AI News Today

arXiv:2610.30504v1 Announce Type: new Abstract: Large language models (LLMs) are widely used to answer factual questions, but their answers can contain errors and must be fact-checked claim by claim. Yet the resulting score depends on three design decisions: which questions the claims answer, under what conditions each claim is judged, and which sources count as evidence. No existing benchmark specifies all three. We introduce JSFactCheckBench, a Japanese benchmark that specifies all three, with

arXiv:2610.30512v1 Announce Type: new Abstract: LLM judges are used to determine which of two answers is better. Trusting these verdicts requires knowing how repeatable they are and how much they depend on the judge's instructions. We show that similar aggregate scores can conceal substantial changes in individual verdicts, both when the same setup is repeated and when the instructions change. Using 500 Arena-Hard v2.0 prompts with two models' answers held fixed, we run GPT-4.1 as the judge unde

arXiv:2610.30518v1 Announce Type: new Abstract: We present Protegrity-PII, a new PII decision model for structured and unstructured text. Protegrity-PII achieves a typed recall of 0.915 with a precision of 0.907 on a new benchmark we propose, PII-SCOPE. Our model processes text in 0.49 seconds per million characters at \$0.0005 per million characters on a single RTX 6000 using batched inference. Due in part to the expansive nature of the entity type taxonomy used in training, Protegrity-PII achi

arXiv:2610.30533v1 Announce Type: new Abstract: Industry Foundation Classes (IFC), the standard for building information exchange in architecture, engineering, and construction (AEC), is increasingly queried through large language models (LLMs) using natural language. Evaluating these systems at scale is difficult because free-text answers require costly human or LLM-based review. We propose IFC-MCQ, a method that automatically generates multiple-choice benchmarks from IFC files for exact option

arXiv:2610.30635v1 Announce Type: new Abstract: Coding-agent benchmarks often describe a task in natural language but grade it with tests taken from the upstream project. Passing those tests can require information the description never states: a constant's name, a module path, an exact message. We call this a specification-oracle gap: where it exists, a benchmark also measures whether an agent reproduces upstream implementation details. We formalise the gap, including the inconsistencies that c

arXiv:2610.30664v1 Announce Type: new Abstract: Large language models increasingly act as controllers in structured decision pipelines, yet their decisions are still executed by autoregressive text decoding. Hierarchical RAG is one such setting: at each frontier of a document tree, an LLM commits to $b$ of $k$ sections under an input-dependent action grammar, executed as either failure-prone free-form JSON or reliable but strictly serial grammar-constrained generation. We show that this serializ

arXiv:2610.30676v1 Announce Type: new Abstract: Singular-value spectra encode important structure in transformer feed-forward projections, yet their causal role in downstream behavior remains underexplored. We study post-hoc spectral smoothing and spiking interventions on the up_proj and down_proj matrices. Positive perturbation stretches deviations from the geometric mean, making large singular values larger and small ones smaller (spiking), and vice versa. The intervention preserves singular v

arXiv:2610.30690v1 Announce Type: new Abstract: Large language models (LLMs) deployed in mental-health contexts can be evaluated for safety failures, but the resulting judgments are often difficult to inspect or explain. Common approaches to improving safety rely on prompt engineering, LLM-as-a-Judge, and Constitutional AI. In this work, we describe a clinician-led alternative in which two clinicians define, finalize, and adjudicate the four safety checks of a rule base, which is formally verifi

arXiv:2610.30705v1 Announce Type: new Abstract: Text embedding and text generation are two of the most useful capabilities of modern large language models, yet they are typically provided by separate specialized models (an embedder and a generator) trained with incompatible objectives and deployed side by side in retrieval-augmented generation and agent systems. Recent unified embedding-generation models such as GritLM share parameters between the two capabilities, but they either switch attenti

arXiv:2610.30715v1 Announce Type: new Abstract: This paper studies the local geometry of semantic meaning in large language models. Current representation analyses ask whether meanings are close in embedding space, or whether linear probes separate semantic classes, but they do not characterize the within-concept local geometry: what is invariant across realizations of a single semantic factor, what varies, and what changes when meaning is refined. This paper characterizes that geometry through

arXiv:2610.30742v1 Announce Type: new Abstract: Guardrail models are increasingly used as safety layers for LLMs and agentic systems, but most are trained on fixed prompt or response interfaces and become unreliable when the same safety case appears as a conversation, agent trajectory, multi-agent trace, or policy-conditioned input. We propose UniGuard, a training framework that learns interface-invariant safety representations from multiple semantically aligned views of the same safety case. We

NVIDIA released Nemotron 3.5 Bolt E4B 4GB, an open small language model post-trained from Gemma 4 E4B for local voice assistants, game characters and robots, targeting about 4GB of VRAM on RTX and Jetson. The post NVIDIA Releases Nemotron 3.5 Bolt E4B 4GB: An Open Small Language Model for Local Voice Agents, Game Characters and Robots appeared first on MarkTechPost .

