Sakana AI released Fugu Ultra v2, a model that orchestrates tasks across multiple specialized models rather than relying on a single monolithic architecture. It's designed for complex reasoning, autonomous research, and software development with a 1M token context window and costs $5/$30 per million input/output tokens.

• Fugu Ultra v2 uses learned multi-agent orchestration to route work across open and specialized models, avoiding dependence on proprietary frontier models
• The model supports configurable reasoning effort levels, function calling, structured outputs, and integrated web search
• Real-world performance shows 31 tokens/second throughput, 10.6 second latency, and 87.47% availability across providers over the past 3 days

This paper explores how large language models make decisions during reasoning. It demonstrates that these models often encode their choices before generating text, influencing their subsequent thought processes. The research shows that altering initial decisions can change reasoning outcomes significantly.

• A linear probe can decode whether a model will call a tool from its activations before it generates any reasoning text, sometimes before any tokens at all
• Artificially flipping this early "decision direction" causes the model to switch its tool-use behavior in 7% to 79% of cases depending on model/benchmark
• When steered toward a different decision, the model's subsequent reasoning rationalizes the new choice rather than resisting or correcting it, suggesting the "thinking" is post-hoc justification rather than genuine deliberation