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...
AI’s hidden storage bill: What inference leaves behind
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 ...
IBM AI Releases Granite Speech 5.0 2B for Multilingual Transcription and Translation
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 Tr...
Leading European Financial Services Organizations Embrace Open Models to Build Proprietary Intelligence
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 founda...
NVIDIA Cosmos Opens New Tools to Train, Evaluate and Deploy World Models
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 leaderboard...
How Companies Are Using NVIDIA DSX to Break the Power Bottleneck
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...
NVIDIA Transforms European Retail, From Digital Discovery to Physical Operations
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 leader...
Pioneers Across Europe Deploy NVIDIA Earth-2 Open Models to Power Real-World Weather and Climate Prediction
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...
Perplexity and NVIDIA Researchers Introduce JOLT: a Joint Teacher-Student Reinforcement Learning Method
Perplexity and NVIDIA introduced JOLT, a post-training method that trains one model as both teacher and student. On MATH it matched GRPO’s accuracy with 6.5x fewer processed training tokens.
The post Perplexity and NVIDIA Researchers Introduce JOLT: a Joint Teacher-Student Reinforcement Learning Met...
Forging More Tasks into Better Harnesses: Agentic Harness Evolution via Successive Scale Racing
arXiv:2610.28815v1 Announce Type: new
Abstract: Agentic harness evolution optimizes an agent's instructions, tools, and control flow by using its task execution experiences as evolving evidence. While more evolution tasks should intuitively improve performance, our preliminary study reveals a parad...
To Keep or Not to Keep: Learning KV Cache Retention in Disaggregated LLM Serving Systems
arXiv:2610.28637v1 Announce Type: new
Abstract: Disaggregated \textsc{LLM} serving separates prefill and decode across node pools, placing a network fabric between \textsc{KV}-cache computation and consumption. This breaks a key assumption of classical cache policies: a miss no longer incurs only l...
Reinforcement learning for hardware acceleration and system-level optimization on FPGA and integrated-circuit platforms: A survey
arXiv:2610.28699v1 Announce Type: new
Abstract: Reinforcement learning (RL) enables adaptive and autonomous decision-making in embedded, edge, and heterogeneous computing systems. Field-programmable gate arrays (FPGAs) and integrated circuits (ICs) offer low latency, energy efficiency, and architec...
Scalable Stress-Constrained Topology Optimization: A Super-resolution and Simultaneous Approach Based on Machine Learning
arXiv:2610.28703v1 Announce Type: new
Abstract: Large-scale topology optimization (TO) remains computationally demanding, especially for stress-constrained problems. To address this challenge, we propose the simultaneous neural topology optimization (SNTO) framework where we represent state, adjoin...
SimHack: Predicting the onset of detectable reward hacking before RL training
arXiv:2610.28707v1 Announce Type: new
Abstract: When training LLMs to code with reinforcement learning, reward hacking is often detectable but difficult to prevent, since (a) environment hardening is increasingly difficult as training environments grow more complex; and (b) training-time interventi...
A Small Model Asks Better: Fine-Tuning Query Clarification for Retrieval-Augmented Generation
arXiv:2610.28686v1 Announce Type: new
Abstract: Query clarification is essential for retrieval-augmented generation (RAG) systems that face ambiguous user queries. We consider multi-turn grounded conversational scenarios where a user query is either ill-formed or ambiguous in the context of the pri...
Scaling in Games: Continued Pre-Training For Embodied Agents In Diverse Virtual Worlds
arXiv:2610.28698v1 Announce Type: new
Abstract: Scaling data, diversity and model capacity has improved embodied learning in physical environments, where diverse experiences remain grounded in shared physical laws. Whether these gains extend to virtual worlds with different visual styles, dynamics,...
Automating ML for Science: Can Frontier Agents Climb Scientific Hills in the Wild?
arXiv:2610.28763v1 Announce Type: new
Abstract: Frontier coding agents are increasingly framed as end-to-end automated researchers, and ML for science offers a demanding test of that promise: improving a scientific model often requires domain insight beyond routine ML engineering. We introduce Agen...
SpecEvo: Speculative Evolution with Large Language Models for Cost-Efficient Scientific Discovery
arXiv:2610.28706v1 Announce Type: new
Abstract: LLM-driven evolutionary search is a promising paradigm for scientific discovery, but existing methods remain costly and slow, often applying expensive reasoning uniformly while underusing evidence from failed, redundant, and saturated trials. We intro...
People are taking an unapproved weight-loss drug. AI found something surprising
Researchers analyzing nearly 150,000 Reddit posts uncovered unexpected symptom reports linked by users to unapproved versions of retatrutide, including increased appetite, sleep disturbances, and elevated heart rates. The findings differ from clinical trial results and highlight how AI could help id...
How Weaviate responded to the eMudhra certificate compromise
Weaviate patched its entire customer estate and infrastructure platform in under 17 hours to mitigate the risk of eight leaked certificates from the eMudhra certificate authority.
LLMs are making formal software verification feasible at a much larger scale
Formal verification can prove that code is free of certain bugs, but historically it has only been feasible for small codebases. With AI models, however, this is beginning to change. Here, we used Claude to prove security properties in over one hundred real-world open-source libraries over the span ...
Recursive Releases Flint, an Open-Weight 40B MoE Local Coding Model for Agents with 2.5B Active Params
Recursive has released Flint, an Apache 2.0 local coding model with 40B total and 2.5B active parameters. It reports 74.2% on SWE-bench Verified and 125+ tokens per second on an M4 Pro MacBook Pro.
The post Recursive Releases Flint, an Open-Weight 40B MoE Local Coding Model for Agents with 2.5B Acti...