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DeepSeek AI Releases DeepSeek-V4: Compressed Sparse Attention and Heavily Compressed Attention Enable One-Million-Token Contexts

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DeepSeek-AI has released a preview version of the DeepSeek-V4 series: two Mixture-of-Experts (MoE) language models built around one core challenge making one-million-token context windows practical and affordable at inference time. The series consists of DeepSeek-V4-Pro, with 1.6T total parameters and 49B activated per token, and DeepSeek-V4-Flash, with 284B total parameters and 13B activated per token. […]

DeepSeek AI Releases DeepSeek-V4: Compressed Sparse Attention and Heavily Compressed Attention Enable One-Million-Token Contexts Read More »

Google Cloud AI Research Introduces ReasoningBank: A Memory Framework that Distills Reasoning Strategies from Agent Successes and Failures

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Most AI agents today have a fundamental amnesia problem. Deploy one to browse the web, resolve GitHub issues, or navigate a shopping platform, and it approaches every single task as if it has never seen anything like it before. No matter how many times it has stumbled on the same type of problem, it repeats

Google Cloud AI Research Introduces ReasoningBank: A Memory Framework that Distills Reasoning Strategies from Agent Successes and Failures Read More »

Google Introduces Simula: A Reasoning-First Framework for Generating Controllable, Scalable Synthetic Datasets Across Specialized AI Domains

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Training powerful AI models depends on one resource that is quietly running out: specialized data. While the internet provided a seemingly infinite supply of text and images to train today’s generalist models, the next wave of AI breakthroughs — in cybersecurity, legal reasoning, healthcare, and other niche domains — requires data that simply doesn’t exist

Google Introduces Simula: A Reasoning-First Framework for Generating Controllable, Scalable Synthetic Datasets Across Specialized AI Domains Read More »

Moonshot AI and Tsinghua Researchers Propose PrfaaS: A Cross-Datacenter KVCache Architecture that Rethinks How LLMs are Served at Scale

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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

Moonshot AI and Tsinghua Researchers Propose PrfaaS: A Cross-Datacenter KVCache Architecture that Rethinks How LLMs are Served at Scale Read More »

Google AI Releases Auto-Diagnose: An Large Language Model LLM-Based System to Diagnose Integration Test Failures at Scale

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

Google AI Releases Auto-Diagnose: An Large Language Model LLM-Based System to Diagnose Integration Test Failures at Scale Read More »

UCSD and Together AI Research Introduces Parcae: A Stable Architecture for Looped Language Models That Achieves the Quality of a Transformer Twice the Size

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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

UCSD and Together AI Research Introduces Parcae: A Stable Architecture for Looped Language Models That Achieves the Quality of a Transformer Twice the Size Read More »

NVIDIA and the University of Maryland Researchers Released Audio Flamingo Next (AF-Next): A Super Powerful and Open Large Audio-Language Model

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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

NVIDIA and the University of Maryland Researchers Released Audio Flamingo Next (AF-Next): A Super Powerful and Open Large Audio-Language Model Read More »

Meta AI and KAUST Researchers Propose Neural Computers That Fold Computation, Memory, and I/O Into One Learned Model

Researchers from Meta AI and the King Abdullah University of Science and Technology (KAUST) have introduced Neural Computers (NCs) — a proposed machine form in which a neural network itself acts as the running computer, rather than as a layer sitting on top of one. The research team presents both a theoretical framework and two

Meta AI and KAUST Researchers Propose Neural Computers That Fold Computation, Memory, and I/O Into One Learned Model Read More »

Researchers from MIT, NVIDIA, and Zhejiang University Propose TriAttention: A KV Cache Compression Method That Matches Full Attention at 2.5× Higher Throughput

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Long-chain reasoning is one of the most compute-intensive tasks in modern large language models. When a model like DeepSeek-R1 or Qwen3 works through a complex math problem, it can generate tens of thousands of tokens before arriving at an answer. Every one of those tokens must be stored in what is called the KV cache

Researchers from MIT, NVIDIA, and Zhejiang University Propose TriAttention: A KV Cache Compression Method That Matches Full Attention at 2.5× Higher Throughput Read More »

Alibaba’s Tongyi Lab Releases VimRAG: a Multimodal RAG Framework that Uses a Memory Graph to Navigate Massive Visual Contexts

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Retrieval-Augmented Generation (RAG) has become a standard technique for grounding large language models in external knowledge — but the moment you move beyond plain text and start mixing in images and videos, the whole approach starts to buckle. Visual data is token-heavy, semantically sparse relative to a specific query, and grows unwieldy fast during multi-step

Alibaba’s Tongyi Lab Releases VimRAG: a Multimodal RAG Framework that Uses a Memory Graph to Navigate Massive Visual Contexts Read More »