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For years, the way large language models handle inference has been stuck inside a box — literally. The high-bandwidth RDMA networks that make modern LLM serving work have confined both prefill and decode to the same datacenter, sometimes even the same rack. A team of researchers at Moonshot AI and Tsinghua University is making the […]
Anthropic has never published a technical paper on Claude Mythos. That has not stopped the research community from theorizing. A new open-source project called OpenMythos, released on GitHub by Kye Gomez, attempts something ambitious: a first-principles theoretical reconstruction of what the Claude Mythos architecture might actually be, built entirely in PyTorch and grounded in peer-reviewed
Quantum computing has spent years living in the future tense. Hardware has improved, research has compounded, and venture dollars have followed — but the gap between a quantum processor running in a lab and one running a real-world application remains stubbornly wide. NVIDIA moved to close that gap with the launch of NVIDIA Ising, the
Anthropic has launched Claude Opus 4.7, it’s latest frontier model and a direct successor to Claude Opus 4.6. The release is positioned as a focused improvement rather than a full generational leap, but the gains it delivers are substantial in the areas that matter most to developers building real-world AI-powered applications: agentic software engineering, multimodal
If you have ever stared at thousands of lines of integration test logs wondering which of the sixteen log files actually contains your bug, you are not alone — and Google now has data to prove it. A team of Google researchers introduced Auto-Diagnose, an LLM-powered tool that automatically reads the failure logs from a
The open-source AI landscape has a new entry worth paying attention to. The Qwen team at Alibaba has released Qwen3.6-35B-A3B, the first open-weight model from the Qwen3.6 generation, and it is making a compelling argument that parameter efficiency matters far more than raw model size. With 35 billion total parameters but only 3 billion activated
Drug discovery is one of the most expensive and time-consuming endeavors in human history. It takes roughly 10 to 15 years to go from target discovery to regulatory approval for a new drug in the United States. Most of that time is spent not in breakthrough moments, but in painstaking analytical work — sifting through
The dominant recipe for building better language models has not changed much since the Chinchilla era: spend more FLOPs, add more parameters, train on more tokens. But as inference deployments consume an ever-growing share of compute and model deployments push toward the edge, researchers are increasingly asking a harder question — can you scale quality
Table of contentsPre-TrainingSupervised FinetuningLoRAQLoRARLHFReasoning (GRPO)Deployment Training a modern large language model (LLM) is not a single step but a carefully orchestrated pipeline that transforms raw data into a reliable, aligned, and deployable intelligent system. At its core lies pretraining, the foundational phase where models learn general language patterns, reasoning structures, and world knowledge from massive
Understanding audio has always been the multimodal frontier that lags behind vision. While image-language models have rapidly scaled toward real-world deployment, building open models that robustly reason over speech, environmental sounds, and music — especially at length — has remained quite hard. NVIDIA and the University of Maryland researchers are now taking a direct swing
