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

For years, AI progress has centered on scaling individual foundation models: larger parameters, longer context windows, stronger reasoning, and better tool use. Sakana AI’s Fugu points elsewhere, behaving like one model from the outside while coordinating multiple expert agents internally. A single API call can trigger direct answering, specialist delegation, intermediate verification, and final synthesis, […] The post Sakana Fugu: Multi-Agent System as a Model  appeared first on Analytics Vidhya.

Fugu and Fugu Ultra route tasks across a swappable model pool, leading most coding, reasoning, and agentic benchmarks. The post Sakana AI Launches Sakana Fugu: An Orchestration Model That Routes Tasks Across a Swappable Pool of Frontier LLMs appeared first on MarkTechPost.

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. Japanese AI firm Sakana AI designed Fugu as a response to these concentration risks by creating an orchestration language model that calls upon a pool of varied models […] The post Mitigating vendor lock-in with Sakana AI Fugu multi-agent models appeared first on AI News.

Sakana AI's first commercial product runs autonomously for up to eight hours per task. It returns multi-page reports and slides, built on AB-MCTS and AI Scientist workflows. The post Sakana AI Commercializes AB-MCTS in Sakana Marlin, an Enterprise Agent Generating Up to 100-Page Research Reports With Slides appeared first on MarkTechPost.

Sakana Marlin's extended autonomous research capabilities could revolutionize strategic planning but risk significant errors without oversight. The post Sakana AI launches Sakana Marlin, an autonomous deep research agent that thinks for 8 hours straight appeared first on Crypto Briefing.

Sakana AI and NVIDIA Researchers demonstrate that simple L1 regularization can induce over 99% sparsity in feedforward layers with negligible downstream performance impact, and translate that sparsity into real GPU throughput gains using new sparse data formats and fused CUDA kernels. The post Sakana AI and NVIDIA Introduce TwELL with CUDA Kernels for 20.5% Inference and 21.9% Training Speedup in LLMs appeared first on MarkTechPost.

Sakana AI Introduces KAME: A Tandem Architecture That Injects Real-Time LLM Knowledge Into Speech-to-Speech Conversational AI Without Adding Latency The post Sakana AI Introduces KAME: A Tandem Speech-to-Speech Architecture That Injects LLM Knowledge in Real Time appeared first on MarkTechPost.