The human brain is incredibly efficient because it only activates the specific neurons needed for a thought. Modern LLMs naturally try to do this too (> 95% of neurons in feedforward layers stay silent for any given word), but our hardware punishes them for it. One of the most
@hardmaru
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Sakana Fugu: Multi-Agent Orchestration System as Foundation Model
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Sakana Fugu: A Multi-Agent Orchestration System as a Foundation Model
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KAME Tandem Architecture Boosts Knowledge in Speech AI Systems
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Two Heads Are Better Than One: Async Knowledge Injection for Speech AI with Tandem Architecture Blog: https://
pub.sakana.ai/kame/ KAME: Tandem Architecture for Enhancing Knowledge in Real-Time Speech-to-Speech Conversational AI Paper: https://
arxiv.org/abs/2510.02327 #ICASSP2026 -
Multi-Agent System Cuts SMBC Corporate Strategy Workflow to Hours
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Today we announced a multi-agent system built with SMBC, one of Japan’s largest banks. It handles complex corporate strategy proposals, reducing a one to two week workflow down to just a few hours. https://
nikkei.com/article/DGXZQO
UB2713R0X20C26A4000000/
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Tandem Voice AI Architecture Enables Speaking While Thinking
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For years, voice AI has been stuck in a rigid loop: think, then speak. But real human conversation is messy, overlapping, and asynchronous.
— hardmaru (@hardmaru) 29 avril 2026
In our new #ICASSP2026 work, we built a tandem architecture that shifts the paradigm to “speak while thinking.” A fast speech model starts… https://t.co/gyRFlqDSUjFor years, voice AI has been stuck in a rigid loop: think, then speak. But real human conversation is messy, overlapping, and asynchronous. In our new #ICASSP2026 work, we built a tandem architecture that shifts the paradigm to “speak while thinking.” A fast speech model starts
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Conductor Framework Orchestrates AI Agents Using Natural Language
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Learning to Orchestrate Agents in Natural Language with the Conductor Fugu Blog: https://
sakana.ai/fugu-beta
Paper: https://
arxiv.org/abs/2512.04388 -
AI Conductor Model Uses RL to Automate Prompt Engineering
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For the past few years, humans have been doing “prompt engineering” to coax the best performance out of different LLMs. In this work, we explored what happens if we train an AI to do that job instead. By training a Conductor model with RL, we found that it naturally learns to
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TRINITY: An Evolved LLM Coordinator System
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TRINITY: An Evolved LLM Coordinator https://
arxiv.org/abs/2512.04695 -
Evolved Multi-Agent LLM System Unlocks Test-Time Compute at ICLR2026
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Scaling massive monolithic LLMs continues to yield incredible results. But to truly unlock their ceiling, the next frontier is test-time compute and dynamic orchestration. Nature solves complex problems through collaborative ecosystems. In our new #ICLR2026 paper, we evolved a
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Sakana AI Fugu Launches Beta API with Recursive Test-Time Scaling
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One of my favorite things about Sakana Fugu is the recursive test-time scaling. When allowed to call itself recursively, it reads its own prior output and spins up corrective workflows on the fly. We are opening up the API for beta testers to try it out: https://
sakana.ai/fugu-beta/