Kyutai released CADRE, a family of 7 open causal diffusion video autoencoders for interactive world models. World models built on CADRE run at 20 fps on 1 H100 and react in under 230 ms. The post Kyutai CADRE: Video Autoencoders That Cut World Model Lag Below 230 ms appeared first on MarkTechPost .

Moonshot AI released Kimi K3.1 Pro, a 2.8T-parameter, 104B-active MoE model with 1M-token context. It scores 65.7% on Terminal-Bench 4.0, costs $2/$10 per million tokens, and open weights arrive by Nov 4, 2026. The post Moonshot AI Releases Kimi K3.1 Pro: A 2.8T Total/104B Active Params (MoE) Model with 1M Context appeared first on MarkTechPost .

Generative AI

TinyFish published two engineering research articles on agentic search. One defines a 7-phase production architecture with evidence contracts and bounded retries. The other explains why its deep research system dropped a separate planner and per-query subagents. The post TinyFish Breaks Down Agentic Search Architecture for Production AI Agents appeared first on MarkTechPost .

Google Cloud announced Agent Compute Engine, a compute service that gives every AI agent its own hardware-isolated microVM with no credentials inside. It already runs Gemini agents and enters preview in the coming months. The post Google Cloud Launches Agent Compute Engine: A MicroVM per AI Agent appeared first on MarkTechPost .

You.com launched Inference Router, an OpenAI-compatible API for 12 open-weight models with a 99.9% uptime SLA, zero data retention, 100% US hosting, server-side web search and no platform fee. The post You.com Launches Inference Router: 12 Open-Weight Models With 99.9% Uptime SLA appeared first on MarkTechPost .

Kazakhstan’s AI ecosystem is moving from infrastructure plans to deployment. The shift is visible across the full AI stack — from infrastructure to models to applications. The announcements below — coinciding with a visit to Astana by NVIDIA founder and CEO Jensen Huang — point to the nation’s increasing supply of accelerated computing, along with […]

<!-- SC_OFF --><div class="md"><p>Extracted a direction from “evil” versus “kind” persona prompts in Qwen2.5-1.5B-Instruct’s activations and steered the model towards evil</p> </div><!-- SC_ON --> submitted by <a href="https://www.reddit.com/user/Thatmf482"> /u/Thatmf482 </a> <br/> <span><a href="https://www.reddit.com/gallery/1xcvil5">[link]</a></span> <span><a href="https://www.reddit.com/r/MachineLearning/comments/1xcvil5/steered_qwen_to_be_more_evil_and_it_started/">[comments]</a></span>

The next competitive advantage in enterprise AI has less to do with which model you choose than with what you build around it. That’s an important shift. For the past several years, much of the AI conversation has centered on model capability: bigger models, better reasoning, larger context windows, higher benchmark scores. Those advances have…

<!-- SC_OFF --><div class="md"><p><strong>TL;DR:</strong> When Coqui AI shut down, they left XTTS v2 without official training code for the DVAE audio tokenizer and Latent HiFi-GAN vocoder, and fine-tuning new scripts (Arabic, Indic, etc.) broke on fixed vocabularies. I built <strong>Open-XTTS-Trainer</strong> — an open-source, modular 3-component training suite under MPL-2.0 with verified GPU loss convergence on real human speech and a 1-click Colab demo.</p> <h1>Why I Built This</h1> <p>XTTS v

<!-- SC_OFF --><div class="md"><p>A while back I posted SteerTTS v1, a small English TTS you steer with numbers (<a href="https://huggingface.co/WhissleAI/steer-tts">https://huggingface.co/WhissleAI/steer-tts</a>). Today I am releasing the edge variant:</p> <p><a href="https://huggingface.co/WhissleAI/steer-tts-edge"><strong>https://huggingface.co/WhissleAI/steer-tts-edge</strong></a></p> <p><strong>Size:</strong> everything an app has to ship (weights, voices, text front end and Python runtime)

Runtime optimizations and a thorough evaluation process driven by agentic AI help ensure that models written in PyTorch will execute efficiently and reliably on Trainium systems.

