🐡 The era of agonizing over which single LLM is "the best" might be coming to an end. Title: Sakana Fugu Technical Report URL: Sakana AI's Fugu is a commander (orchestrator) model that marshals a team of frontier LLMs and automatically assembles the best lineup for each task. Here are the three highlights worth knowing. 🧭 A learned conductor Fugu adds a lightweight selection head on top of a pretrained backbone and picks the best worker model directly from logits over hidden states. Because it skips autoregressive decoding, it keeps latency on par with calling a single frontier model — an elegant trick. 🕸️ Dynamically writing its own workflows (Fugu-Ultra) The high-end variant writes out full agentic workflows in natural language, defining task decomposition, subtask allocation, and targeted communication strategies. It composes multi-agent topologies — debate, sequential chaining, tree structures — of up to 5 steps, tailored per problem. 📊 Beating any single model 73.7 on SWE-Bench Pro (above Claude Opus 4.8's 69.2), 95.5 on GPQA-Diamond, 93.2 on LiveCodeBench — state-of-the-art among publicly accessible models. In agentic coding it lands a 5–6% relative gain, described as a full "generational" improvement. Reaching the frontier through smart orchestration rather than sheer scale — a fresh new scaling axis. #SakanaAI# #LLM#