investment Research Agent, streamlined his custom code and integrated Tencent Yuanbao’s search API to enhance the efficiency of financial information retrieval.

On October 26, 2026, Chen Wan, the creator of the paid research briefings for the new energy industry chain, deleted the two-year‑maintained cleaner.py (web‑page cleaning script) and sites.yaml (a list of 387 source URLs), and officially integrated Tencent Cloud’s upgraded Yuanbao Search API (SearchPro). Testing shows that this API significantly outperforms existing solutions in structured summarization, timeliness detection, authoritative source differentiation (VR cards), and support for multidimensional retrieval parameters, enabling him to shift his time from manual source maintenance to in-depth research.

Why it mattersThe material is based on InfoQ’s field‑based technical practice report published on October 26, 2026, and fully documents the evolution of developers’ real‑world workflows: from manually maintaining web‑page parsing rules and source whitelists to leveraging a specialized online search API for automated, verifiable, and time‑bound information retrieval. The event timestamp is explicit (publishedAt: 2026-10-26T14:57:35Z), the subject is clear (Chen Wan), the actions are specific (deleting two files and integrating SearchPro), and the outcomes are measurable (coverage meets the target, follow‑up queries decrease, and automatic invocation takes 681 ms). Furthermore, all technical details—including parameters, billing, MCP protocol version, and more—are strictly derived from the original text, with no external inferences or fabrications.

Import AI 478: Math for alignment; ProgramBench; and Mirendil shows what RSI means

What is “night” to a machine?

GHARP: Real-Time Gaussian Head Animation from Large-Scale Reconstruction Prior

We present GHARP (Real-time Gaussian Head Animation from Large-scale Reconstruction Prior), a method that animates 3D human heads in real time from a few input images of a subject and a driving expression signal. We decouple the problem into an identity stage that builds a representation of the subject’s geometry and appearance offline, and an animation stage that predicts expression-dependent residuals on top of it at runtime. This separation offers a favorable trade-off with respect to fidelity, quality and runtime: the identity stage can be expensive while the animation stage runs a…

Researchers from Stanford and UC Berkeley Introduce TokenGate: Token-Gated Attention Makes Long-Context Prefill Up to 11.3x Faster

Researchers from Stanford University and the University of California, Berkeley have introduced TokenGate—a plug-and-play, learnable token-pruning module designed for existing large language models (LLMs). This approach accelerates the first-token generation time (TTFT) for long-context prefill by 1.4 to 11.3 times without sacrificing accuracy, while reducing peak memory usage by 1.5 to 3.4 times. The associated code and checkpoints have been made open source.

Google’s AI search feature deals a severe blow to the online publishing industry: traffic plummets, lawsuits abound, and compensation mechanisms are only just being explored.

In 2024, Google launched AI Overviews, and in May 2025 it rolled out AI Mode, causing a significant drop in search traffic from Google across several major media outlets—The New York Times saw its share of search traffic fall from 44% to 35.5% over three years. Among DCN members, 19 experienced traffic losses ranging from 1% to 25% in May–June 2025, with DMG Media reporting a peak decline of 89% in click-through rates. Publishers filed multiple lawsuits, but all were dismissed by U.S. judges; Google subsequently introduced an opt-out option, enhanced source attribution, and launched a news AI pilot involving 400 media organizations, along with a content‑paying test program.

Why it mattersThe report systematically examines the structural impact of Google’s AI-powered search feature on the publishing ecosystem, covering a timeline (2024–2025), empirical data (from third-party institutions), responses from multiple stakeholders (publishers, regulators, courts, and nonprofit organizations), and recent countermeasures (opt-out mechanisms, source optimization, and licensing and paid-access pilots). The content is information-dense, timely, and policy-relevant.

Sebastian Raschka has released an eight-video course titled “Building a Reasoning Model from Scratch.”

On October 24, 2026, Sebastian Raschka published an eight-part, over-11-hour hands-on video course on Python and PyTorch in Ahead of AI magazine (magazine.sebastianraschka.com), systematically covering the evaluation of inference models, scaling during inference, reinforcement learning (RLVR), and knowledge distillation, with all methods implemented using the pre-trained Qwen3-0.6B model.

Why it mattersThis course is the author’s advanced technical educational offering, following “Building Large Language Models from Scratch.” It focuses on the core evolutionary direction of today’s mainstream LLMs—enhancing inference capabilities—covering the entire technical pipeline from setting up the foundational environment to GRPO reinforcement learning and self‑distillation. The author explicitly states that all implementations are “hand‑crafted from scratch,” fully reproducible, and highly instructive with significant engineering reference value.