A scalable AI system requires a strong foundation. Data, engineering, platforms, operating models, and governance must converge to work as one. This session from MIT Technology Review’s EmTech Future conference explores what it takes to build that foundation in a highly regulated environment and how a responsible, trustworthy implementation can turn emerging capabilities, including agentic…

<!-- SC_OFF --><div class="md"><p>Have you looked at this iclr text detector?</p> <p>[<a href="https://opentextdetector.com/%5D(https://opentextdetector.com/">https://opentextdetector.com/](https://opentextdetector.com/)</a>)</p> <p>Do you think this will impact the review process? AI detectors are usually unreliable so I hope not.</p> </div><!-- SC_ON --> submitted by <a href="https://www.reddit.com/user/Whatever_635"> /u/Whatever_635 </a> <br/> <span><a href="https://www.reddit.com/r/MachineLe

Liquid AI released RIPL, a real-time inference engine for its LFM models. On one B200, LFM2.5-1.2B on RIPL showed 14.4 to 28.4x lower p95 latency than Qwen3.5-0.8B on vLLM. The post Liquid AI Launches RIPL, a Real-Time Inference Engine for Sub-50 ms LFMs appeared first on MarkTechPost .

Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research .

Launched today, NVIDIA Minerva is an AIOps platform built to help teams find problems earlier and keep AI factories productive. It brings together telemetry from infrastructure and AI workloads, then applies purpose-built AI models to turn that data into timely, actionable insight. AI factories are among the most complex operations in the world, with vast […]

<!-- SC_OFF --><div class="md"><p>Carbon-X is a 14.8B parameter decoder-only model that takes raw DNA in a normal chat and answers in English. It shares one token stream for text and DNA, using 6-mer tokens, and was mid-trained on 302B tokens.</p> <p>Hugging Face reports a 66.7% average across five DNA benchmarks with thinking disabled, versus 67.5% for GPT-6.1 Sol. The weights fit on one 80 GB GPU in bfloat16 and are Apache 2.0. It has not been clinically validated, and the model card says exac

Enterprise AI can generate recommendations, automate tasks, and help organizations make decisions faster. But without an understanding of how a business actually operates, even powerful models can miss the constraints and relationships that determine whether a decision is the right one. As companies look to move AI from experimentation into everyday operations, the challenge ahead…

Ask most executives to picture the cost of AI and they’ll likely see GPUs—racks of accelerators with gigawatts of power—and a waitlist for silicon. But that picture leaves something out: Data storage costs. What happens to data after a model responds? Much of the back-end data that’s generated—from billions of daily prompts, images, and videos—needs…

<!-- SC_OFF --><div class="md"><p>I'm training text2asmr, a text-to-ASMR model (whispering plus trigger sounds like tapping, crinkling, brushing). The generator needs every training clip tagged with the sounds in it, and there are about 126k recordings, so I tried frontier models as labellers. They were much worse than I expected, so here are the numbers.</p> <p><strong>Setup.</strong> Every judge got the same prompt and the same 20 labels on 480 clips:</p> <ul> <li><em>External:</em> sound-effe

IBM released Granite Speech 5.0 2B, an Apache 2.0 speech recognition and translation model. It ranks #2 on the Open ASR Leaderboard with 5.68% average English WER, alongside updated Turbo CTC and 4.1 2B Plus models. The post IBM AI Releases Granite Speech 5.0 2B for Multilingual Transcription and Translation appeared first on MarkTechPost .

At GTC Berlin, banks, payment companies and financial data providers are showing what production-scale intelligence looks like — built on NVIDIA accelerated computing, powered by open weight models and running on their own infrastructure. Revolut is rebuilding its technology stack on a single foundation model PRAGMA, powered by NVIDIA accelerated computing. Using the NVIDIA cuDF […]

To help developers rapidly build world models, which help physical AI systems understand and simulate the real world, NVIDIA has expanded the Cosmos platform with new training tools, simulation checkpoints and Cosmos Data Factory blueprints. Ranking top five on six different open models leaderboards, the NVIDIA Cosmos platform gives world model builders the frontier open […]

Companies around the world are building on the NVIDIA DSX AI Factory Platform to plan, simulate and run the grid and the AI factory as one system, from the substation to the rack. Their adoption of the platform responds to an acute problem. Power is now the biggest bottleneck in infrastructure. More than $300 billion […]

European retailers are using open models and physical intelligence across use cases — from a commerce platform cutting inference costs by 90% to to a fulfillment center where collaboration of different robotic fleets is simulated inside a digital twin. Frontier Results With Open Models Retail leaders and technology companies across Europe are using NVIDIA-accelerated open […]

Weather forecasts shape more than daily plans; timely weather information is crucial for energy producers, insurers and transport operators to plan around changing conditions. A global forecast can show a storm taking shape, but decisions about solar farms, shipping routes or insured property depend on what happens locally. NVIDIA Earth-2 gives developers and researchers a […]

Governments need modern solutions to serve the public while keeping sensitive information under their control. Open models provide a path forward, letting governments evaluate them, adapt them to local languages and public services, run them on infrastructure they control, apply their own safeguards and keep sensitive data within controlled environments. Some open models go further, […]