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Sakana AI Releases Fugu-Cyber: An Orchestration Model Reporting 86.9% on CyberGym and 72.1% on CTI-REALM

Sakana AI has released Fugu-Cyber (model ID is fugu-cyber-v1.0), a cybersecurity-specialized addition to its Fugu orchestration family. It is not just…

Sakana AI’s Error Diffusion Trains Dale-Compliant Dual-Stream Networks, Reaching 96.7% MNIST and 61.7% CIFAR-10 Without Backpropagation

Backpropagation dominates deep learning, yet it uses a mechanism the brain likely cannot. Specifically, the backward pass needs exact transposes of forward weight matrices. This is…

Sakana AI Launches Sakana Translate, a Namazu-Powered Japanese–English–Chinese Translation Tool With Translate, Proofread, and Ask Modes

Sakana AI has added a new feature called Sakana Translate to its chat service, Sakana Chat. It handles bidirectional translation across…

Sakana AI Launches Sakana Fugu: An Orchestration Model That Routes Tasks Across a Swappable Pool of Frontier LLMs

Today, Sakana AI launched Sakana Fugu. It is a multi-agent orchestration system that behaves like one model. You send a request to a single endpoint. Fugu…

Mitigating vendor lock-in with Sakana AI Fugu multi-agent models

Sakana AI launched Fugu to orchestrate multi-agent operations and mitigate single-vendor dependency risks in enterprise deployments.Enterprises face operational vulnerabilities when relying entirely on monolithic AI APIs.…

Sakana AI Commercializes AB-MCTS in Sakana Marlin, an Enterprise Agent Generating Up to 100-Page Research Reports With Slides

Tokyo-based Sakana AI shipped its first commercial product ‘Sakana Marlin’ this week. Sakana team positions it as a Virtual CSO (Chief Strategy Officer). It is a…

Sakana AI Proposes DiffusionBlocks: a Block-wise Training Framework That Converts Residual Networks into Independently Trainable Denoising Modules

Researchers from Sakana AI and the University of Tokyo propose DiffusionBlocks. It trains transformer-based networks one block at a time. Training memory is reduced by a…

Sakana AI and NVIDIA Introduce TwELL with CUDA Kernels for 20.5% Inference and 21.9% Training Speedup in LLMs

Scaling large language models (LLMs) is expensive. Every token processed during inference and every gradient computed during training flows through feedforward layers that account for over…

Sakana AI Introduces KAME: A Tandem Speech-to-Speech Architecture That Injects LLM Knowledge in Real Time

The fundamental tension in conversational AI has always been a binary choice: respond fast or respond smart. Real-time speech-to-speech (S2S) models — the kind that power…

Sakana AI Introduces Doc-to-LoRA and Text-to-LoRA: Hypernetworks that Instantly Internalize Long Contexts and Adapt LLMs via Zero-Shot Natural Language

Customizing Large Language Models (LLMs) currently presents a significant engineering trade-off between the flexibility of In-Context Learning (ICL) and the efficiency of Context Distillation (CD) or…

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