Machine Learning

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Generating scenarios for extreme events, without extreme data

Can a city’s seawall stand up to a blockbuster storm? Will a region’s power grid hold against record-breaking heat? And can a town’s fire-fighting resources contain a major wildfire? To answer these questions, communities will first need to know how such extreme events could unfold. How far is a wildfire likely to spread? How much of

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Scientific Data Analysis with LabPlot in Python: Signal Processing, Spectral Peak Fitting, Visualization, and Batch Automation

In this tutorial, we explore a LabPlot-inspired scientific data analysis workflow in Python while preserving the structure and terminology of LabPlot’s aspect tree, analysis kernels, plotting system, and project model. We build reusable components to import tabular data, compute descriptive statistics, smooth and differentiate signals, perform Fourier analysis and filtering, detect peaks, integrate curves, reduce

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Harvey Introduces Harvey Tenet: A Kimi K3 Base Post-Trained with Fireworks for Long-Horizon Legal Agent Work

Harvey has released Harvey Tenet, its first post-trained model, as a research preview as of today. Tenet is a Kimi K3 base post-trained with Fireworks through asynchronous reinforcement learning on long-horizon legal work. The training corpus combined synthetic data, publicly available legal data, and human expert data. Harvey states no customer data was used. Against

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Meet FreeToken: An Edge-Native MoE Serving Engine that Runs 753B GLM-5.2 on a Single Workstation GPU

Frontier open-weight models are shipping faster than the hardware assumptions around them. Kimi-K3, GLM-5.2 and DeepSeek-V4-Flash are closing the capability gap with proprietary systems, but releasing parameters only determines who can obtain a model — not who can afford to run it. Serving them still assumes datacenter-class GPU clusters, and as agentic workloads push inference

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PagedAttention vs. RadixAttention: Optimizing LLM KV Cache Management

Modern LLMs rely on quantization, pruning, distillation, and faster attention kernels, but production performance often depends most on KV cache management. As context windows grow, the cache consumes significant GPU memory, limiting concurrency, throughput, and latency. Two breakthroughs transformed this challenge: PagedAttention improves memory allocation, while RadixAttention enables efficient prefix reuse. Together, these techniques make

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Liquid AI Releases LFM2.5-DSpark Draft Models That Deliver Up to 3.18x Faster Decoding Without Changing Model Outputs

Liquid AI has released DSpark draft model checkpoints for three models in its LFM2.5 family: LFM2.5-1.2B-Instruct, LFM2.5-2.6B, and LFM2.5-8B-A1B. Each drafter adds a speculative decoding path to an existing target model. A roughly 300M-parameter draft proposes a block of nine candidate tokens, and the target model verifies the whole block in a single forward pass.

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Paving the way for greener ammonia production

Ammonia is one of the most important chemicals produced in the world, ranking second only to sulfuric acid in the total volume produced each year. It is used mostly to make fertilizer, which is essential to feeding the world’s population. Yet its production accounts for up to 2 percent of the world’s energy consumption and

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When AI art has no author: Study finds generated images often can’t be traced to training data

When an artificial intelligence image generator produces a portrait, whose work went into it? The question sits at the center of lawsuits, licensing deals, and proposed regulations worldwide. Artists want credit. Companies want clarity. Policymakers want a way to assign responsibility.New work from a team of researchers at MIT’s Computer Science and Artificial Intelligence Laboratory

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Meet SAM (Sovereign Agent Mesh): A Zero-Config, Zero-Trust P2P Network for AI Agents

google/sam is not Segment Anything. SAM here means Sovereign Agent Mesh, an Apache-2.0 networking project for autonomous AI agents. The problem it targets is concrete. Agents now run across cloud servers, on-prem datacenters, laptops, Raspberry Pis and Android devices. Letting them share tools usually means exposing internal scripts, LLM endpoints or private APIs to the

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Samsung health AI models analyse wearable biosignal data

Samsung Research America’s Digital Health Team has presented two AI foundation models designed to learn from wearable biosignals. The work centres on data captured by smartwatches, including heart activity, sleep, and physical activity. The company discussed its Connected Care vision at the Health Forum during Galaxy Unpacked in July 2026. Samsung described a future of

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